This course teaches the design of contemporary information systems for biological and medical data. Examples are chosen from biology and medicine to illustrate complete life cycle information systems, beginning with data acquisition, following to data storage and finally to retrieval and analysis. Design of appropriate databases, client-server strategies, data interchange protocols, and computational modeling architectures. Students are expected to have some familiarity with scientific application software and a basic understanding of at least one contemporary programming language (e.g. C, C++, Java, Lisp, Perl, Python). A major term project is required of all students. This subject is open to motivated seniors having a strong interest in biomedical engineering and information system design with the ability to carry out a significant independent project.
This course was offered as part of the Singapore-MIT Alliance (SMA) program as course number SMA 5304.

•Understand what metadata is and how it relates to DCAT• Learn about the history and objectives of DCAT• Understand the basic model of DCAT• See some examples of DCAT entities

What is this book? This book is designed as a quick introduction to authoring metadata using basic elements from the Dublin Core Metadata Initiative.

Is Energy and GIS your passion? If so, Energy Industry Applications of GIS provides students with an in-depth exploration of the complexities of siting decisions in the electricity market. The course introduces a variety of siting challenges that confront the energy industry and its customers and neighbors but focuses on the siting of electrical transmission lines. The course also provides hands-on experience with a common decision support technology, ArcGIS, and considers how the technology may be used to facilitate public participation in siting decisions.

The LRMI specification is a collection of classes and properties for markup and description of educational resources. The specification builds on the extensive vocabulary provided by Schema.org and other standards. LRMI terms not included in schema.org may nevertheless be used to augment and enrich Schema.org markup.

This is a test resource - not intended to be used by NSDL users. Will be deleted as soon as possible

In order to keep our content consistent and discoverable, we are using the following shared keyword terms and subjects in our descriptions. Please use the values mapped below when describing authored or submitted content.

This content is an adaptation of slides from the OpenEd 2021 presentation by ISKME, VIVA, and LOUIS titled: "OER Discovery Research: Librarian and Faculty Curation Personas". The slides are based on research conducted by ISKME with funding from the Institute of Museum and Library Services (IMLS), grant number LG-246327-OLS-20.

This book is intended to give the non-expert an overview of standards and best practises related to publishing metadata about works. Its primary focus is metadata from cultural heritage institutions - i.e. GLAM institutions (galleries, libraries, archives and museums).

The book was started to help us get to grips with diverse collections of metadata which we were interested in using to figure out which works have entered the public domain in which different countries. At the OKF, we have been working on the developement of automated calculation to determine the public domain status of a work (see http://publicdomain.okfn.org/calculators), and we soon realized that we often do not have the necessary metadata to accurately determine whether or not a work is in the public domain. We have obtained data from different sources, e.g. BBC, British National Library, but we need to combine this data in meaningful ways in order to achieve a more comprehensive set of metadata. This required us to engage in the process of vocabulary alignment, removing duplicate entries, understanding whether similar fields actually mean the same thing, and figuring out whether different data models are compatible with each others.

With over 305,000 open educational resources cataloged on OER Commons since 2007, ISKME works to make learning and knowledge sharing more participatory, equitable, and open, in pursuit of a more just society.

Those resources don’t describe themselves, though. The metadata of every resource in OER Commons was put together by someone before it got added to our collection, and then a librarian at ISKME reviewed it for quality – and that’s a lot of work, both in and out of house!

How much work? Well, if a librarian were to spend just five minutes on each record that ever found its way into our collection, that would take 25,433 hours. That’s enough time to…

- do 123 round trips to the moon (time to finally take that leave you’ve been saving)

- get 3,178 full nights of sleep (unless you’re a cat, then it’s only 1,413)

- walk 8 times from Cape Town to Copenhagen (we’re gonna need a bigger passport)

- work full-time for over 13 years (don’t worry, that excludes 4 weeks vacation)

All of that to say, metadata takes time.

It can be a challenge to balance metadata creation with other tasks like maintaining existing records, curation work, and supporting educational partners with curation. As such, we’re always on the lookout for tools and techniques that boost our capacity without compromising quality.

In 2023 and 2024, we’re testing out how generative AI tools like large language models can support our work in the OER landscape. This poster highlights some of the places where we’ve had successes, along with possible future applications that we think are both useful and doable.

