THE ELEMENTS OF BRIEFING Procedural History Legal Issue Facts of Case Statement of Rule Policy Dicta Reasoning Holding Concurrence Dissents

Reasoning The reasoning gives the reader insight into how the court arrived at its decision. It is instructive in nature. Courts often back their holdings with several lines of reasoning, each of which should be summarized in this section. Unnecessary repetition of facts or the issue should be avoided. A court�s rationale for its holding might be a simple explanation of its thought process. Alternatively, the reasoning might be based on the plain language of the statute, Congressional intent, the re-enactment doctrine, or other common means of resolving judicial disputes.

Holding As the issue�s complement, the holding consists of two parts: (1) a “yes” or “no” conclusion to the brief�s issue and (2) the rule of law the court establishes. The rule of law is a guidepost that courts use to decide future cases based on the legal concept of stare decisis (judicial tendency to follow prior decisions).

Beginning the issue with “are” or “is” often leads to a clearer and more concise expression of the issue than beginning it with “may,” “can,” “does,” or “should.” The latter beginnings may lead to vague or ambiguous versions of the issue. Examine the following alternative statements of the judicial issue from Aiken Industries, Inc. (TC, 1971), acq.: Issue 2 (Poor): Are the interest payments exempt from the withholding tax? Issue 2 (Poor): Should the taxpayer exempt the interest payments from withholding tax? In the first version of issue 2 above, to which interest payments and which withholding tax is the writer referring? The issue does not stand alone since it cannot be precisely understood apart from separately reading the brief�s facts. The extreme brevity leads to ambiguity. In the second version, the question can be interpreted as a moral or judgment issue rather than a legal one. Whether the taxpayer should do (or should not do) something may be a very different issue than the legal question of what the law requires. A legal brief, however, should focus on the latter. Rewriting issue 2 as follows leads to a clearer expression of the precise issue: Issue 2 (Better): Are interest payments exempt from the U.S. 30% withholding tax when paid to an entity established in a tax treaty country for no apparent purpose other than to escape taxation on the interest received?

Issues should be stated so that they “stand alone.” That is, issues should be completely understandable without reference to the facts or other sections of the brief or judicial decision. Use of the definite article “the” indicates that the issue does not stand alone when it alludes to prior information.

dural issue : What is the appealing party claiming the lower court did wrong (e.g., ruling on evidence, jury instructions, granting of summary judgment, etc.)?

antive issue : A substantive statement of the issue consists of two parts -- i. the point of law in dispute ii. the key facts of the case re lating to that point of law in dispute (legally relevant facts) You must include the key facts from the case so that the issue is specific to that case. Typically, the disputed issue involves how the court applied some element of the pertinent rule to the facts of the specific case. Resolving the issue will determine the court’s disposition of the case.

b. Identify legally relevant facts, t hat is, those facts that tend to prove or disprove an issue before the court. The relevant facts tell what happened before the parties enter ed the judicial system. c. Identify procedurally significant facts. You should set out (1) the cause of action (C/A) (the law the plaintiff claimed was broken), (2) relief the plaintiff requested, (3) defenses, if any, the defendant raised.

Identify the relationship/status of the parties (Note: Do not merely refer to the parties as the plai ntiff/defendant or appellant/appellee; be sure to also include more descr iptive generic terms to identify the relationship/status at issue, e.g., buyer/seller, employer/employee, landlord/tenant, etc.)

Functions of case briefing A. Case briefing helps you acquire the skills of case analysis and legal reasoning. Briefing a case helps you understand it. B. Case briefing aids your memory. Briefs help you remember the cases you read (1) for class discussion, (2) fo r end-of-semester review for final examinations, and (3) for writing and analyzing legal problems.

nctions A. A case brief is a dissection of a judici al opinion -- it contains a written summary of the basic components of that decision. B. Persuasive briefs (trial and appella te) are the formal documents a lawyer files with a court in support of his or her client’s position

A CAUTIONARY NOTE Don’t brief the case until you have read it through at least once. Don’t think that because you have found the judge’s best purple prose you have necessarily extracted the essence of the decision. Look for unarticulated premises, logical fallacies, manipulation of the factual record, or distortions of precedent. Then ask, How does this case relate to other cases in the same general area of law? What does it show about judicial policymaking? Does the result violate your sense of justice or fairness? How might it have been better decided?

T h i s p r o t e c t i o n i s s u b j e c t t o a n i m p o r t a n t l i m i t a t i o n . T h e m e r e f a c t t h a t a w o r k i s c o p y r i g h t e d d o e s n o t m e a n t h a t e v e r y e l e m e n t o f t h e w o r k m a y b e p r o t e c t e d . O r i g i n a l i t y r e m a i n s t h e s i n e q u a n o n o f c o p y r i g h t ; a c c o r d i n g l y , c o p y r i g h t p r o t e c t i o n m a y e x t e n d o n l y t o t h o s e c o m p o n e n t s o f a w o r k t h a t a r e o r i g i n a l t o t h e a u t h o r . P a t t e r s o n & J o y c e 8 0 0 - 8 0 2 ; G i n s b u r g , C r e a t i o n a n d C o m m e r c i a l V a l u e : C o p y r i g h t P r o t e c t i o n o f W o r k s o f I n f o r m a t i o n , 9 0 C o l u m . L . R e v . 1 8 6 5 , 1 8 6 8 , a n d n . 1 2 ( 1 9 9 0 ) ( h e r e i n a f t e r G i n s b u r g ) .

F a c t u a l c o m p i l a t i o n s , o n t h e o t h e r h a n d , m a y p o s s e s s t h e r e q u i s i t e o r i g i n a l i t y .

C e n s u s t a k e r s , f o r e x a m p l e , d o n o t " c r e a t e " t h e p o p u l a t i o n f i g u r e s t h a t e m e r g e f r o m t h e i r e f f o r t s ; i n a s e n s e , t h e y c o p y t h e s e f i g u r e s f r o m t h e w o r l d a r o u n d t h e m . D e n i c o l a , C o p y r i g h t i n C o l l e c t i o n s o f F a c t s : A T h e o r y f o r t h e P r o t e c t i o n o f N o n f i c t i o n L i t e r a r y W o r k s , 8 1 C o l u m . L . R e v . 5 1 6 , 5 2 5 ( 1 9 8 1 ) ( h e r e i n a f t e r D e n i c o l a ) . C e n s u s d a t a t h e r e f o r e d o n o t t r i g g e r c o p y r i g h t b e c a u s e t h e s e d a t a a r e n o t " o r i g i n a l " i n t h e c o n s t i t u t i o n a l s e n s e . N i m m e r § 2 . 0 3 [ E ] . T h e s a m e i s t r u e o f a l l f a c t s — s c i e n t i f i c , h i s t o r i c a l , b i o g r a p h i c a l , a n d n e w s o f t h e d a y . " [ T ] h e y m a y n o t b e c o p y r i g h t e d a n d a r e p a r t o f t h e p u b l i c d o m a i n a v a i l a b l e t o e v e r y p e r s o n . " M i l l e r , s u p r a , a t 1 3 6 9 .

