3,211 Matching Annotations
  1. Last 7 days
    1. Bij datacenters in het Science Park komt warmte vrij bij het koelen van de servers.

      Meer Energie data center warmte van het Science Park

    1. 3.1 Guest Lecture: Lauren Klein » Q&A on "What is Feminist Data Science?"<br /> https://www.complexityexplorer.org/courses/162-foundations-applications-of-humanities-analytics/segments/15631

      https://www.youtube.com/watch?v=c7HmG5b87B8

      Theories of Power

      Patricia Hill Collins' matrix of domination - no hierarchy, thus the matrix format

      What are other broad theories of power? are there schools?

      Relationship to Mary Parker Follett's work?

      Bright, Liam Kofi, Daniel Malinsky, and Morgan Thompson. “Causally Interpreting Intersectionality Theory.” Philosophy of Science 83, no. 1 (January 2016): 60–81. https://doi.org/10.1086/684173.

      about Bayesian modeling for intersectionality


      Where is Foucault in all this? Klein may have references, as I've not got the context.


      How do words index action? —Laura Klein


      The power to shape discourse and choose words - relationship to soft power - linguistic memes

      Color Conventions Project


      20:15 Word embeddings as a method within her research


      General result (outside of the proximal research) discussed: women are more likely to change language... references for this?


      [[academic research skills]]: It's important to be aware of the current discussions within one's field. (LK)


      36:36 quantitative imperialism is not the goal of humanities analytics, lived experiences are incredibly important as well. (DK)

    1. https://www.complexityexplorer.org/courses/162-foundations-applications-of-humanities-analytics/segments/15630

      https://www.youtube.com/watch?v=HwkRfN-7UWI


      Seven Principles of Data Feminism

      • Examine power
      • Challenge power
      • Rethink binaries and hierarchies
      • Elevate emotion an embodiment
      • Embrace pluralism
      • Consider context
      • Make labor visible

      Abolitionist movement

      There are some interesting analogies to be drawn between the abolitionist movement in the 1800s and modern day movements like abolition of police and racial justice, etc.


      Topic modeling - What would topic modeling look like for corpuses of commonplace books? Over time?


      wrt article: Soni, Sandeep, Lauren F. Klein, and Jacob Eisenstein. “Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers.” Journal of Cultural Analytics 6, no. 1 (January 18, 2021). https://doi.org/10.22148/001c.18841. - Brings to mind the difference in power and invisible labor between literate societies and oral societies. It's easier to erase oral cultures with the overwhelm available to literate cultures because the former are harder to see.

      How to find unbiased datasets to study these?


      aspirational abolitionism driven by African Americans in the 1800s over and above (basic) abolitionism

    1. Big tech has benefited from an educational dynamic that consistently underfunds public education but demands increased technology to prepare the workers of the future, providing low-cost solutions in exchange for data and the potential for future product loyalty

      This is a pattern most of us are familiar with. The best example I know is Apple's launch of the iPad in LA schools without saying, or knowning, how it will be used. Apple has a long history of testing its products out on users. Google habitually does the same, offering products for "free" in exchange for data and expanding a user base for its products.

  2. Jan 2023
    1. 个人学习可能取决于他人行为的主张突出了将学习环境视为一个涉及多个互动参与者的系统的重要性
    1. In March, Fortum and Microsoft announced our joint plan for a ground-breaking data centre region in the Helsinki, Finland metro­politan area.

      Data centers and district heating - a perfect match. Clean electricity and then output for heat.

    1. Blind news audiences are being left behind in the data visualisation revolution: here's how we fix that

      !- Title : Blind news audiences are being left behind in the data visualisation revolution: here's how we fix that

    1. When engaging in data literacy work in our classrooms, it’s helpful to keep two ideas at play at once: on the one hand, these algorithmic systems are nowhere near as “smart” as these platforms want to lead us to believe they are; and on the other hand, concerns about accuracy can distract us from the bigger picture, that these platforms are built on a logic of prediction that, one nudge at a time, may ultimately infringe upon users’ ability to make up their own mind.
    1. If you have experienced trouble in rememberingdates try the following system which has proved beneficial to at least onestudent.

