985 Matching Annotations
  1. Last 7 days
    1. For example, the idea of “data ownership” is often championed as a solution. But what is the point of owning data that should not exist in the first place? All that does is further institutionalise and legitimate data capture. It’s like negotiating how many hours a day a seven-year-old should be allowed to work, rather than contesting the fundamental legitimacy of child labour. Data ownership also fails to reckon with the realities of behavioural surplus. Surveillance capitalists extract predictive value from the exclamation points in your post, not merely the content of what you write, or from how you walk and not merely where you walk. Users might get “ownership” of the data that they give to surveillance capitalists in the first place, but they will not get ownership of the surplus or the predictions gleaned from it – not without new legal concepts built on an understanding of these operations.
    1. These models are emerging, which is why its exciting to be involved in the ground floor of this sector, however some models clearly make sense already and thats largely because they closely follow the models free software itself has shaped. If you want status, then you can make a name for yourself by leading a team to write the docs ala free software itself, if you want money then build the reputation for the documentation team and contract out your knowledge (eg. extend the docs on contract ala free software).

      Creo que hay que conectarlo con modelos de microfinanciación y tiendas independientes tipo Itch.io y que el experimento debería ser progresivo pero dejar un mapa posible de su propio futuro. Algo así intentaremos en la edición 13a del Data Week.

  2. Feb 2019
    1. Dissecting Flavivirus Biology in Salivary Gland Cultures from Fed and Unfed Ixodes scapularis (Black-Legged Tick)

      Data worth viewing: a tick trachea with viral infection in its salivary glands.

    1. !..�P'�r\0CA \= e,;4 ��'-"-'

      Could empirical data made up of experiences present in the form of an ethnography? Or autoethnography? I'm not sure if this is what you were getting at here, but it is a thought that came to mind!

  3. Jan 2019
    1. Nyhan and Reifler also found that presenting challenging information in a chart or graph tends to reduce disconfirmation bias. The researchers concluded that the decreased ambiguity of graphical information (as opposed to text) makes it harder for test subjects to question or argue against the content of the chart.

      Amazingly important double-edged finding for discussions of data visualization!

    1. Big Data is a buzzword which works on the platform of large data volume and aggregated data sets. The data sets can be structured or unstructured.  The data that is kept and stored at a global level keeps on growing so this big data has a big potential.  Your Big Data is generated from every little thing around us all the time. It has changed its way as the people are changing in the organization. New skills are being offered to prepare the new generated power of Big Data. Nowadays the organizations are focusing on new roles, new challenges and creating a new business.  
  4. demandlab.weebly.com demandlab.weebly.com
    1. y bosses want to see quick wins, but I know we can achieve big w

      add "My data (database) quality sucks"

    1. The main thing Smith has learned over the past seven years is “the importance of ownership.” He admitted that Tumblr initially helped him “build a community around the idea of digital news.” However, it soon became clear that Tumblr was the only one reaping the rewards of its growing community. As he aptly put it, “Tumblr wasn’t seriously thinking about the importance of revenue or business opportunities for their creators.”
    1. You may not access or use the Site in any manner that could damage or overburden any MIT server, or any network connected to any MIT server. You may not use the Site in any manner that would interfere with any other party’s use of the Site.

      Vamos a realizar pequeños scrapping, que no sobrecargarán el servidor, así que estamos cumpliendo con esta parte y de hecho, después de que trabajemos, permitiran repartir la carga del servidor, pues una copia estará en nuestros servidores.

    1. Adoption of good practice to generate high quality data will depend on sharing the burden of capacity building in some way. That in turn, can-not happen until there is a framework that provides sufficient trust to allow the sharing and compar-ison of data and its management.

      harkening to the 'data trust' concept being discussed from U.S. Mellon-funded projects, also co-authored by the authors of this paper.

