- Mar 2020
Asking for consent when processing users’ personal data is one of the most important duties imposed on website owners by the GDPR.
- Feb 2020
- Jan 2020
- Dec 2019
greater integration of data, data security, and data sharing through the establishment of a searchable database.
Would be great to connect these efforts with others who work on this from the data end, e.g. RDA as mentioned above.
Also, the presentation at http://www.gfbr.global/wp-content/uploads/2018/12/PG4-Alpha-Ahmadou-Diallo.pptx states
This data will be made available to the public and to scientific and humanitarian health communities to disseminate knowledge about the disease, support the expansion of research in West Africa, and improve patient care and future response to an outbreak.
but the notion of public access is not clearly articulated in the present article.
Does it have a name and online presence? The details provided here go beyond what's given in reference 13, but some more detail would still be useful, e.g. to connect the initiative to efforts directed at data management and curation more generally, for instance in the framework of the Research Data Alliance, https://www.rd-alliance.org/ .
- Apr 2019
- Nov 2017
An institution has implemented a learning management system (LMS). The LMS contains a learning object repository (LOR) that in some aspects is populated by all users across the world who use the same LMS. Each user is able to align his/her learning objects to the academic standards appropriate to that jurisdiction. Using CASE 1.0, the LMS is able to present the same learning objects to users in other jurisdictions while displaying the academic standards alignment for the other jurisdictions (associations).
- LMS (Learning Management System)
- Vitrine technologie-éducation
- Linked Open Data
- Competency-Based Education (CBE)
- Learning Object Repository
- Semantic Web
- Sep 2017
Over the course of many years, every school has refined and perfected the connections LMSs have into a wide variety of other campus systems including authentication systems, identity management systems, student information systems, assessment-related learning tools, library systems, digital textbook systems, and other content repositories. APIs and standards have decreased the complexity of supporting these connections, and over time it has become easier and more common to connect LMSs to – in some cases – several dozen or more other systems. This level of integration gives LMSs much more utility than they have out of the box – and also more “stickiness” that causes them to become harder to move away from. For LMS alternatives, achieving this same level of connectedness, particularly considering how brittle these connections can sometimes become over time, is a very difficult thing to achieve.
- Aug 2017
This has much in common with a customer relationship management system and facilitates the workflow around interventions as well as various visualisations. It’s unclear how the at risk metric is calculated but a more sophisticated predictive analytics engine might help in this regard.
Have yet to notice much discussion of the relationships between SIS (Student Information Systems), CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), and LMS (Learning Management Systems).
- Learning Analytics
- SIS (Student Information System)
- Learner Data
- ERP (Enterprise Resource Planning)
- LMS (Learning Management System)
- Ellucian Banner
- CRM (Customer Relationship Management)
- Jun 2015
If you can’t find the correct web page, ask a reference librarian.
YES, ASK US. Also, we love to work with faculty on managing their data!
- Jan 2014
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".
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.
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.
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:
- data curation
- tape backup
- data entropy
- key component
- data management
- 20-year rule
- data sharing
- sharing standards
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:
- 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
- assigning permanent identifiers to data
- creating/publishing descriptions of data
- capturing computational provenance
- identifying funding sources for curation support
- creating data management plans at proposal time