Data Carpentry Genomics workshop lesson to learn how to structure your metadata, organize and document your genomics data and bioinformatics workflow, and access data on the NCBI sequence read archive (SRA) database. Good data organization is the foundation of any research project. It not only sets you up well for an analysis, but it also makes it easier to come back to the project later and share with collaborators, including your most important collaborator - future you. Organizing a project that includes sequencing involves many components. There’s the experimental setup and conditions metadata, measurements of experimental parameters, sequencing preparation and sample information, the sequences themselves and the files and workflow of any bioinformatics analysis. So much of the information of a sequencing project is digital, and we need to keep track of our digital records in the same way we have a lab notebook and sample freezer. In this lesson, we’ll go through the project organization and documentation that will make an efficient bioinformatics workflow possible. Not only will this make you a more effective bioinformatics researcher, it also prepares your data and project for publication, as grant agencies and publishers increasingly require this information. In this lesson, we’ll be using data from a study of experimental evolution using E. coli. More information about this dataset is available here. In this study there are several types of files: Spreadsheet data from the experiment that tracks the strains and their phenotype over time Spreadsheet data with information on the samples that were sequenced - the names of the samples, how they were prepared and the sequencing conditions The sequence data Throughout the analysis, we’ll also generate files from the steps in the bioinformatics pipeline and documentation on the tools and parameters that we used. In this lesson you will learn: How to structure your metadata, tabular data and information about the experiment. The metadata is the information about the experiment and the samples you’re sequencing. How to prepare for, understand, organize and store the sequencing data that comes back from the sequencing center How to access and download publicly available data that may need to be used in your bioinformatics analysis The concepts of organizing the files and documenting the workflow of your bioinformatics analysis

Description
This lesson introduces students to working with metadata, which can be broadly thought of as the data ABOUT existing data. Data isn’t complete without metadata, and this lesson will help students understand both how to work with metadata and how to create their own.

Data used: NEON aquatic macroinvertebrate datasets from multiple stations. It could be adapted to use any data sets or taxonomic groups though.

Activities: The lesson involves three major activities. 1) Querying and downloading datasets and corresponding products from NEON. 2) Reading and answering comprehension questions about metadata files that correspond with data files 3) Combining two datasets based off understanding the metadata in exercise 2 (e.g. understanding which columns indicate sampling dates and in which formats will allow them to appropriately combine multiple data sets).

Programs: No specific programming skills or language is required for this lesson. This lesson is designed to be done entirely in common office/student software programs (e.g. Microsoft Word and Microsoft Office) and could be done using online programs (e.g. my university has student licenses for Google Spreadsheets and Google Docs).

Learning objectives:

1 – Students will be able to define ‘metadata’ and understand how metadata is critical for reproducible research.

2 – Students will be able to correctly answer comprehension questions about a metadata file.

3 – Students will be able to apply their understanding of the metadata file to create a new data file from two data sets.

4 – Students will understand the importance of creating and understanding metadata to go along with datasets.

Timing: This lesson was designed to take place in two – 75 minute class periods that are in a workshop format. This lesson could easily be part of a longer lab, homework, or a remote / online / asynchronous assignment.

Notes
This version is current as of Spring 2019 and was classroom taught. I encourage folks to adapt, modify, and make new versions.

Cite this work
Researchers should cite this work as follows:

Whitney, K. S. (2019). Introduction to Data Management and Metadata using NEON aquatic macroinvertebrate data. NEON Faculty Mentoring Network, QUBES Educational Resources. doi:10.25334/SJX1-F373

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Clinical research is crucial to improving medical treatment and healthcare. But with all the associated data spread across various information storehouses, it’s often unclear where the improvement process should begin. To a team of researchers from ECRIN, the European Clinical Research Infrastructure Network, it’s a classic metadata problem—an issue of cataloguing data about data. Their solution: a universal scheme for labeling clinical research data and documents. With this system, harvesting critical information could become much easier, regardless of where it’s located, vastly improving the speed with which researchers, reviewers, and clinicians alike can help translate clinical research into better healthcare. We tend to think that a research paper contains everything there is to know about a study. But it’s really only part of the story—especially in clinical settings..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

An animated presentation explaining the basic concept of a subject heading. Created in MS Powerpoint (version 2205), it can be edited to add your own content or branding. Can also be exported to video, see example here: https://www.youtube.com/watch?v=2HmkaMd_lgoScrIpt for narration is found in the notes section of each slide.