    1. I tried very hard in that book, when it came to social media, to be platform agnostic, to emphasize that social media sites come and go, and to always invest first and foremost in your own media. (Website, blog, mailing list, etc.)
  5. Dec 2018
    1. Outliers : All data sets have an expected range of values, and any actual data set also has outliers that fall below or above the expected range. (Space precludes a detailed discussion of how to handle outliers for statistical analysis purposes, see: Barnett & Lewis, 1994 for details.) How to clean outliers strongly depends on the goals of the analysis and the nature of the data.

      Outliers can be signals of unanticipated range of behavior or of errors.

    2. Understanding the structure of the data : In order to clean log data properly, the researcher must understand the meaning of each record, its associated fi elds, and the interpretation of values. Contextual information about the system that produced the log should be associated with the fi le directly (e.g., “Logging system 3.2.33.2 recorded this fi le on 12-3-2012”) so that if necessary the specifi c code that gener-ated the log can be examined to answer questions about the meaning of the record before executing cleaning operations. The potential misinterpretations take many forms, which we illustrate with encoding of missing data and capped data values.

      Context of the data collection and how it is structured is also a critical need.

      Example, coding missing info as "0" risks misinterpretation rather than coding it as NIL, NDN or something distinguishable from other data

    3. Data transformations : The goal of data-cleaning is to preserve the meaning with respect to an intended analysis. A concomitant lesson is that the data-cleaner must track all transformations performed on the data .

      Changes to data during clean up should be annotated.

      Incorporate meta data about the "chain of change" to accompany the written memo

    4. Data Cleaning A basic axiom of log analysis is that the raw data cannot be assumed to correctly and completely represent the data being recorded. Validation is really the point of data cleaning: to understand any errors that might have entered into the data and to transform the data in a way that preserves the meaning while removing noise. Although we discuss web log cleaning in this section, it is important to note that these principles apply more broadly to all kinds of log analysis; small datasets often have similar cleaning issues as massive collections. In this section, we discuss the issues and how they can be addressed. How can logs possibly go wrong ? Logs suffer from a variety of data errors and distortions. The common sources of errors we have seen in practice include:

      Common sources of errors:

      • Missing events

      • Dropped data

      • Misplaced semantics (encoding log events differently)

    5. In addition, real world events, such as the death of a major sports fi gure or a political event can often cause people to interact with a site differently. Again, be vigilant in sanity checking (e.g., look for an unusual number of visitors) and exclude data until things are back to normal.

      Important consideration for temporal event RQs in refugee study -- whether external events influence use of natural disaster metaphors.

    6. Recording accurate and consistent time is often a challenge. Web log fi les record many different timestamps during a search interaction: the time the query was sent from the client, the time it was received by the server, the time results were returned from the server, and the time results were received on the client. Server data is more robust but includes unknown network latencies. In both cases the researcher needs to normalize times and synchronize times across multiple machines. It is common to divide the log data up into “days,” but what counts as a day? Is it all the data from midnight to midnight at some common time reference point or is it all the data from midnight to midnight in the user’s local time zone? Is it important to know if people behave differently in the morning than in the evening? Then local time is important. Is it important to know everything that is happening at a given time? Then all the records should be converted to a common time zone.

      Challenges of using time-based log data are similar to difficulties in the SBTF time study using Slack transcripts, social media, and Google Sheets

    7. Log Studies collect the most natural observations of people as they use systems in whatever ways they typically do, uninfl uenced by experimenters or observers. As the amount of log data that can be collected increases, log studies include many different kinds of people, from all over the world, doing many different kinds of tasks. However, because of the way log data is gathered, much less is known about the people being observed, their intentions or goals, or the contexts in which the observed behaviors occur. Observational log studies allow researchers to form an abstract picture of behavior with an existing system, whereas experimental log stud-ies enable comparisons of two or more systems.

      Benefits of log studies:

      • Complement other types of lab/field studies

      • Provide a portrait of uncensored behavior

      • Easy to capture at scale

      Disadvantages of log studies:

      • Lack of demographic data

      • Non-random sampling bias

      • Provide info on what people are doing but not their "motivations, success or satisfaction"

      • Can lack needed context (software version, what is displayed on screen, etc.)

