the notes you make about it as you curate it (which tells the AI exactly what you find useful about it)

Our goal is to have the best answers to every question, so if you see questions or answers that can be improved, you can edit them.

In fact, I think this self-answered Q&A of yours was already quite good by the standards of the site, and very useful - I've used it to close other duplicates several times. As someone who wears a "curator" hat around here, I want to make questions like this even better - as good as they can be - and make it clear to others that this is the right duplicate target to use when someone else asks the same question.

À cette fin, un groupe d'experts serait appelé à éditer ces contenus

la curation des contenus est un des éléments du processus d'éditorialisation, tandis que cette dernière désigne le processus dans son intégralité, prenant en considération tous les aspects de la production d'un contenu et du sens que ce contenu acquiert au sein d'une culture.

le concept d'éditorialisation implique une dimension culturelle qui n'est pas présente dans l'idée de curation

Every day, thousands of strangers upload little slices of their consciousness directly into my mind. My concern is that I'm prone to mistake their thoughts for my own — that some part of me believes I'm only hearing myself think.

dealised utopia

The most common way is to log the number of upvotes (or likes/downvotes/angry-faces/retweets/poop-emojis/etc) and algorithmically determine the quality of a post by consensus.

Since most of our feeds rely on either machine algorithms or human curation, there is very little control over what we actually want to see.

…and they are typically sorted: chronologically: newest items are displayed firstthrough data: most popular, trending, votesalgorithmically: the system determines what you see through your consumption patterns and what it wants you to seeby curation: humans determine what you seeby taxonomy: content is displayed within buckets of categories, like Wikipedia Most media entities employ a combination of the above.

Aaron Tay, a librarian at Singapore Management University who studies academic search tools, gets literature recommendations from both Twitter and Google Scholar, and finds that the latter often highlights the same articles as his human colleagues, albeit a few days later. Google Scholar “is almost always on target”, he says.

adopt the mindset of a curator – objective, opinionated, and reflective.

On iOS, not even dedicated file managers like DEVONthink are capable (or willing might be a more accurate term) of handling the diversity of data Telegram will happily pass on for you, especially through the Share Sheet.

The mere scribe and the mere compiler have disappeared (almost completely), and the mere commentator has become very rare. Each exists only insofar as any author in creating his own work cannot do without some copying, some compiling (or research), and some commenting.

the five Cs

In the second pass, read the paper with greater care, butignore details such as proofs.

The first pass is a quick scan to get a bird’s-eye view ofthe paper.

public collections

I will be looking for your conscious choice in your entry selections, dedicated organizational patterns and curation techniques, self-reflection and thoughtful responses in your short writing exercises, and as a whole, your engagement with and understanding of our various texts.

Taking turns at hosting shared the administrative load and the benefits that accrued. It was considered good practice to read all the submissions and craft your own post that would link to them, possibly exercising some selection, in a way that might entice readers to see for themselves. In that respect, because they were curated, blog carnivals to me are distinct from planets that merely accrete stuff, admittedly on a topic, without curation.

If we cannot afford real, diverse, and independent assessment, we will not realize the promise of middleware.

Building on platforms' stores of user-generated content, competing middleware services could offer feeds curated according to alternate ranking, labeling, or content-moderation rules.

Francis Fukuyama has called "middleware": content-curation services that could give users more control over the material they see on internet platforms such as Facebook or Twitter.

sharing it on a cloud-based platform.

But in my apt/deb world, where I use official repositories from my distro, where is the threat from 3rd party ? They are eventually « curated » in partner repository, or in universe

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

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.

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.

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.

LitCovid: Curated literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus .t3_fp7epr ._2FCtq-QzlfuN-SwVMUZMM3 { --postTitle-VisitedLinkColor: #9b9b9b; --postTitleLink-VisitedLinkColor: #9b9b9b; } methods and toolshttps://www.ncbi.nlm.nih.gov/research/coronavirus/LitCovid is a curated literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus. It is the most comprehensive resource on the subject, providing a central access to 1724 (and growing) relevant articles in PubMed. The articles are updated daily and are further categorized by different research topics and geographic locations for improved access. You can read more at Chen et al. Nature (2020) and download our data here.

LitCovid is a curated literature hub for tracking up-to-date scientific information about the 2019 novel Coronavirus. It is the most comprehensive resource on the subject, providing a central access to 1737 (and growing) relevant articles in PubMed. The articles are updated daily and are further categorized by different research topics and geographic locations for improved access. You can read more at Chen et al. Nature (2020) and download our data here.

Reverse engineering a bronze cannon from theLaBelleshipwreck

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

Investment in data curation and governance is essential and often substantial

A pedagogy of abundance, acknowledging that content often is freely available and abundant, may eventually prove relevant in this regard.

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).

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.

Daniel does a lot of great work

try to find connecting points

In the interest

am removed from the anchor text

rough take

to each other

reacting to ideas

It’s also invisible, to some degree.

Sifting

curate

The Trending algorithm is clearly not ready for prime time

the concept of the printed edition

la curation de mon profil,

entre curation et éditorialisation

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

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

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

Educators

distributed curation

some kind of curated library

long time curating these tomes

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?

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.

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.