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Example: a poster explicating a concept.": 5, "Service - self-contained unit of functionality that enables operations or capabilities, e.g., searching for instruction or training resources on data management.": 3, "Software - programs used to direct the operation of a computer and related devices.": 2, "Physical Object - entity that has a physical presence in the present, as opposed to an artifact (a physical object) of the past.": 1, "Sound - an explanation of a concept or a story using audio means within the acoustic range available to humans, and differentiated from audio visual resources (i.e.,moving image).": 1 }, "access_cost": { "false": 543, "true": 2 }, "status": { "true": 544, "false": 1 }, "pub_status": { "published": 545, "in-process": 0 } }, "results": [ { "id": "93a7b9ae-659d-46bd-acfa-86c79a968868", "pub_status": "published", "status": 0, "rating": 0.0, "ratings": [], "creator": "Nhoebelheinrich", "created": "2024-01-25T09:12:50Z", "submitter_email": "nhoebel@kmotifs.com", "submitter_name": "Nhoebelheinrich", "title": "Databases and Data Management", "url": "https://ucsb-library-research-data-services.github.io/bren-meds213-spring-2023/", "access_cost": "false", "authors": [ { "givenName": "Greg", "familyName": "Jan\u00e9e", "name_identifier": "", "name_identifier_type": "n/a" }, { "givenName": "Renata", "familyName": "Curty", "name_identifier": "", "name_identifier_type": "n/a" } ], "author_org": { "name": "University of California,Santa Barbara", "name_identifier": "https://ror.org/02t274463", "name_identifier_type": "ROR" }, "contact": { "name": "Greg Janee", "org": "University of California, Santa Barbara", "email": "gjanee@ucsb.edu" }, "abstract_data": "This is an archive of the materials used for a 4-unit, letter-grade course delivered in Spring 2023 as part of the Master of Environmental Data Science (MEDS) program in the Bren School of Environmental Science & Management. It includes PowerPoint presentations, instructor notes, live coding transcripts, supplemental materials and readings, and homework assignments. The goals of the course were to give MEDS students the skills they need to practically, successfully, and ethically manage their data, and to create, manage, and use relational databases where appropriate. Relational database topics went farther than just SQL queries and included a significant unit on data modeling and database constraints and integrity, in addition to advanced database topics such as triggers and indexes and accessing databases from programming environments. The data management portion tied into the students\u2019 capstone projects in a couple places, and included analyzing data from an ethical perspective, creating standards-compliant metadata, and employing data de-identification techniques. The course also included a unit on the Unix command line, with an emphasis on creating reusable Bash scripts, given in the spirit that Bash is a generally useful tool that all data scientists should have at least some familiarity with.", "subject": [ "Physical Sciences and Mathematics: Environmental Sciences" ], "keywords": [ "Accessible data - FAIR Data Principle", "Data documentation", "Data ethics", "Data management", "Data modeling", "Data privacy", "Data publication", "Database design", "Database management", "Metadata", "Metadata editors", "Metadata standards", "Open data", "Programming", "Python", "R software", "Relational database", "Restricted data", "Structured Query Language (SQL)", "Unix" ], "license": "Creative Commons Attribution-NonCommercial 4.0 International - CC BY-NC 4.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "University of California,Santa Barbara", "accesibility_features": [], "accesibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Graduate student" ], "purpose": "Instruction - detailed information about aspects or processes related to data management or data skills.", "completion_time": "More than 1 Week", "media_type": "Collection - a group or set of items that comprise a single learning resource, e.g., a PDF version of a slide presentation, an audio file of the presentation and a textual representation of the oral transcription of the presentation.", "lr_type": "Unit - long-range plan of instruction on a particular concept containing multiple, related lessons.", "country_of_origin": "US", "resource_modification_date": "2023-12-21", "contributors": [], "contributor_orgs": [], "modification_date": "2024-01-25T09:14:59Z", "published": "2024-01-25T09:12:50Z", "score": 1.3269914, "abstract_format": null, "accessibility_features": {}, "accessibility_summary": null, "author_names": [], "credential_status": null, "md_record_id": null, "notes": [], "version": null }, { "title": "Introducci\u00f3n a Topic Modeling y MALLET", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:59:03Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://programminghistorian.org/es/lecciones/topic-modeling-y-mallet", "access_cost": 0, "submitter_name": "Nancy J Hoebelheinrich", "submitter_email": "njhoebel@gmail.com", "authors": [ { "givenName": "Shawn", "familyName": "Graham", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Scott", "familyName": "Weingart", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Ian", "familyName": "Milligan", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Shawn