      Ways to mitigate: Collecting, Cleaning and Using Log Data section

    8. Two common ways to partition log data are by time and by user. Partitioning by time is interesting because log data often contains signifi cant temporal features, such as periodicities (including consistent daily, weekly, and yearly patterns) and spikes in behavior during important events. It is often possible to get an up-to-the- minute picture of how people are behaving with a system from log data by compar-ing past and current behavior.

      Bookmarked for time reference.

      Mentions challenges of accounting for time zones in log data.

    9. An important characteristic of log data is that it captures actual user behavior and not recalled behaviors or subjective impressions of interactions.

      Logs can be captured on client-side (operating systems, applications, or special purpose logging software/hardware) or on server-side (web search engines or e-commerce)

    10. Table 1 Different types of user data in HCI research

    11. Large-scale log data has enabled HCI researchers to observe how information diffuses through social networks in near real-time during crisis situations (Starbird & Palen, 2010 ), characterize how people revisit web pages over time (Adar, Teevan, & Dumais, 2008 ), and compare how different interfaces for supporting email organi-zation infl uence initial uptake and sustained use (Dumais, Cutrell, Cadiz, Jancke, Sarin, & Robbins, 2003 ; Rodden & Leggett, 2010 ).

      Wide variety of uses of log data

    12. Behavioral logs are traces of human behavior seen through the lenses of sensors that capture and record user activity.

      Definition of log data

    1. Ethnographic findings are not privileged, just particular: another country heard from. To regard them as anything more (or anything less) than that distorts both them and their implications, which are far profounder than mere primitivity, for social theory.

      This tension exists in HCI as well.

      Interpreted data vs empirical data and how each is systematically analyzed.

    1. With Alphabet Inc.’s Google, and Facebook Inc. and its WhatsApp messaging service used by hundreds of millions of Indians, India is examining methods China has used to protect domestic startups and take control of citizens’ data.

      Governments owning citizens' data directly?? Why not have the government empower citizens to own their own data?

  6. Nov 2018
    1. One way to think about "core" biodiversity data is as a network of connected entities, such as taxa, taxonomic names, publications, people, species, sequences, images, collections, etc. (Fig. 1)
    1. “It’s about embracing the inscrutable nature of human interactions,” says Chang. Evidence-based medicine was a massive improvement over intuition-based medicine, he says, but it only covers traditionally quantifiable data, or those things that are easy to measure. But we’re now quantifying information that was considered qualitative a generation ago.

      Biggest challenges to redesigning the health care system in a way that would work better for patients and improve health

    2. “Our biggest opportunity is leaning into that. It’s either embracing the qualitative nature of that and designing systems that can act just on the qualitative nature of their experience, or figuring how to quantitate some of those qualitative measures,” says Chang. “That’ll get us much further, because the real value in health care systems is in the human interactions. My relationship with you as a doctor and a patient is far more valuable than the evidence that some trial suggests.”

      Biggest challenges to redesigning the health care system in a way that would work better for patients and improve health

    1. The Chinese place a higher value on community good versus individual rights, so most feel that, if social credit will bring a safer, more secure, more stable society, then bring it on
    1. Unless you need to push the boundaries of what these technologies are capable of, you probably don’t need a highly specialized team of dedicated engineers to build solutions on top of them. If you manage to hire them, they will be bored. If they are bored, they will leave you for Google, Facebook, LinkedIn, Twitter, … – places where their expertise is actually needed. If they are not bored, chances are they are pretty mediocre. Mediocre engineers really excel at building enormously over complicated, awful-to-work-with messes they call “solutions”. Messes tend to necessitate specialization.
    1. For the second, we could try to detect inconsistencies, eitherby inspecting samples of the class hierarchy

      Yes, that's what I do when doing quality work on the taxonomy (with the tool wdtaxonomy)

    2. Possible relations between Items

      This only includes properties of data-type item?! It should be made more clear because the majority of Wikidata classes has other data types.

