70 Matching Annotations
  1. Mar 2019
  2. Feb 2019
    1. Creating a work of art will depend more and more on the ability of the artist to select, organize and present the bits of raw data we have at our disposal

      The Artist-Researcher paradigm

  3. Jan 2019
    1. It isn't rocket science, but as Jon indicates, it's incredibly powerful.

      I use my personal website with several levels of taxonomy for tagging and categorizing a variety of things for later search and research.

      Much like the example of the Public Radio International producer, I've created what I call a "faux-cast" because I tag everything I listen to online and save it to my website including the appropriate <audio> link to the.mp3 file so that anyone who wants to follow the feed of my listens can have a playlist of all the podcast and internet-related audio I'm listening to.

      A visual version of my "listened to" tags can be found at https://boffosocko.com/kind/listen/ with the RSS feed at https://boffosocko.com/kind/listen/feed/

  4. Oct 2018
    1. But on the Web, stories of all kinds can show up anywhere and information and news are all mixed together. Light features rotate through prominent spots on the "page" with the same weight as breaking news, sports coverage, and investigative pieces, even on mainstream news sites. Advertorial "features" and opinion pieces are not always clearly identified in digitalspaces.

      This difference is one of the things I miss about reading a particular newspaper and experiencing the outlet's particular curation of their own stories.Perhaps I should spend more time looking at the "front page" of various news sites.

    1. A more active stance by librarians, journalists, educators, and others who convey truth-seeking habits is essential.

      In some sense these people can also be viewed as aggregators and curators of sorts. How can their work be aggregated and be used to compete with the poor algorithms of social media?

  5. Aug 2018
    1. You find them in a place that you curate yourself, not one “curated” for you by a massive corporate social network intent on forcing you to be every part of yourself to everyone, all at once. You should control how, when, and where to interact with your people.
  6. Nov 2017
  7. Oct 2017
  8. Jul 2017
    1. A pedagogy of abundance, acknowledging that content often is freely available and abundant, may eventually prove relevant in this regard.

      This may be a great way to develop evaluation skills in "students". There is so much good on the web but you need to have the ability to distinguish it from the other.

  9. Jun 2017
    1. Althoughit cannot be taken for granted that the publication lists on RIDare error-free, these lists will probably be more reliable thantheautomatically generated lists (by Elsevier).

      Seems like a list which is automatically populated and then edited by an researcher would be better than one manually created. I don't think there's any factual basis for this claim.

  10. May 2017
    1. Lack of clarity about how DH work and outputs support institutional aims and who should “own” these outputs makes it difficult for those planning deeper investments, whether in the form of a research support system including workshops and training, or as a more entrepreneurial lab effort for building new grant-supported works.

      This also seems like it could relate to data curation in a couple of ways: If "ownership" of outputs is unclear, this may be a barrier to making data and/or outputs available for reuse, etc. Also, the sharing of this data/research could generate information about how DH work is contributing to the institutional mission.

  11. Apr 2017
  12. Jan 2017
    1. That’s not to say that social media curbs our self-awareness, or that our internet selves aren’t highly artificial and curated. Nor that people living in oppressive regimes, or as minorities in societies where they know they will be targeted, aren’t justifiably anxious about what they say online. But the point remains that digital media have radically transformed our conceptions of intimacy and shame, and they’ve done so in ways that are unpredictable and paradoxical.
  13. Dec 2016
    1. Daniel does a lot of great work

      CURATION BY CALLOUT

    2. try to find connecting points

      CURATION BY THREAD

      Trying to pull out common threads. Also, trying to pull out substantive differences.

    3. In the interest

      CURATION WITH OTHER MODES (image, text in image, gif, video, sound files)

    4. am removed from the anchor text

      CURATION WITHIN THE ANNOTATION FRAME

      CURATION OUTSIDE OF THE ANNOTATION FRAME

    5. rough take

      CURATING BY SUMMING UP

    6. to each other

      CURATION WITH EACH OTHER

    7. reacting to ideas

      CURATION BY REACTION

    8. It’s also invisible, to some degree.

      CURATING BY SHARING (PUBLIC, UNLISTED, and PRIVATE GROUPS)

      Private annotations for oneself and for private groups are also possible. Nice. Fine grained. Along with tagging, there are many possibilities to send annotations in multiple channels within Hypothes.is (private groups, public notes, tags, social sharing).

    9. These annotations are for me an experiment in meta-curation, curating about curation. I hope that we can draw some lessons in how to curate from Kevin's post. It would be great to do this elsewhere and then draw our discoveries together on Hypothes.is.

      In lieu of starting that project I have begun a private group where we can gather together curatorial strategies. Here is a link if you want to join Curation Strategies: https://hypothes.is/groups/j3eoYn2b/curation-strategies

      It might also be helpful to come up with a set of specific (curation strategies) and generic tags (curation) that we can search for on Hypothes.is here: https://hypothes.is/stream?q=tag:curation

    10. Sifting

      CURATING BY SIFTING

      Technique #1: thinking of the marginalia as vein to be mined for nuggets and gems. Begs the question: what criteria do we apply to the vein so that we can sift stuff?