I t i s t h i s b e d r o c k p r i n c i p l e o f c o p y r i g h t t h a t m a n d a t e s t h e l a w ' s s e e m i n g l y d i s p a r a t e t r e a t m e n t o f f a c t s a n d f a c t u a l c o m p i l a t i o n s . " N o o n e m a y c l a i m o r i g i n a l i t y a s t o f a c t s . " I d . , § 2 . 1 1 [ A ] , p . 2 - 1 5 7 . T h i s i s b e c a u s e f a c t s d o n o t o w e t h e i r o r i g i n t o a n a c t o f a u t h o r s h i p . T h e d i s t i n c t i o n i s o n e b e t w e e n c r e a t i o n a n d d i s c o v e r y : T h e f i r s t p e r s o n t o f i n d a n d r e p o r t a p a r t i c u l a r f a c t h a s n o t c r e a t e d t h e f a c t ; h e o r s h e h a s m e r e l y d i s c o v e r e d i t s e x i s t e n c e . T o b o r r o w f r o m B u r r o w - G i l e s , o n e w h o d i s c o v e r s a f a c t i s n o t i t s " m a k e r " o r " o r i g i n a t o r . " 1 1 1 U . S . , a t 5 8 . " T h e d i s c o v e r e r m e r e l y f i n d s a n d r e c o r d s . " N i m m e r § 2 . 0 3 [ E ] .

T h e o r i g i n a l i t y r e q u i r e m e n t a r t i c u l a t e d i n T h e T r a d e - M a r k C a s e s a n d B u r r o w - G i l e s r e m a i n s t h e t o u c h s t o n e o f c o p y r i g h t p r o t e c t i o n t o d a y . S e e G o l d s t e i n v . C a l i f o r n i a , 4 1 2 U . S . 5 4 6 , 5 6 1 - 5 6 2 ( 1 9 7 3 ) . I t i s t h e v e r y " p r e m i s e o f c o p y r i g h t l a w . " M i l l e r v . U n i v e r s a l C i t y S t u d i o s , I n c . , 6 5 0 F . 2 d 1 3 6 5 , 1 3 6 8 ( C A 5 1 9 8 1 ) . L e a d i n g s c h o l a r s a g r e e o n t h i s p o i n t . A s o n e p a i r o f c o m m e n t a t o r s s u c c i n c t l y p u t s i t : " T h e o r i g i n a l i t y r e q u i r e m e n t i s c o n s t i t u t i o n a l l y m a n d a t e d f o r a l l w o r k s . "

I n B u r r o w - G i l e s , t h e C o u r t d i s t i l l e d t h e s a m e r e q u i r e m e n t f r o m t h e C o n s t i t u t i o n ' s u s e o f t h e w o r d " a u t h o r s . " T h e C o u r t d e f i n e d " a u t h o r , " i n a c o n s t i t u t i o n a l s e n s e , t o m e a n " h e t o w h o m a n y t h i n g o w e s i t s o r i g i n ; o r i g i n a t o r ; m a k e r . " 1 1 1 U . S . , a t 5 8 ( i n t e r n a l q u o t a t i o n m a r k s o m i t t e d ) . A s i n T h e T r a d e - M a r k C a s e s , t h e C o u r t e m p h a s i z e d t h e c r e a t i v e c o m p o n e n t o f o r i g i n a l i t y . I t d e s c r i b e d c o p y r i g h t a s b e i n g l i m i t e d t o " o r i g i n a l i n t e l l e c t u a l c o n c e p t i o n s o f t h e a u t h o r , " 1 1 1 U . S . , a t 5 8 , a n d s t r e s s e d t h e i m p o r t a n c e o f r e q u i r i n g a n a u t h o r w h o a c c u s e s a n o t h e r o f i n f r i n g e m e n t t o p r o v e " t h e e x i s t e n c e o f t h o s e f a c t s o f o r i g i n a l i t y , o f i n t e l l e c t u a l p r o d u c t i o n , o f t h o u g h t , a n d c o n c e p t i o n . " I d . , a t 5 9 - 6 0 .

I n T h e T r a d e - M a r k C a s e s , t h e C o u r t a d d r e s s e d t h e c o n s t i t u t i o n a l s c o p e o f " w r i t i n g s . " F o r a p a r t i c u l a r w o r k t o b e c l a s s i f i e d " u n d e r t h e h e a d o f w r i t i n g s o f a u t h o r s , " t h e C o u r t d e t e r m i n e d , " o r i g i n a l i t y i s r e q u i r e d . " 1 0 0 U . S . , a t 9 4 . T h e C o u r t e x p l a i n e d t h a t o r i g i n a l i t y r e q u i r e s i n d e p e n d e n t c r e a t i o n p l u s a m o d i c u m o f c r e a t i v i t y : " [ W ] h i l e t h e w o r d w r i t i n g s m a y b e l i b e r a l l y c o n s t r u e d , a s i t h a s b e e n , t o i n c l u d e o r i g i n a l d e s i g n s f o r e n g r a v i n g , p r i n t s , & c . , i t i s o n l y s u c h a s a r e o r i g i n a l , a n d a r e f o u n d e d i n t h e c r e a t i v e p o w e r s o f t h e m i n d . T h e w r i t i n g s w h i c h a r e t o b e p r o t e c t e d a r e t h e f r u i t s o f i n t e l l e c t u a l l a b o r , e m b o d i e d i n t h e f o r m o f b o o k s , p r i n t s , e n g r a v i n g s , a n d t h e l i k e . " I b i d . ( e m p h a s i s i n o r i g i n a l ) .

O r i g i n a l i t y d o e s n o t s i g n i f y n o v e l t y ; a w o r k m a y b e o r i g i n a l e v e n t h o u g h i t c l o s e l y r e s e m b l e s o t h e r w o r k s s o l o n g a s t h e s i m i l a r i t y i s f o r t u i t o u s , n o t t h e r e s u l t o f c o p y i n g . T o i l l u s t r a t e , a s s u m e t h a t t w o p o e t s , e a c h i g n o r a n t o f t h e o t h e r , c o m p o s e i d e n t i c a l p o e m s . N e i t h e r w o r k i s n o v e l , y e t b o t h a r e o r i g i n a l a n d , h e n c e , c o p y r i g h t a b l e .

O r i g i n a l i t y i s a c o n s t i t u t i o n a l r e q u i r e m e n t . T h e s o u r c e o f C o n g r e s s ' p o w e r t o e n a c t c o p y r i g h t l a w s i s A r t i c l e I , § 8 , c l . 8 , o f t h e C o n s t i t u t i o n , w h i c h a u t h o r i z e s C o n g r e s s t o " s e c u r [ e ] f o r l i m i t e d T i m e s t o A u t h o r s . . . t h e e x c l u s i v e R i g h t t o t h e i r r e s p e c t i v e W r i t i n g s . " I n t w o d e c i s i o n s f r o m t h e l a t e 1 9 t h c e n t u r y — T h e T r a d e - M a r k C a s e s , 1 0 0 U . S . 8 2 ( 1 8 7 9 ) ; a n d B u r r o w - G i l e s L i t h o g r a p h i c C o . v . S a r o n y , 1 1 1 U . S . 5 3 ( 1 8 8 4 ) — t h i s C o u r t d e f i n e d t h e c r u c i a l t e r m s " a u t h o r s " a n d " w r i t i n g s . " I n s o d o i n g , t h e C o u r t m a d e i t u n m i s t a k a b l y c l e a r t h a t t h e s e t e r m s p r e s u p p o s e a d e g r e e o f o r i g i n a l i t y .