    3. A KG typically spans across several domains and is built on topof a conceptual schema, orontology, which defines what types of entities (classes) are allowed inthe graph, alongside the types ofpropertiesthey can have

      Wikidata differs from typical KG as it is not build on top of classes (entity types). Any item (entity) can be connected by any property. Wikidata's only strict "classes" in the sense of KG classes are its data types (item, lemma, monolingual string...).

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    1. Entscheidend ist, dass sie Herren des Verfahrens bleiben - und eine Vision für das neue Maschinenzeitalter entwickeln.

      Es sieht für mich nicht eigentlich so aus als wären wir jemals die "Herren des Verfahrens" gewesen. Und auch darum geht es ja bei Marx. Denke ich.

    1. Does the widespread and routine collection of student data in ever new and potentially more-invasive forms risk normalizing and numbing students to the potential privacy and security risks?

      What happens if we turn this around - given a widespread and routine data collection culture which normalizes and numbs students to risk as early as K-8, what are our responsibilities (and strategies) to educate around this culture? And how do our institutional practices relate to that educational mission?

  7. Oct 2018
    1. As a recap, Chegg discovered on September 19th a data breach dating back to April that "an unauthorized party" accessed a data base with access to "a Chegg user’s name, email address, shipping address, Chegg username, and hashed Chegg password" but no financial information or social security numbers. The company has not disclosed, or is unsure of, how many of the 40 million users had their personal information stolen.

    1. tl;dr: data engineer = software, coding, cleaning data sets data architects = structure the technology to manage data models and database admin data scientist = stats + math models business analysts = communication and domain expertise

    1. research publications are not research data

      they could be, if used as part of a text mining corpus, for example

  8. Sep 2018
    1. Third, the post-LMS world should protect the pedagogical prerogatives and intellectual property rights of faculty members at all levels of employment. This means, for example, that contingent faculty should be free to take the online courses they develop wherever they happen to be teaching. Similarly, professors who choose to tape their own lectures should retain exclusive rights to those tapes. After all, it’s not as if you have to turn over your lecture notes to your old university whenever you change jobs.

      Own your pedagogy. Send just like anything else out there...

    1. I love the voice of their help page. Someone very opinionated (in a good way) is building this product. I particularly like this quote: Your data is a liability to us, not an asset.
    1. End-Users

      Because Grafoscopio was used in critical digital literacy workshops, dealing with data activism and journalism, the intended users are people who don't know how to program necessarily, but are not afraid of learning to code to express their concerns (as activists, journalists and citizens in general) and if fact are wiling to do so.

      Tool adaptation was "natural" of the workshops, because the idea was to extend the tool so it can deal with authentic problems at hand (as reported extensively in the PhD thesis) and digital citizenship curriculum was build in the events as a memory of how we deal with the problems. But critical digital literacy is a long process, so coding as a non-programmers knowledge in service of wider populations able to express in code, data and visualizations citizen concerns is a long time process.

      Visibility, scalability and sustainablitiy of such critical digital literacy endeavors where communities and digital tools change each other mutually is still an open problem, even more considering their location in the Global South (despite addressing contextualized global problems).

    1. In October 2014 the Open Knowledge Foundation recommends the Creative Commons CC0 license to dedicate content to the public domain,[51][52] and the Open Data Commons Public Domain Dedication and License (PDDL) for data.[53]
    1. predictive analysis

      Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.

  9. Aug 2018
    1. this possibility of increased ownership and agency over technology and a somewhat romantic idea I have that this can transfer to inspire ownership and agency over learning
    1. A file containing personal information of 14.8 million Texas residents was discovered on an unsecured server. It is not clear who owns the server, but the data was likely compiled by Data Trust, a firm created by the GOP.