      Notable quotes (why notable?) Main ideas Worthy figurative language (metaphors, symbols, analogies) New wine. Old wine in new bottles

  14. Oct 2016
    1. why encourage posting before you’ve even read the thing? Because, at least my hope is, it’ll prevent posting a link from becoming an endorsement for the content at the other end of that link. There’s a natural tendency to curate what we associate with our online profiles and I think that’s, in large part, because we’ve spent a lot of time equating a user’s profile page with a user’s identity and, consequently, their beliefs. But I consume a wealth of content that I don’t necessarily agree with, and that helps to inform me, to shape my opinions, as much as the content that I agree with wholeheartedly.
  15. Sep 2016
    1. curate

      The term may still sound somewhat misleading to those who work in, say, museums (where “curator” is a very specific job title). But the notion behind it is quite important, especially when it comes to Open Education. A big part of the job is to find resources and bring them together for further reuse, remix, and reappropriation. In French, we often talk about «veille technologique», which is basically about watching/monitoring relevant resources, especially online.

  16. Aug 2016
    1. the concept of the printed edition

      oui, plus exact que la distinction avec la "curation". Curation peut être une éditorialisation : ouvert et dynamique. c'est une éditorialisation spécifique.

    1. la curation de mon profil,

      je dirais que la curation s'effectue sur un profil, à travers un profil (curation de contenus externes), mais on ne fait pas la curation d'un profil.

  17. Jul 2016
    1. collaborate on joint, shared, or cooperative programs that address common educa-tional and training needs.

      are they asking for grant proposals on training for data curation for cultural heritage?

    2. support the re-use of data over time and across generations of technology (digital curation)

      is this how they're defining digital curation?

  18. Apr 2016
    1. Centralizing content and distributing labor: a community model for curating the very long tail of microbial genomes.

      This is going to be a good talk. Get your coffee, open your eyes, and open your mind! A pattern that could actually scale up - worth a try! Disagree? reply here.

  19. Feb 2016
    1. Educators

      Just got to think about our roles, in view of annotation. Using “curation” as a term for collecting URLs sounds like usurping the title of “curator”. But there’s something to be said about the role involved. From the whole “guide on the side” angle to the issue with finding appropriate resources based on a wealth of expertise.

  20. Dec 2015
    1. distributed curation

      While “web curation” is well-established as a practice, there’s still a lot of work to do on what it represents, conceptually and epistemologically.

  21. Nov 2015
    1. some kind of curated library

      Which is where OER catalogues (tied to the Semantic Web) may shine. Sure, they can require a lot of work. But this is precisely why they matter.

  22. Oct 2015
    1. long time curating these tomes

      Part of the argument for OER might come from more efficient ways to curate this type of material. Creating textbooks is some people’s main goal, but there’s a whole lot to be said about Open Coursepacks in Linked Open Data.

  23. Aug 2015
  24. Jan 2014
    1. 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.

      Categories of data sharing considered:

      • I would use other researchers' datasets if their datasets were easily accessible.
      • I would be willing to place at least some of my data into a central data repository with no restrictions.
      • I would be willing to place all of my data into a central data repository with no restrictions.
      • I would be more likely to make my data available if I could place conditions on access.
      • I am satisfied with my ability to integrate data from disparate sources to address research questions.
      • I would be willing to share data across a broad group of researchers who use data in different ways.
      • It is important that my data are cited when used by other researchers.
      • It is appropriate to create new datasets from shared data.
    2. 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.

      There is a willingness, but not a way!

    3. 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.
    4. 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.

      Not surprising that roughly 80% use no or ad hoc metadata.

    5. 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.
    6. 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].

      Policies and procedures are barriers, but there are many more barriers beyond that which get in the way first.

    7. data practices of researchers – data accessibility, discovery, re-use, preservation and, particularly, data sharing
      • data accessibility
      • discovery
      • re-use
      • preservation
      • data sharing
    1. 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

      The lifecycle according to who? This 8-component description is from the point of view of only the people who obsessively think about this "problem".

      Ask a researcher and I think you'll hear that lifecycle means something like:

      collect -> analyze -> publish
      

      or a more complex data management plan might be:

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

      To most people lifecycle means "while I am using the data" and archiving means "my storage guy makes backups occasionally".

      Asking people to be aware of the whole cycle outlined here is a non-starter, but I think there is another approach to achieve what we want... dramatic pause [to be continued]

      What parts of this cycle should the individual be responsible for vs which parts are places where help is needed from the institution?

    2. 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.

      Who else might care about this other than Grand Challenge Question researchers?

    3. Journals and sponsors want you to share your data

      What is the sharing standard? What are the consequences of not sharing? What is the enforcement mechanism?