T h e k e y t o r e s o l v i n g t h e t e n s i o n l i e s i n u n d e r s t a n d i n g w h y f a c t s a r e n o t c o p y r i g h t a b l e . T h e s i n e q u a n o n o f c o p y r i g h t i s o r i g i n a l i t y . T o q u a l i f y f o r c o p y r i g h t p r o t e c t i o n , a w o r k m u s t b e o r i g i n a l t o t h e a u t h o r . S e e H a r p e r & R o w , s u p r a , a t 5 4 7 - 5 4 9 . O r i g i n a l , a s t h e t e r m i s u s e d i n c o p y r i g h t , m e a n s o n l y t h a t t h e w o r k w a s i n d e p e n d e n t l y c r e a t e d b y t h e a u t h o r ( a s o p p o s e d t o c o p i e d f r o m o t h e r w o r k s ) , a n d t h a t i t p o s s e s s e s a t l e a s t s o m e m i n i m a l d e g r e e o f c r e a t i v i t y . 1 M . N i m m e r & D . N i m m e r , C o p y r i g h t § § 2 . 0 1 [ A ] , [ B ] ( 1 9 9 0 ) ( h e r e i n a f t e r N i m m e r ) . T o b e s u r e , t h e r e q u i s i t e l e v e l o f c r e a t i v i t y i s e x t r e m e l y l o w ; e v e n a s l i g h t a m o u n t w i l l s u f f i c e . T h e v a s t m a j o r i t y o f w o r k s m a k e t h e g r a d e q u i t e e a s i l y , a s t h e y p o s s e s s s o m e c r e a t i v e s p a r k , " n o m a t t e r h o w c r u d e , h u m b l e o r o b v i o u s " i t m i g h t b e . I d . , § 1 . 0 8 [ C ] [ 1 ] .

T h e r e i s a n u n d e n i a b l e t e n s i o n b e t w e e n t h e s e t w o p r o p o s i t i o n s . M a n y c o m p i l a t i o n s c o n s i s t o f n o t h i n g b u t r a w d a t a — i . e . , w h o l l y f a c t u a l i n f o r m a t i o n n o t a c c o m p a n i e d b y a n y o r i g i n a l w r i t t e n e x p r e s s i o n . O n w h a t b a s i s m a y o n e c l a i m a c o p y r i g h t i n s u c h a w o r k ? C o m m o n s e n s e t e l l s u s t h a t 1 0 0 u n c o p y r i g h t a b l e f a c t s d o n o t m a g i c a l l y c h a n g e t h e i r s t a t u s w h e n g a t h e r e d t o g e t h e r i n o n e p l a c e . Y e t c o p y r i g h t l a w s e e m s t o c o n t e m p l a t e t h a t c o m p i l a t i o n s t h a t c o n s i s t e x c l u s i v e l y o f f a c t s a r e p o t e n t i a l l y w i t h i n i t s s c o p e

i t i s b e y o n d d i s p u t e t h a t c o m p i l a t i o n s o f f a c t s a r e w i t h i n t h e s u b j e c t m a t t e r o f c o p y r i g h t . C o m p i l a t i o n s w e r e e x p r e s s l y m e n t i o n e d i n t h e C o p y r i g h t A c t o f 1 9 0 9 , a n d a g a i n i n t h e C o p y r i g h t A c t o f 1 9 7 6

T h i s c a s e c o n c e r n s t h e i n t e r a c t i o n o f t w o w e l l - e s t a b l i s h e d p r o p o s i t i o n s . T h e f i r s t i s t h a t f a c t s a r e n o t c o p y r i g h t a b l e ; t h e o t h e r , t h a t c o m p i l a t i o n s o f f a c t s g e n e r a l l y a r e . E a c h o f t h e s e p r o p o s i t i o n s p o s s e s s e s a n i m p e c c a b l e p e d i g r e e . T h a t t h e r e c a n b e n o v a l i d c o p y r i g h t i n f a c t s i s u n i v e r s a l l y u n d e r s t o o d . T h e m o s t f u n d a m e n t a l a x i o m o f c o p y r i g h t l a w i s t h a t " [ n ] o a u t h o r m a y c o p y r i g h t h i s i d e a s o r t h e f a c t s h e n a r r a t e s . " H a r p e r & R o w , P u b l i s h e r s , I n c . v . N a t i o n E n t e r p r i s e s , 4 7 1 U . S . 5 3 9 , 5 5 6 ( 1 9 8 5 ) .

T h i s c a s e r e q u i r e s u s t o c l a r i f y t h e e x t e n t o f c o p y r i g h t p r o t e c t i o n a v a i l a b l e t o t e l e p h o n e d i r e c t o r y w h i t e p a g e s

Alexander v. Haley, 460 F.Supp. 40 (S.D.N.Y. 1978)

Lecture 1: The Foundations of Copyright Law

In addition, the results imply that there is a lack of awareness about the importance of metadata among the scientific community –at least in practice– which is a serious problem as their involvement is quite crucial in dealing with problems regarding data management.

Less than half (45%) of the respondents are satisfied with their ability to integrate data from disparate sources to address research questions

Reasons for not making data electronically available. Regarding their attitudes towards data sharing, most of the respondents (85%) are interested in using other researchers' datasets, if those datasets are easily accessible. Of course, since only half of the respondents report that they make some of their data available to others and only about a third of them (36%) report their data is easily accessible, there is a major gap evident between desire and current possibility. Seventy-eight percent of the respondents said they are willing to place at least some their data into a central data repository with no restrictions. Data repositories need to make accommodations for varying levels of security or access restrictions. When asked whether they were willing to place all of their data into a central data repository with no restrictions, 41% of the respondents were not willing to place all of their data. Nearly two thirds of the respondents (65%) reported that they would be more likely to make their data available if they could place conditions on access. Less than half (45%) of the respondents are satisfied with their ability to integrate data from disparate sources to address research questions, yet 81% of them are willing to share data across a broad group of researchers who use data in different ways. Along with the ability to place some restrictions on sharing for some of their data, the most important condition for sharing their data is to receive proper citation credit when others use their data. For 92% of the respondents, it is important that their data are cited when used by other researchers. Eighty-six percent of survey respondents also noted that it is appropriate to create new datasets from shared data. Most likely, this response relates directly to the overwhelming response for citing other researchers' data. The breakdown of this section is presented in Table 13.

Data sharing practices. Only about a third (36%) of the respondents agree that others can access their data easily, although three-quarters share their data with others (see Table 11). This shows there is a willingness to share data, but it is difficult to achieve or is done only on request.

Nearly one third of the respondents chose not to answer whether they make their data available to others. Of those who did respond, 46% reported they do not make their data electronically available to others. Almost as many reported that at least some of their data are available somehow, either on their organization's website, their own website, a national network, a global network, a personal website, or other (see Table 10). The high percentage of non-respondents to this question most likely indicates that data sharing is even lower than the numbers indicate. Furthermore, the less than 6% of scientists who are making “All” of their data available via some mechanism, tends to re-enforce the lack of data sharing within the communities surveyed.

Adding descriptive metadata to datasets helps makes the dataset more accessible by others and into the future. Respondents were asked to indicate all metadata standards they currently use to describe their data. More than half of the respondents (56%) reported that they did not use any metadata standard and about 22% of respondents indicated they used their own lab metadata standard. This could be interpreted that over 78% of survey respondents either use no metadata or a local home grown metadata approach.

Data reuse. Respondents were asked to indicate whether they have the sole responsibility for approving access to their data. Of those who answered this question, 43% (n=545) have the sole responsibility for all their datasets, 37% (n=466) have for some of their datasets, and 21% (n=266) do not have the sole responsibility.