      Maxfield suggest drawing out a timeline as a possible visual cue for helping to remember dates. He seemingly misses any mention of ars memoria techniques here.

    1. ProPublica recently reported that breathing machines purchased by people with sleep apnea are secretly sending usage data to health insurers, where the information can be used to justify reduced insurance payments.

      !- surveillance capitalism : example- - Propublica reported breathing machines for sleep apnea secretly send data to insurance companies

    1. Actually I’m not sure most people do this, I just hope I’m not the only one.

      You are not. I will hoard this blog post on my hypothes.is :)

    1. We believe that the numeric notational marks associated with the animals constituted a calendar, and given that it references natural behaviour in terms of seasons relative to a fixed point in time, we may refer to it as a phenological calendar, with a meteorological basis.
    2. We have proposed the existence of a notational system associated with an unambiguous animal subject, relating to biologically significant events informed by the ethological record, which allows us for the first time to understand a Palaeolithic notational system in its entirety. This utilized/allowed the function of ordinality (and, later, place value), which were revolutionary steps forward in information recording.
    1. Data Viz with Python and RLearn to Make Plots in Python and R

      data viz with python and R

    1. We can have a machine learning model which gives more than 90% accuracy for classification tasks but fails to recognize some classes properly due to imbalanced data or the model is actually detecting features that do not make sense to be used to predict a particular class.

      Les mesures de qualite d'un modele de machine learning

  3. Dec 2022
    1. According to an analysis from the Wall Street Journal, the top 1% of Twitch streamers made over 50% of all money paid out by the platform in 2021. Furthermore, just 5% of users had made over $1,000 in the same year. Only 0.06% had made over the U.S. median household income of $67,521. In a survey of 5,000 community members composed of smaller Twitch streamers, Stream Scheme found that 76% were not able to reach Twitch’s $100 minimum payout threshold. Most others were making between $25-130 per month on the platform. 
    2. In a 2021 leak of Twitch’s user data that included creator payouts, it was revealed that from August 2019 to October 2021, the top 100 streamers on the platform made anywhere between $9,626,712.16 and $886,999.17. 
    1. Best times to post on social media overall: Tuesdays through Thursdays at 9 a.m. or 10 a.m. Best days to post on social media: Tuesdays through Thursdays Worst days to post on social media: Sundays
    1. Remember the book title and its genre. You will need to define the term "memoir," and recognize the publisher, title, and author for bibliographic information including the year of publication.

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  4. Nov 2022
    1. https://whatever.scalzi.com/2022/11/25/how-to-weave-the-artisan-web/

      “But Scalzi,” I hear you say, “How do we bring back that artisan, hand-crafted Web?” Well, it’s simple, really, and if you’re a writer/artist/musician/other sort of creator, it’s actually kind of essential:

    1. Our annotators achieve thehighest precision with OntoNotes, suggesting thatmost of the entities identified by crowdworkers arecorrect for this dataset.

      interesting that the mention detection algorithm gives poor precision on OntoNotes and the annotators get high precision. Does this imply that there are a lot of invalid mentions in this data and the guidelines for ontonotes are correct to ignore generic pronouns without pronominals?

    2. an algorithm with high precision on LitBank orOntoNotes would miss a huge percentage of rele-vant mentions and entities on other datasets (con-straining our analysis)

      these datasets have the most limited/constrained definitions for co-reference and what should be marked up so it makes sense that precision is poor in these datasets

    3. Procedure: We first launch an annotation tutorial(paid $4.50) and recruit the annotators on the AMTplatform.9 At the end of the tutorial, each annotatoris asked to annotate a short passage (around 150words). Only annotators with a B3 score (Bagga

      Annotators are asked to complete a quality control exercise and only annotators who achieve a B3 score of 0.9 or higher are invited to do more annotation