      There are three primary sharing mechanisms I can think of today: email, usb stick, and dropbox (née ftp).

      The dropbox option is supplanting ftp which comes from another era, but still satisfies an important niche for larger data sets and/or higher-volume or anonymous traffic.

      Dropbox, email and usb are all easily accessible parts of the day-to-day consumer workflow; they are all trivial to set up without institutional support or, importantly, permission.

      An email account is already provisioned by default for everyone or, if the institutional email offerings are not sufficient, a person may easily set up a 3rd-party email account with no permission or hassle.

      Data management alternatives to these three options will have slow or no adoption until the barriers to access and use are as low as email; the cost of entry needs to be no more than *a web browser, an email address, and no special permission required".

    4. 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 .

      Who cares most about the 20-year rule? This is an ideal that appeals to some, but in practice even the most zealous adherents can't picture what this looks like in some concrete way-- except in the most traditional ways: physical paper journals in libraries are tangible examples of the 20-year rule.

      Until we have a digital equivalent for data I don't blame people looking for tenure or jobs for not caring about this ideal if we can't provide a clear picture of how to achieve this widely at an institutional level. For digital materials I think the picture people have in their minds is of tape backup. Maybe this is generational? New generations not exposed widely to cassette tapes, DVDs, and other physical media that "old people" remember, only then will it be possible to have a new ideal that people can see in their minds-eye.

    5. 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 ].

      I agree with the key component mentioned here, but I feel the term data entropy is an unhelpful crutch.

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

      I'm not sure what this really means and I don't think data entropy is a helpful term. Poor practices certainly lead to disorganized collections of data, but I think this notion comes from a time when people were very concerned about degradation of physical media on which data is stored. That is, of course, still a concern, but I think the term data entropy really lends itself as an excuse for people who don't use good practices to manage data and is a cover for the real problem which is a kind of data illiteracy in much the same way we also face computational illiteracy widely in the sciences. Managing data really is hard, but let's not mask it with fanciful notions like data entropy.

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

      What are the "several basic elements?"

    8. 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

      Citation is an incentive. An answer to the question "What's in it for me?"

    9. 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

      Data management practices:

      • create
      • organize
      • manage
      • describe
      • preserve
      • share
    10. 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?
    1. 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.
    2. 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.

      Categories of data management activities:

      • storage
        • backup/archival data storage
        • identifying appropriate data repositories
        • day-to-day data storage
        • interacting with data repositories
      • more information
        • obtaining more information about curation best practices
        • identifying appropriate data registries
        • search portals
      • metadata
        • assigning permanent identifiers to data
        • creating/publishing descriptions of data
        • capturing computational provenance
      • funding
        • identifying funding sources for curation support
      • planning
        • creating data management plans at proposal time
    3. 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.

      Storage is a broad topic and is a very frequently mentioned topic in all of the University-run surveys.

      http://www.alexandria.ucsb.edu/~gjanee/dc@ucsb/survey/plots/q4.2.png

      Highlight by Chris during today's discussion.

    4. 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.

      This section contains an interesting diagram showing the distribution of departments with respect to responsibility spheres:

      http://www.alexandria.ucsb.edu/~gjanee/dc@ucsb/survey/plots/q2.5.png

    5. 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.

      For those who do not feel they have data that merits curation I would at least like to hear a description of the kinds of data they have and why they feel it does not need to be curated?

      For some people they may already be using well-curated data sets; on the other hand there are some people who feel their data may not be useful to anyone outside their own research group, so there is no need to curate the data for use by anyone else even though under some definition of "curation" there may be important unmet curation needs for internal-use only that may be visible only to grad students or researchers who work with the data hands-on daily.

      UPDATE: My question is essentially answered here: https://hypothes.is/a/xBpqzIGTRaGCSmc_GaCsrw

    6. 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.)

      This answers my previous question about this survey item:

      https://hypothes.is/a/QrDAnmV8Tm-EkDuHuknS2A

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

      Great observation!

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

      For those that identify themselves as personally responsible would they identify themselves (or their group) as the only ones responsible for the data? Or is there a belief that the institution should also be responsible in some way in addition to themselves?

    9. Availability of the raw survey data is subject to the approval of the UCSB Human Subjects Committee.
    10. 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.

      Summary of the survey design

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

      In my mind this is a key challenge: even if people can describe what they need for themselves (that in itself is a very hard problem), what to do from the infrastructure standpoint to implement services that aid the individual researcher and also aid collaboration across individuals in the same domain, as well as across domains and institutions... in a long-term sustainable way is not obvious.

      In essence... how do we translate needs that we don't yet fully understand into infrastructure with low barrier to adoption, use, and collaboration?

    12. Researchers view curation as a collaborative activity and collective responsibility.
    13. 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.

      Summary of survey findings.

    14. 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.

      1) better understanding the scope of the digital curation problem and the curation services that are needed

      2) characterizing the role that the UCSB Library might play in supporting curation of campus research outputs.