Policies and procedures sometimes serve as an active rather than passive barrier to data sharing. Campbell et al. (2003) reported that government agencies often have strict policies about secrecy for some publicly funded research. In a survey of 79 technology transfer officers in American universities, 93% reported that their institution had a formal policy that required researchers to file an invention disclosure before seeking to commercialize research results. About one-half of the participants reported institutional policies that prohibited the dissemination of biomaterials without a material transfer agreement, which have become so complex and demanding that they inhibit sharing [15].

data practices of researchers – data accessibility, discovery, re-use, preservation and, particularly, data sharing

The Data Life Cycle: An Overview The data life cycle has eight components: Plan : description of the data that will be compiled, and how the data will be managed and made accessible throughout its lifetime Collect : observations are made either by hand or with sensors or other instruments and the data are placed a into digital form Assure : the quality of the data are assured through checks and inspections Describe : data are accurately and thoroughly described using the appropriate metadata standards Preserve : data are submitted to an appropriate long-term archive (i.e. data center ) Discover : potentially useful data are located and obtained, along with the relevant information about the data ( metadata ) Integrate : data from disparate sources are combined to form one homogeneous set of data that can be readily analyzed Analyze : data are analyzed

collect -> analyze -> publish

ask someone -> receive data in email -> analyze -> cite -> publish -> tenure

Data represent important products of the scientific enterprise that are, in many cases, of equivalent or greater value than the publications that are originally derived from the research process. For example, addressing many of the grand challenge scientific questions increasingly requires collaborative research and the reuse , integration, and synthesis of data.

Journals and sponsors want you to share your data

An effective data management program would enable a user 20 years or longer in the future to discover , access , understand, and use particular data [ 3 ]. This primer summarizes the elements of a data management program that would satisfy this 20-year rule and are necessary to prevent data entropy .

A key component of data management is the comprehensive description of the data and contextual information that future researchers need to understand and use the data. This description is particularly important because the natural tendency is for the information content of a data set or database to undergo entropy over time (i.e. data entropy ), ultimately becoming meaningless to scientists and others [ 2 ].

data entropy Normal degradation in information content associated with data and metadata over time (paraphrased from [ 2 ]).

Although data management plans may differ in format and content, several basic elements are central to managing data effectively.

By documenting your data and recommending appropriate ways to cite your data, you can be sure to get credit for your data products and their use

This primer describes a few fundamental data management practices that will enable you to develop a data management plan, as well as how to effectively create, organize, manage, describe, preserve and share data

The goal of data management is to produce self-describing data sets. If you give your data to a scientist or colleague who has not been involved with your project, will they be able to make sense of it? Will they be able to use it effectively and properly?

If federally funded research is going to broadly benefit society, it has to be widely accessible, not just to curious private citizens, but also to industries, private organizations, and federal, state, and local governments where scientific knowledge can help create new products, solve problems, educate students, and make policy decisions.

Giving the public what it paid for sounds noble, but from where I sit, a scientist at a well-funded research university, ensuring that research papers are available to the public for free seems pointless.

One respondent noted that NSF doesn't have an enforcement policy. This is presumably true of other mandate sources as well, and brings up the related and perhaps more significant problem that mandates are not always (if they are ever) accompanied by the funding required to satisfy them. Another respondent wrote that funding agencies expect universities to contribute to long-term data storage.

Data management activities, grouped. The data management activities mentioned by the survey can be grouped into five broader categories: "storage" (comprising backup or archival data storage, identifying appropriate data repositories, day-to-day data storage, and interacting with data repositories); "more information" (comprising obtaining more information about curation best practices and identifying appropriate data registries and search portals); "metadata" (comprising assigning permanent identifiers to data, creating and publishing descriptions of data, and capturing computational provenance); "funding" (identifying funding sources for curation support); and "planning" (creating data management plans at proposal time). When the survey results are thus categorized, the dominance of storage is clear, with over 80% of respondents requesting some type of storage-related help. (This number may also reflect a general equating of curation with storage on the part of respondents.) Slightly fewer than 50% of respondents requested help related to metadata, a result explored in more detail below.

Locally and/or externally focused departments. These departments look almost exclusively to external repositories or locally-provided solutions. To the extent these solutions suffice, the departments may need little help from campus.

Data management activities, grouped. The data management activities mentioned by the survey can be grouped into five broader categories: "storage" (comprising backup or archival data storage, identifying appropriate data repositories, day-to-day data storage, and interacting with data repositories); "more information" (comprising obtaining more information about curation best practices and identifying appropriate data registries and search portals); "metadata" (comprising assigning permanent identifiers to data, creating and publishing descriptions of data, and capturing computational provenance); "funding" (identifying funding sources for curation support); and "planning" (creating data management plans at proposal time). When the survey results are thus categorized, the dominance of storage is clear, with over 80% of respondents requesting some type of storage-related help. (This number may also reflect a general equating of curation with storage on the part of respondents.) Slightly fewer than 50% of respondents requested help related to metadata, a result explored in more detail below.

Distribution of departments with respect to responsibility spheres. Ignoring the "Myself" choice, consider clustering the parties potentially responsible for curation mentioned in the survey into three "responsibility spheres": "local" (comprising lab manager, lab research staff, and department); "campus" (comprising campus library and campus IT); and "external" (comprising external data repository, external research partner, funding agency, and the UC Curation Center). Departments can then be positioned on a tri-plot of these responsibility spheres, according to the average of their respondents' answers. For example, all responses from FeministStds (Feminist Studies) were in the campus sphere, and thus it is positioned directly at that vertex. If a vertex represents a 100% share of responsibility, then the dashed line opposite a vertex represents a reduction of that share to 20%. For example, only 20% of ECE's (Electrical and Computer Engineering's) responses were in the campus sphere, while the remaining 80% of responses were evenly split between the local and external spheres, and thus it is positioned at the 20% line opposite the campus sphere and midway between the local and external spheres. Such a plot reveals that departments exhibit different characteristics with respect to curatorial responsibility, and look to different types of curation solutions.

In the course of your research or teaching, do you produce digital data that merits curation? 225 of 292 (77%) of respondents answered "yes" to this first question, which corresponds to 25% of the estimated population of 900 faculty and researchers who received the survey.

Responsibility, myself versus others. It may appear that responses to the question of responsibility are bifurcated between "Myself" and all other parties combined. However, respondents who identified themselves as being responsible were more likely than not to identify additional parties that share that responsibility. Thus, curatorial responsibility is seen as a collaborative effort. (The "Nobody" category is a slight misnomer here as it also includes non-responses to this question.)

Awareness of data and commitment to its preservation are two key preconditions for successful data curation.

Which parties do you believe have primary responsibility for the curation of your data? Almost all respondents identified themselves as being personally responsible.

Availability of the raw survey data is subject to the approval of the UCSB Human Subjects Committee.

Survey design The survey was intended to capture as broad and complete a view of data production activities and curation concerns on campus as possible, at the expense of gaining more in-depth knowledge.

Researchers may be underestimating the need for help using archival storage systems and dealing with attendant metadata issues.

Researchers view curation as a collaborative activity and collective responsibility.