    4. Annotation structure: Two annotation ap-proaches are prominent in the literature: (1) a localpairwise approach, annotators are shown a pairof mentions and asked whether they refer to thesame entity (Hladká et al., 2009; Chamberlain et al.,2016a; Li et al., 2020; Ravenscroft et al., 2021),which is time-consuming; or (2) a cluster-basedapproach (Reiter, 2018; Oberle, 2018; Bornsteinet al., 2020), in which annotators group all men-tions of the same entity into a single cluster. InezCoref we use the latter approach, which can befaster but requires the UI to support more complexactions for creating and editing cluster structures.

      ezCoref presents clusters of coreferences all at the same time - this is a nice efficient way to do annotation versus pairwise annotation (like we did for CD^2CR)

    5. owever, these datasets vary widelyin their definitions of coreference (expressed viaannotation guidelines), resulting in inconsistent an-notations both within and across domains and lan-guages. For instance, as shown in Figure 1, whileARRAU (Uryupina et al., 2019) treats generic pro-nouns as non-referring, OntoNotes chooses not tomark them at all

      One of the big issues is that different co-reference datasets have significant differences in annotation guidelines even within the coreference family of tasks - I found this quite shocking as one might expect coreference to be fairly well defined as a task.

    6. Specifically, our work investigates the quality ofcrowdsourced coreference annotations when anno-tators are taught only simple coreference cases thatare treated uniformly across existing datasets (e.g.,pronouns). By providing only these simple cases,we are able to teach the annotators the concept ofcoreference, while allowing them to freely interpretcases treated differently across the existing datasets.This setup allows us to identify cases where ourannotators disagree among each other, but moreimportantly cases where they unanimously agreewith each other but disagree with the expert, thussuggesting cases that should be revisited by theresearch community when curating future unifiedannotation guidelines

      The aim of the work is to examine a simplified subset of co-reference phenomena which are generally treated the same across different existing datasets.

      This makes spotting inter-annotator disagreement easier - presumably because for simpler cases there are fewer modes of failure?

    7. this work, we developa crowdsourcing-friendly coreference annota-tion methodology, ezCoref, consisting of anannotation tool and an interactive tutorial. Weuse ezCoref to re-annotate 240 passages fromseven existing English coreference datasets(spanning fiction, news, and multiple other do-mains) while teaching annotators only casesthat are treated similarly across these datasets

      this paper describes a new efficient coreference annotation tool which simplifies co-reference annotation. They use their tool to re-annotate passages from widely used coreference datasets.

    1. An independent initiative made by Owen Cornec who has also made many other beautiful data visualizations. Wikiverse vividly captures the fact that Wikipedia is a an awe-inspiring universe to explore.

    1. One example could be putting all files into an Amazon S3 bucket. It’s versatile, cheap and integrates with many technologies. If you are using Redshift for your data warehouse, it has great integration with that too.

      Essentially the raw data needs to be vaguely homogenised and put into a single place

    1. Dr. Miho Ohsaki re-examined workshe and her group had previously published and confirmed that the results are indeed meaningless in the sensedescribed in this work (Ohsaki et al., 2002). She has subsequently been able to redefine the clustering subroutine inher work to allow more meaningful pattern discovery (Ohsaki et al., 2003)

      Look into what Dr. Miho Ohsaki changed about the clustering subroutine in her work and how it allowed for "more meaningful pattern discovery"

    2. Eamonn Keogh is an assistant professor of Computer Science at the University ofCalifornia, Riverside. His research interests are in Data Mining, Machine Learning andInformation Retrieval. Several of his papers have won best paper awards, includingpapers at SIGKDD and SIGMOD. Dr. Keogh is the recipient of a 5-year NSF CareerAward for “Efficient Discovery of Previously Unknown Patterns and Relationships inMassive Time Series Databases”.

      Look into Eamonn Keogh's papers that won "best paper awards"

    1. It took me a while to grok where dbt comes in the stack but now that I (think) I have it, it makes a lot of sense. I can also see why, with my background, I had trouble doing so. Just as Apache Kafka isn’t easily explained as simply another database, another message queue, etc, dbt isn’t just another Informatica, another Oracle Data Integrator. It’s not about ETL or ELT - it’s about T alone. With that understood, things slot into place. This isn’t just my take on it either - dbt themselves call it out on their blog:

      Also - just because their "pricing" page caught me off guard and their website isn't that clear (until you click through to the technical docs) - I thought it's worth calling out that DBT appears to be an open-core platform. They have a SaaS offering and also an open source python command-line tool - it seems that these articles are about the latter

    2. Of course, despite what the "data is the new oil" vendors told you back in the day, you can’t just chuck raw data in and assume that magic will happen on it, but that’s a rant for another day ;-)

      Love this analogy - imagine chucking some crude into a black box and hoping for ethanol at the other end. Then, when you end up with diesel you have no idea what happened.