To summarize the survey's findings: Curation of digital data is a concern for a significant proportion of UCSB faculty and researchers. Curation of digital data is a concern for almost every department and unit on campus. Researchers almost universally view themselves as personally responsible for the curation of their data. Researchers view curation as a collaborative activity and collective responsibility. Departments have different curation requirements, and therefore may require different amounts and types of campus support. Researchers desire help with all data management activities related to curation, predominantly storage. Researchers may be underestimating the need for help using archival storage systems and dealing with attendant metadata issues. There are many sources of curation mandates, and researchers are increasingly under mandate to curate their data. Researchers under curation mandate are more likely to collaborate with other parties in curating their data, including with their local labs and departments. Researchers under curation mandate request more help with all curation-related activities; put another way, curation mandates are an effective means of raising curation awareness. The survey reflects the concerns of a broad cross-section of campus.

In 2012 the Data Curation @ UCSB Project surveyed UCSB campus faculty and researchers on the subject of data curation, with the goals of 1) better understanding the scope of the digital curation problem and the curation services that are needed, and 2) characterizing the role that the UCSB Library might play in supporting curation of campus research outputs.

In all cases, one standard is clear: Each of these vendors is betting very heavily on HTML5-based applications as well as methods to make HTML5 compatibility the basis for their future. Whether made from Java or other language frameworks, HTML5 is the common thread that runs through each of these alternative mobile operating systems. Start with HTML5, and your applications’ portability is almost assured.

the parties, the procedural posture, the facts, the issue , the h olding, and the analysis.

When a law student briefs a case, he typically identifies several pieces of information: the parties, the procedural posture, the facts, the issue , the h olding, and the analysis. Although it seems foreign at first, identifying this information, understanding judicial opinions , and applying their reasoning to new cases becomes much easier with practice.

the judge will state the legal issue(s) involved, her decision about the issue s (the holding) , and her reasoning.

H o w t o R e a d O p i n i o n s

The opinion will also typically give the name of the judge or justice who wrote it. In some cases, judges sitting together will decide not to reveal wh o wrote an opinion. In that situation, it will say p e r c u r i a m /DWLQIRU³E\WKHFRXUW ́ ) i QSODFHRIDMXGJH¶VQDPH

In a judicial opinion, the judge explains her ruling and the reasoning behind it. At its heart, an opinion is similar to a scholarly essay or even a short story. However, like any genre, the judicial opinion has some unique and unusual characteristics.

Student brief A student brief is a short summary and analysis of the case prepared for use in classroom discussion. It is a set of notes, presented in a systematic way, in order to sort out the parties, identify the issues, ascertain what was decided, and analyze the reasoning behind decisions made by the courts. Although student briefs always include the same items of information, the form in which these items are set out can vary. Before committing yourself to a particular form for briefing cases, check with your instructor to ensure that the form you have chosen is acceptable.

Appellate brief An appellate brief is a written legal argument presented to an appellate court. Its purpose is to persuade the higher court to uphold or reverse the trial court’s decision. Briefs of this kind are therefore geared to presenting the issues involved in the case from the perspective of one side only. Appellate briefs from both sides can be very valuable to anyone assessing the legal issues raised in a case. Unfortunately, they are rarely published. The U.S. Supreme Court is the only court for which briefs are regularly available in published form. The Landmark Briefs series (REF. LAW KF 101.9 .K8) includes the full texts of briefs relating to a very few of the many cases heard by this court. In addition, summaries of the briefs filed on behalf of the plaintiff or defendant for all cases reported are included in the U.S. Supreme Court Reports. Lawyer’s Ed., 2nd. series (REF. LAW KF 101 .A42).

Confusion often arises over the term “legal brief.” There are at least two different senses in which the term is used.

This suggests that peer production will thrive where projects have three characteristi cs

the proposition that diverse motivations animate human beings, and, more importantly, that there exist ranges of human experience in which the presence of monetary rewards is inversely related to the presence of other, social-psychological rewards.

common appropriation regimes do not give a complete answer to the sustainability of motivation and organization for the truly open, large-scale nonproprietary peer production projects we see on the Internet.

The following selection from The Yale Law Journal is not paginated and should not be used for citation purposes.

understanding that when a project of any size is broken up into little pieces, each of which can be performed by an individual in a short amount of time, the motivation to get any given individual to contribute need only be very small.

I call this new kind of bad roadway behavior vague driving. It's as if people are sort of surprised to find themselves behind the wheel of a car. They're slightly puzzled by the experience. They may even be a little intimidated by the car. I'm not sure.

Once you abandon entirely the crazy idea that the type of a value has anything whatsoever to do with the storage, it becomes much easier to reason about it. Of course, my point above stands: you don't need to reason about it unless you are writing unsafe code or doing some sort of heavy interoperating with unmanaged code. Let the compiler and the runtime manage the lifetime of your storage locations; that's what its good at.

There are three kinds of storage locations: stack locations, heap locations, and registers.

There are three kinds of values: (1) instances of value types, (2) instances of reference types, and (3) references. (Code in C# cannot manipulate instances of reference types directly; it always does so via a reference. In unsafe code, pointer types are treated like value types for the purposes of determining the storage requirements of their values.)

Having made these points many times in the last few years, I've realized that the fundamental problem is in the mistaken belief that the type system has anything whatsoever to do with the storage allocation strategy. It is simply false that the choice of whether to use the stack or the heap has anything fundamentally to do with the type of the thing being stored. The truth is: the choice of allocation mechanism has to do only with the known required lifetime of the storage.

A lot of people seem to think that heap allocation is expensive and stack allocation is cheap. They are actually about the same, typically. It’s the deallocation costs – the marking and sweeping and compacting and moving memory from generation to generation – that are massive for heap memory compared to stack memory.

Now compare this to the stack. The stack is like the heap in that it is a big block of memory with a “high water mark”. But what makes it a “stack” is that the memory on the bottom of the stack always lives longer than the memory on the top of the stack; the stack is strictly ordered. The objects that are going to die first are on the top, the objects that are going to die last are on the bottom. And with that guarantee, we know that the stack will never have holes, and therefore will not need compacting. We know that the stack memory will always be “freed” from the top, and therefore do not need a free list. We know that anything low-down on the stack is guaranteed alive, and so we do not need to mark or sweep.

This sketch is complicated by the fact that there are actually three such arenas; the CLR collector is generational. Objects start off in the “short lived” heap. If they survive they eventually move to the “medium lived” heap, and if they survive there long enough, they move to the “long lived” heap. The GC runs very often on the short lived heap and very seldom on the long lived heap; the idea is that we do not want to have the expense of constantly re-checking a long-lived object to see if it is still alive. But we also want short-lived objects to be reclaimed swiftly.

When a garbage collection is performed there are three phases: mark, sweep and compact. In the “mark” phase, we assume that everything in the heap is “dead”. The CLR knows what objects were “guaranteed alive” when the collection started, so those guys are marked as alive. Everything they refer to is marked as alive, and so on, until the transitive closure of live objects are all marked. In the “sweep” phase, all the dead objects are turned into holes. In the “compact” phase, the block is reorganized so that it is one contiguous block of live memory, free of holes.

If we’re in that situation when new memory is allocated then the “high water mark” is bumped up, eating up some of the previously “free” portion of the block. The newly-reserved memory is then usable for the reference type instance that has just been allocated. That is extremely cheap; just a single pointer move, plus zeroing out the newly reserved memory if necessary.

The idea is that there is a large block of memory reserved for instances of reference types. This block of memory can have “holes” – some of the memory is associated with “live” objects, and some of the memory is free for use by newly created objects. Ideally though we want to have all the allocated memory bunched together and a large section of “free” memory at the top.

I regret that the documentation does not focus on what is most relevant; by focusing on a largely irrelevant implementation detail, we enlarge the importance of that implementation detail and obscure the importance of what makes a value type semantically useful. I dearly wish that all those articles explaining what “the stack” is would instead spend time explaining what exactly “copied by value” means and how misunderstanding or misusing “copy by value” can cause bugs.

I find this characterization of a value type based on its implementation details rather than its observable characteristics to be both confusing and unfortunate

Surely the most relevant fact about value types is not the implementation detail of how they are allocated, but rather the by-design semantic meaning of “value type”, namely that they are always copied “by value”.

I blogged a while back about how “references” are often described as “addresses” when describing the semantics of the C# memory model. Though that’s arguably correct, it’s also arguably an implementation detail rather than an important eternal truth. Another memory-model implementation detail I often see presented as a fact is “value types are allocated on the stack”. I often see it because of course, that’s what our documentation says.

Additional broader impacts will emerge from analyses of the whooping crane dataset. Through collaborations with endangered species biologists in the US Geological Survey, these analyses will have direct relevance to specific management actions for the whooping crane, such as the timing, group size, and composition of crane reintroductions and potentially their training with ultra-light aircraft.

The project will develop an analysis package in the open-source language R and complement it with a step-by-step hands-on manual to make tools available to a broad, international user community that includes academics, scientists working for governments and non-governmental organizations, and professionals directly engaged in conservation practice and land management. The software package will be made publicly available under http://www.clfs.umd.edu/biology/faganlab/movement/.

These species represent not only different types of movement (on land, in air, in water) but also different types of relocation data (from visual observations of individually marked animals to GPS relocations to relocations obtained from networked sensor arrays).

For example, by statistically analyzing the interrelationships of relocation data among individuals, it will be possible to distinguish and quantify population-level movement patterns such as migration, range residency, and nomadism.

intra-individual concordance

This project will develop new and innovative data management and analysis tools focusing on the interrelationship of multiple moving individuals. These include measures that calculate 1) realized mobility (quantifying the relationship of individual to population ranges), 2) population dispersion (quantifying the spatial relationship among individuals), 3) movement coordination (quantifying the coordination of movements among individuals), and 4) intra-individual concordance (quantifying the spatial relationship of relocations of individuals over time). These innovative ways of treating animal movement data will allow researchers to investigate a broad range of new research questions.

but scientists' understanding of the emergent spatial dynamics at the population level has not kept pace, in large part due to an absence of appropriate tools for data handling and statistical analysis.

A grant is awarded to University of Maryland, College Park to develop informatics tools that allow scientists and conservation managers to use animal relocation and tracking data to study movement processes at the population level.

NSF Advances in Biological Informatics: "Informatics tools for population-level animal movements." with T. Mueller, P. Leimgruber, A. Royle, and J. Calabrese. Thomas Mueller, an Assistant Research Scientist in my lab, leads this project. Also on this grant, postdoc Chris Fleming is investigating theoretical aspects of animal foraging and statistical issues associated with empirical data on animal movements. This project is developing innovative data management and analysis tools that will allow scientists and conservation managers to use animal relocation and tracking data to study movement processes at the population-level, focusing on the interrelationship of multiple moving individuals. We are developing and testing these new tools using datasets on Mongolian gazelles, whooping cranes, and blacktip sharks. More information is available on the Movement Dynamics Homepage.

My project seeks to develop computer models that simulate and link behavioral movement mechanisms which can be either based on memory, perceptual cues or triggered by environmental factors. It explores their efficiency under different scenarios of resource distributions across time and space. Finally it tries to integrate empirical data on resource distributions as well as movements of moving animals, such as satellite data on primary productivity and satellite tracking data of Mongolian gazelles.

News Thomas Mueller and Bill Fagan receive a new NSF Bioinformatics grant Collaborators Peter Leimgruber Smithsonian Institution Volker Grimm Centre for Environmental Research - UFZ, Leipzig Kirk A. Olson University of Massachusetts Todd K. Fuller University of Massachusetts George B. Schaller Wildlife Conservation Society Nuria Selva Institute of Nature Conservation, Krakow

In the Middle Ages, just the opposite was true. Reading was generally done aloud, often to an audience. It was an active process, so active that Susan Noakes, in her analysis of medieval reading, points out “that it had been recommended by physicians, since classical times, as a mild form of exercise, like walking.”

that’s the strange thing about writing, which makes it truly analogous to painting. The painter’s products stand before us as if they were alive, but if you question them, they maintain a most majestic silence. It is the same with written words; they seem to talk to you as if they were intelligent, but if you ask them anything about what they say, from a desire to be instructed, they go on telling you just the same thing forever.

Socrates was concerned with reflective thought: the ability to think deeply about things, to question and examine every statement. He thought that reading was experiential, that it would not lead to reflection.

Questioning and examination are the tools of reflection: Hear an idea, ponder it, question it, modify it, explore its limitations. When the idea is presented by a person, the audience can interrupt, ask questions, probe to get at the underlying assumptions. But the author doesn’t come along with a book, so how could the book be questioned if it couldn’t answer back? This is what bothered Socrates.

Socrates, Plato tells us, argued that books would destroy thought.

Creating an atlas is more encompassing than image acquisition and analysis. It requires a clear understanding of the biological questions to be addressed. Then appropriate labeling, sample preparation, imaging, image analysis, visualization, and data management methods must be selected (Figure 2). An interdisciplinary team is required that collectively possess the needed expertise. Generating useful atlases is still in its infancy. Which methods to use at each step along the pipeline will depend greatly on what analysis is required. There is currently no ‘magic toolbox’ that scientists can use to apply to their specific task. Each step has to be tailored to suit the experiment.

The database should have an associated web site for access by internal researchers working on atlas construction and quite likely a separate Web site for public access to published datasets.

The creation and exploitation of large-scale quantitative atlases will lead to a more precise understanding of development.

The results of the subsequent mathematical analysis can often be exported as additional rows and columns into an updated version of the atlas and then explored by the biologist using the visualization tool.

This exploration may itself lead to novel discoveries, but will also help the biologist better understand the quality and nature of the dataset, improving his or her ability to suggest analyses to computational colleagues.

The challenge is that while biologists best understand the questions that can be addressed using the atlas, they may not always possess the computational and mathematical skills needed to conduct sophisticated analyses of such data files. For this reason, biologists generally collaborate with computational scientists. It is not always clear, though, what is the best way to frame the analysis.

Once a searchable atlas has been constructed there are fundamentally two approaches that can be used to analyze the data: one visual, the other mathematical.

The initial inputs for deriving quantitative information of gene expression and embryonic morphology are raw image data, either of fluorescent proteins expressed in live embryos or of stained fluorescent markers in fixed material. These raw images are then analyzed by computational algorithms that extract features, such as cell location, cell shape, and gene product concentration. Ideally, the extracted features are then recorded in a searchable database, an atlas, that researchers from many groups can access. Building a database with quantitative graphical and visualization tools has the advantage of allowing developmental biologists who lack specialized skills in imaging and image analysis to use their knowledge to interrogate and explore the information it contains.

approaches to establish permanent, quantitative datasets—atlases

A rigorous understanding of these developmental processes requires automated methods that quantitatively record and analyze complex morphologies and their associated patterns of gene expression at cellular resolution.