    3. Working with the raw data has lots of benefits, since at the point of ingest you don’t know all of the possible uses for the data. If you rationalise that data down to just the set of fields and/or aggregate it up to fit just a specific use case then you lose the fidelity of the data that could be useful elsewhere. This is one of the premises and benefits of a data lake done well.

      absolutely right - there's also a data provenance angle here - it is useful to be able to point to a data point that is 5 or 6 transformations from the raw input and be able to say "yes I know exactly where this came from, here are all the steps that came before"

    1. binary string (i.e., a string in which each character in the string is treated as a byte of binary data)
    1. okay so remind you what is a sheath so a sheep is something that allows me to 00:05:37 translate between physical sources or physical realms of data and physical regions so these are various 00:05:49 open sets or translation between them by taking a look at restrictions overlaps 00:06:02 and then inferring

      Fixed typos in transcript:

      Just generally speaking, what can I do with this sheaf-theoretic data structure that I've got? Okay, [I'll] remind you what is a sheaf. A sheaf is something that allows me to translate between physical sources or physical realms of data [in the left diagram] and the data that are associated with those physical regions [in the right diagram]

      So these [on the left] are various open sets [an example being] simplices in a [simplicial complex which is an example of a] topological space.

      And these [on the right] are the data spaces and I'm able to make some translation between [the left and the right diagrams] by taking a look at restrictions of overlaps [a on the left] and inferring back to the union.

      So that's what a sheaf is [regarding data structures]. It's something that allows me to make an inference, an inferential machine.

    1. I also think being able to self-host and export parts of your data to share with others would be great.

      This might be achievable through Holochain application framework. One promising project built on Holochain is Neighbourhoods. Their "Social-Sensemaker Architecture" across "neighbourhoods" is intriguing

    1. with Prisma you never create application models in your programming language by manually defining classes, interfaces, or structs. Instead, the application models are defined in your Prisma schema
    1. high friction and cost of discovering, understanding, trusting, and ultimately using quality data. If not addressed, this problem only exacerbates with data mesh, as the number of places and teams who provide data - domains - increases.

      Encore un lien avec https://frictionlessdata.io/

    1. building common infrastructure

      Solution à la duplication des efforts et des données.

    2. A data product owner makes decisions around the vision and the roadmap for the data products, concerns herself with satisfaction of her consumers and continuously measures and improves the quality and richness of the data her domain owns and produces. She is responsible for the lifecycle of the domain datasets, when to change, revise and retire data and schemas. She strikes a balance between the competing needs of the domain data consumers.

      Ressemble aux rôles et responsabilités de nos intendants de données.

    1. CEO, Mike Tung was on Data science podcast. Seems to be solving problem that Google search doesn't; how seriously should you take the results that come up? What confidence do you have in their truth or falsity?

  5. Oct 2022
    1. only by examining a constellation of metrics in tension can we understand and influence developer productivity

      I love this framing! In my experience companies don't generally acknowledge that metrics can be in tension, which usually means they're only tracking a subset of the metrics they ought to be if they want to have a more complete/realistic understanding of the state of things.