Just as comprehensive datasets of genomic sequence have revolutionalized biological discovery, large-scale quantitative measurements of gene expression and morphology will certainly be of great assistance in enabling computational embryology in the future. Such datasets will form the essential basis for systems level, computational models of molecular pathways and how gene expression concentrations and interactions alter to drive changes in cell shape, movement, connection, and differentiation. In this review, we discuss the strategies and methods used to generate such datasets.

These large-scale quantitative data provide new insights that could not have been gained through qualitative analyses.

Qualitative statements describe in a yes/no manner for example, which tissues a gene is expressed in or if two groups of cells move relative to one another. This basic information is insufficient, though, to address many fundamental questions in developmental biology.

Difference between XZ and LZMA2 Short answer: xz is a format that (currently) only uses the lzma2 compression algorithm. Long answer: think of xz as a container for the compression data generated by the lzma2 algorithm. We also have this paradigm for video files for example: avi/mkv/mov/mp4/ogv are containers, and xvid/x264/theora are compression algorithms. The confusion is often made because currently, the xz format only supports the lzma2 algorithm (and it’ll remain the default, even if some day, others algorithms may be added). This confusion doesn’t happen with other formats/algorithms, as for example gzip is both a compression algorithm and a format. To be exact, the gzip format only supports to encapsulate data generated by gzip… the compression algorithm. In this article I’ll use “xz” to say “the lzma2 algorithm whose data is being encapsulated by the xz format”. You’ll probably agree it’s way simpler

at its core, DevOps is about culture

The true essence of DevOps is empathy.

Information exchange requires (and can contribute to) mutual understanding; e.g., empathy.

I was surprised to encounter empathy again in the context of cybernetics. This rediscovery happened thanks to a Twitter exchange with @seungchan​. Cybernetics tells us that, in order for any one or any thing to function, it must have a relationship with other people and/or things.

I first encountered empathy as an explicit design principle in the context of design thinking. You can’t design anything truly useful unless you understand the people for whom you’re designing.

Is it because ops care deeply about systems while devs consider them a tool or implementation detail?

The closest thing to common ground may be events for configuration-management software like PuppetConf or ChefConf, or possibly re:Invent.

When I look at the DevOps “community” today, what I generally see is a near-total lack of overlap between people who started on the dev side and on the ops side.

a China Miéville novel called The City & the City. It’s about two cities that literally overlap in geography, with the residents of each completely ignoring the other — and any violations, or breaches, of that separation are quickly enforced by a shadowy organization known as the Breach.

Until academics get their acts together and start using new modes of publication, we need to recognize that actions like Aaron Swartz's civil disobedience are legitimate.

Aaron Swartz's act of hacktivism was an act of resistance to a corrupt system that has subverted distribution of the most important product of the academy—knowledge.

the philosophy department at the University of Michigan at Ann Arbor started an online journal called Philosophers' Imprint, noting in its mission statement the possibility of a sunnier alternative: "There is a possible future in which academic libraries no longer spend millions of dollars purchasing, binding, housing, and repairing printed journals, because they have assumed the role of publishers, cooperatively disseminating the results of academic research for free, via the Internet. Each library could bear the cost of publishing some of the world's scholarly output, since it would be spared the cost of buying its own copy of any scholarship published in this way. The results of academic research would then be available without cost to all users of the Internet, including students and teachers in developing countries, as well as members of the general public."

And JSTOR really was in an impossible bargaining position. Important scientific papers do not have cheaper alternatives. If someone wants to read Watson and Crick's paper on DNA or Einstein's paper on the photoelectric effect, it is not as if there is a paper by John Doe that is just as good and available for less. Academic publishers are, in effect, natural monopolies that can demand as much money as we can afford, and possibly more.

But like the original authors, JSTOR had to negotiate its licensing agreements from a position of weakness. There is a wonderful history of JSTOR written by Roger C. Schonfeld. In it he notes that the charter publishers signed up by JSTOR (in particular the University of Chicago Press) demanded that they be compensated if there was a loss to their (minimal) sales of rights to older materials, and they demanded compensation even before JSTOR covered its own expenses.

JSTOR, which did not pursue criminal charges against Swartz and "regretted being drawn into" the U.S. attorney's case against him, came into existence in 1995 with good intentions. It sought a solution to the rapidly expanding problem of paying for and storing an ever-growing list of academic journals. The situation for libraries was becoming untenable.

Academic publishers have inverted their whole purpose for being; they used to be vehicles for the dissemination of knowledge in the most efficient way possible. Today they are useless choke points in the distribution of knowledge, even taking advantage of their positions to demand fees.

There was a time when securing a contract with an academic publisher meant that the work would receive the widest audience possible.

a "contract of adhesion"—meaning a contract in which one party has all the power and it was not freely bargained.

If you don't publish, you won't get tenure. Even if you have tenure, your reputation (and salary) is staked to your publication record.

The consensus so far has been that Swartz did something wrong by accessing and releasing millions of academic papers from the JSTOR archive.

He laid the philosophical groundwork back in 2008, in an essay entitled "Guerilla Open Access Manifesto."

The academic publisher Elsevier has contributed to many U.S. Congressional representatives, pushing the Elsevier-supported Research Works Act, which among other things would have forbidden any effort by any federal agency to ensure taxpayer access to work financed by the federal government without permission of the publisher.

"There is no justice in following unjust laws. It's time to come into the light and, in the grand tradition of civil disobedience, declare our opposition to this private theft of public culture."

Server error, there was an error while handling your request. Administrators have been notified, please try reloading the page.

We regularly provide scholars with access to content for this purpose. Our Data for Research site (http://dfr.jstor.org)

The criminal investigation and today’s indictment of Mr. Swartz has been directed by the United States Attorney’s Office. It was the government’s decision whether to prosecute, not JSTOR’s. As noted previously, our interest was in securing the content. Once this was achieved, we had no interest in this becoming an ongoing legal matter.

The Harvard Business Review has been writing about the benefits of cultures of gratitude in the workplace.

more than half of all employees intended to search for new jobs because they felt underappreciated and undervalued.

Jon R. Katzenbach and Douglas K. Smith, authors of the Wisdom of Teams, define a high-performing team in part by members’ strong personal commitment to the growth and success of each team member and of the team as a whole.

High performing teams have well-defined goals, systems of accountability, clear roles and responsibilities, and open communication.

Support camaraderie and collegiality

Involve employees

Help others develop

Several recent articles point out the importance of saying “thank you” and giving specific praise to employees when earned in genuine, honest, and heartfelt ways. Mark Gaston’s blog on How to Give a Meaningful Thank-you is full of great advice such as sharing with employees how their contributions had personal significance for the leader and team.

Tell them what it personally meant to you.

Acknowledge to them the effort (or personal sacrifice) that they made in doing the above.

Thank them for something they specifically did that was above the call of duty.

So take action now. Give that person what I call a Power Thank You. This has three parts

research by Adam Grant and Francesca Gino has shown that saying thank you not only results in reciprocal generosity — where the thanked person is more likely to help the thanker — but stimulates prosocial behavior in general. In other words, saying “thanks” increases the likelihood your employee will not only help you, but help someone else.

They are a person deserving of your not infrequent acknowledgment and worthy of appreciation and respect. When was the last time you thanked them — really thanked them?

research by Adam Grant and Francesca Gino has shown that saying thank you not only results in reciprocal generosity — where the thanked person is more likely to help the thanker — but stimulates prosocial behavior in general. In other words, saying “thanks” increases the likelihood your employee will not only help you, but help someone else.

too often, they begin to view and treat their teams, and especially their assistants, as appliances

So when I wrote to her boss, I included this: “When I get to be rich, I’m going to hire someone like your assistant — to protect me from people like me. She was helpful, friendly, feisty vs. boring and yet guarded access to you like a loyal pit bull. If she doesn’t know how valuable she is to you, you are making a big managerial mistake and YOU should know better.”

How we meet this challenge depends on how we address the following fundamental question about teaching our digital-age children: Should we teach our children as though they have two lives, or one?

The tie that binds us to our ancestors is that both ancient and digital-age humans crave community—and all the things that make community possible: survival, effective communication, cultural stability, purposeful education for our children, and creative expression.

But we were surprised that an unadorned set of 127 slides—no music, no animation—would become so influential.

Here’s a simple test: If your company has a performance bonus plan, go up to a random employee and ask, “Do you know specifically what you should be doing right now to increase your bonus?” If he or she can’t answer, the HR team isn’t making things as clear as they need to be.

Instead of cheerleading, people in my profession should think of themselves as businesspeople. What’s good for the company? How do we communicate that to employees? How can we help every worker understand what we mean by high performance?

I frequently see CEOs who are clearly winging it. They lack a real agenda. They’re working from slides that were obviously put together an hour before or were recycled from the previous round of VC meetings. Workers notice these things, and if they see a leader who’s not fully prepared and who relies on charm, IQ, and improvisation, it affects how they perform, too. It’s a waste of time to articulate ideas about values and culture if you don’t model and reward behavior that aligns with those goals.

We continually told managers that building a great team was their most important task. We didn’t measure them on whether they were excellent coaches or mentors or got their paperwork done on time. Great teams accomplish great work, and recruiting the right team was the top priority.

We distributed options every month, at a slight discount from the market price. We had no vesting period—the options could be cashed in immediately. Most tech companies have a four-year vesting schedule and try to use options as “golden handcuffs” to aid retention, but we never thought that made sense. If you see a better opportunity elsewhere, you should be allowed to take what you’ve earned and leave. If you no longer want to work with us, we don’t want to hold you hostage.

We also believed in market-based pay and would tell employees that it was smart to interview with competitors when they had the chance, in order to get a good sense of the market rate for their talent. Many HR people dislike it when employees talk to recruiters, but I always told employees to take the call, ask how much, and send me the number—it’s valuable information.

Discussing the military’s performance during the Iraq War, Donald Rumsfeld, the former defense secretary, once famously said, “You go to war with the army you have, not the army you might want or wish to have at a later time.” When I talk to managers about creating great teams, I tell them to approach the process in exactly the opposite way.

I replied, “Why bother? We know how this will play out. You’ll write up objectives and deliverables for her to achieve, which she can’t, because she lacks the skills. Every Wednesday you’ll take time away from your real work to discuss (and document) her shortcomings. You won’t sleep on Tuesday nights, because you’ll know it will be an awful meeting, and the same will be true for her. After a few weeks there will be tears. This will go on for three months. The entire team will know. And at the end you’ll fire her. None of this will make any sense to her, because for five years she’s been consistently rewarded for being great at her job—a job that basically doesn’t exist anymore. Tell me again how Netflix benefits?

HR people can’t believe that a company the size of Netflix doesn’t hold annual reviews. “Are you making this up just to upset us?” they ask. I’m not. If you talk simply and honestly about performance on a regular basis, you can get good results—probably better ones than a company that grades everyone on a five-point scale.

Traditional corporate performance reviews are driven largely by fear of litigation. The theory is that if you want to get rid of someone, you need a paper trail documenting a history of poor achievement. At many companies, low performers are placed on “Performance Improvement Plans.” I detest PIPs. I think they’re fundamentally dishonest: They never accomplish what their name implies.

Eliminating a formal policy and forgoing expense account police shifted responsibility to frontline managers, where it belongs.

Instead, we tried really hard to not hire those people, and we let them go if it turned out we’d made a hiring mistake.

One day I was talking with one of our best engineers, an employee I’ll call John. Before the layoffs, he’d managed three engineers, but now he was a one-man department working very long hours. I told John I hoped to hire some help for him soon. His response surprised me. “There’s no rush—I’m happier now,” he said. It turned out that the engineers we’d laid off weren’t spectacular—they were merely adequate. John realized that he’d spent too much time riding herd on them and fixing their mistakes. “I’ve learned that I’d rather work by myself than with subpar performers,” he said. His words echo in my mind whenever I describe the most basic element of Netflix’s talent philosophy: The best thing you can do for employees—a perk better than foosball or free sushi—is hire only “A” players to work alongside them. Excellent colleagues trump everything else.

Despite her work ethic, her track record, and the fact that we all really liked her, her skills were no longer adequate. Some of us talked about jury-rigging a new role for her, but we decided that wouldn’t be right. So I sat down with Laura and explained the situation—and said that in light of her spectacular service, we would give her a spectacular severance package. I’d braced myself for tears or histrionics, but Laura reacted well: She was sad to be leaving but recognized that the generous severance would let her regroup, retrain, and find a new career path. This incident helped us create the other vital element of our talent management philosophy: If we wanted only “A” players on our team, we had to be willing to let go of people whose skills no longer fit, no matter how valuable their contributions had once been. Out of fairness to such people—and, frankly, to help us overcome our discomfort with discharging them—we learned to offer rich severance packages.

Rule of thumb: When pulling changes from origin/develop onto your local develop use rebase. When finishing a feature branch merge the changes back to develop.

If Master has diverged since the feature branch was created, then merging the fea - ture branch into master will create a merge commit. This is a typical merge.

People may use merge commits to represent the last deployed version of production code. That’s an antipattern. Use tags.

Treat yourself as a writer and approach each commit as a chapter in a book. Writers don’t publish first drafts. Michael Crichton said, “Great books aren’t written– they’re rewritten.”

Git is revolutionary because it gives you the best of both worlds. You can regularly check in changes while prototyping a solution but deliver a clean history when you’re finished. When this is your goal, Git’s defaults make a lot more sense.

If you’re fighting Git’s defaults, ask why. Treat public history as immutable, atomic, and easy to follow. Treat private history as disposable and malleable. The intended workflow is: Create a private branch off a public branch. Regularly commit your work to this private branch. Once your code is perfect, clean up its history. Merge the cleaned-up branch back into the public branch.

n its space-time representation (Ogata, 1998), the ETASmodel is a temporal marked point process model, and a special case of marked Hawks process, withconditional intensity function(t;x;yjHt) =(x;y) +Xti<tk(mi)g(tti)f(xxi;yyijmi)

This doesn’t mean gossip is always good.

“Witnessing the unfair play,” the researchers write, “led to elevated heart rates for participants who had no opportunity to gossip.”

Welcome one-on-ones Career planning

Blameless post-mortems

Create slack time for important improvement projects

They also started to standardize and very deliberately reduce the supported infrastructure and configurations. One decision was to switch everything to PHP and MySQL. This was a philosophical decision, not a technology one: they wanted both Dev and Ops to be able to understand the stack, so that everyone can contribute if they wanted to, as well as enabling everyone to be able to read, rewrite and fix someone else’s code.

and most importantly, a culture that the rest of the world admires.

Visible Ops Handbook

They have events like “Meetsy” (suggested lunch groups to meet people you may not work directly with) and “Eatsy” (where the entire company eats together).