    1. Software engineers typically stay at one job for an average of two years before moving somewhere different. They spend less than half the amount of time at one company compared to the national average tenure of 4.2 years.
    2. The average performance pay rise for most employees is 3% a year. That is minuscule compared to the 14.8% pay raise the average person gets when they switch jobs.
    1. There are a lot of PostgreSQL servers connected to the Internet: we searched shodan.io and obtained a sample of more than 820,000 PostgreSQL servers connected to the Internet between September 1 and September 29. Only 36% of the servers examined had SSL certificates. More than 523,000 PostgreSQL servers listening on the Internet did not use SSL (64%)
    2. At most 15% of the approximately 820,000 PostgreSQL servers listening on the Internet require encryption. In fact, only 36% even support encryption. This puts PostgreSQL servers well behind the rest of the Internet in terms of security. In comparison, according to Google, over 96% of page loads in Chrome on a Mac are encrypted. The top 100 websites support encryption, and 97 of those default to encryption.
    1. one recognizes in the tactile realitythat so many of the cards are on flimsy copy paper, on the verge of disintegration with eachuse.

      Deutsch used flimsy copy paper, much like Niklas Luhmann, and as a result some are on the verge of disintegration through use over time.

      The wear of the paper here, however, is indicative of active use over time as well as potential care in use, a useful historical fact.

    1. En cas de non-respect de la Loi, la Commission d’accès à l’information pourra imposer des sanctionsimportantes, qui pourraient s’élever jusqu’à 25 M$ ou à 4 % du chiffre d’affaires mondial. Cette sanctionsera proportionnelle, notamment, à la gravité du manquement et à la capacité de payer de l’entreprise.ENTREPRISES
    1. Noting the dates of available materials within archives or sources can be useful on bibliography notes for either planning or revisiting sources. (p16,18)

      Similarly one ought to note missing dates, data, volumes, or resources at locations to prevent unfruitfully looking for data in these locations or as a note to potentially look for the missing material in other locations. (p16)

  6. Sep 2022
    1. First, to clarify - what is "code", what is "data"? In this article, when I say "code", I mean something a human has written, that will be read by a machine (another program or hardware). When I say "data", I mean something a machine has written, that may be read by a machine, a human, or both. Therefore, a configuration file where you set logging.level = DEBUG is code, while virtual machine instructions emitted by a compiler are data. Of course, code is data, but I think this over-simplified view (humans write code, machines write data) will serve us best for now...
    1. The authors propose, based on these experiences, that the cause ofa number of unexpected difficulties in human-computer interaction lies in users’ unwillingness orinability to make structure, content, or procedures explicit

      I'm curious if this is because of unwillingness or difficulty.

  7. Aug 2022
    1. In practice, a system in which different parts of the web have different capabilities cannot insist on bidirectional links. Imagine, for example the publisher of a large and famous book to which many people refer but who has no interest in maintaining his end of their links or indeed in knowing who has refered to the book.

      Why it's pointless to insist that links should have been bidirectional: it's unenforceable.

    1. If the key, or the de-vice on which it is stored is compromised, or if avulnerability can be exploited, then the data assetcan be irrevocably stolen

      Another scenario, if the key or the storage-key device is compromised, or if vulnerability exploitation occurs, then data asset can be stolen.

    2. If akey is lost, this invariably means that the secureddata asset is irrevocably lost

      Counterpart, be careful! If a key is lost, the secured data asset is lost

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    1. Benjy Renton. (2021, November 16). New data update: Drawing from 23 states reporting data, 5.3% of kids ages 5-11 in these states have received their first dose. Vermont leads these states so far in vaccination rates for this age group—17%. The CDC will begin to report data for this group late this week. Https://t.co/LMJXl6lo6Z [Tweet]. @bhrenton. https://twitter.com/bhrenton/status/1460638150322180098

    1. Yaniv Erlich. (2021, December 8). Updated table of Omicron neuts studies with @Pfizer results (which did the worst job in terms of reporting raw data). Strong discrepancy between studies with live vs pseudo. Https://t.co/InQuWMAm4l [Tweet]. @erlichya. https://twitter.com/erlichya/status/1468580675007795204

    1. John Burn-Murdoch. (2021, November 25). Five quick tweets on the new variant B.1.1.529 Caveat first: Data here is very preliminary, so everything could change. Nonetheless, better safe than sorry. 1) Based on the data we have, this variant is out-competing others far faster than Beta and even Delta did 🚩🚩 https://t.co/R2Ac4e4N6s [Tweet]. @jburnmurdoch. https://twitter.com/jburnmurdoch/status/1463956686075580421