Graham", "Scott Weingart", "Ian Milligan" ], "author_org": { "name": "The Programming Historian", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "En esta lección, primero aprenderás qué es topic modeling1 y por qué podrías querer utilizarlo en tus investigaciones. Luego aprenderás cómo instalar y trabajar con MALLET, una caja de herramientas para procesamiento de lenguajes naturales (PLN) que sirve para realizar este tipo de análisis. MALLET requiere que se modifique una variable de entorno (esto es, configurar un atajo para que la computadora sepa en todo momento dónde encontrar el programa MALLET) y que se trabaje con la línea de comandos (es decir, tecleando comandos manualmente en vez de hacer clic en íconos o menús).", "abstract_format": "filtered_html", "subject": "Arts and Humanities", "keywords": [ "Data access methods", "Data modeling", "Data skills education", "Digital humanities", "Humanities data", "Programming", "Software management" ], "license": "", "usage_info": "", "citation": "Shawn Graham, Scott Weingart, y Ian Milligan, \"Introducci\u00f3n a Topic Modeling y MALLET\", traducido por Ulrike Henny-Krahmer, The Programming Historian en espa\u00f1ol 2 (2018), https://doi.org/10.46430/phes0040.", "locator_data": "10.46430/phes0040", "locator_type": "DOI", "publisher": "The Programming Historian", "version": "", "created": "2021-06-01T17:00:29", "published": "2012-09-02T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "es", "languages_secondary": [ "en" ], "ed_frameworks": [], "target_audience": [ "Early-career research scientist", "Graduate student", "Librarian", "Mid-career research scientist", "Research faculty", "Research scientist", "Software engineer", "Technology expert group", "Undergraduate student" ], "purpose": "Instruction - detailed information about aspects or processes related to data management or data skills.", "completion_time": "More than 1 hour (but less than 1 day)", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "Nhoebelheinrich", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "af3793fd-2008-315f-9f10-4a79adef3b9f", "contributors": [], "contributor_orgs": [], "score": 1.3269914, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "EC FAIR How-to Series: Getting a DOI for Your Data", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:59:03Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://library.ucsd.edu/dc/object/bb10665684", "access_cost": 0, "submitter_name": "Nancy J Hoebelheinrich", "submitter_email": "njhoebel@gmail.com", "authors": [ { "givenName": "Nancy J.", "familyName": "Hoebelheinrich", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Nancy J. Hoebelheinrich" ], "author_org": { "name": "Knowledge Motifs LLC", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Nancy J. Hoebelheinrich", "org": "Knowledge Motifs LLC", "email": "" }, "abstract_data": "Identifiers are very important as a means to make research data more FAIR (Findable Accessible Interoperable Reusable). This quick reference guide briefly describes why, when, where and how to acquire a digital object identifier (DOI) for research data.    The guide is targeted to Earth Science researchers, but should be useful for other researchers and their support staff as well.  It is part of the EarthCub How-to Series that is designed to provide targeted, practical lessons on making research data FAIR.  ", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Accessible data - FAIR Data Principle", "Findable data - FAIR Data Principle", "Persistent Identifiers (PID)" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "Hoebelheinrich, Nancy J. (2021). EC FAIR How-to Series: Getting a DOI for Your Data. 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Example: a poster explicating a concept.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "Nhoebelheinrich", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "91d35f96-d883-342a-a3e9-442fda2abff8", "contributors": [], "contributor_orgs": [ { "name": "EarthCube", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" } ], "score": 1.3269914, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "EarthCube FAIR How-to Series: Choosing a Repository for Your Data", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:59:03Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://library.ucsd.edu/dc/object/bb74830103", "access_cost": 0, "submitter_name": "Nancy J. Hoebelheinrich", "submitter_email": "nhoebel@kmotifs.con", "authors": [ { "givenName": "Nancy J.", "familyName": "Hoebelheinrich", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Nancy J. Hoebelheinrich" ], "author_org": { "name": "Knowledge Motifs LLC", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Nancy J. Hoebelheinrich", "org": "Knowledge Motifs LLC", "email": "" }, "abstract_data": "This quick reference guide briefly describes why, when, where and how to choose a FAIR-enabled data repository for research data.    The guide is targeted to Earth Science researchers, but should be useful for other researchers and their support staff as well.  It is part of the EarthCub How-to Series that is designed to provide targeted, practical lessons on making research data FAIR.  ", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Data reuse - Core Trustworthy Data Repositories Requirements", "Data storage" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "Hoebelheinrich, Nancy J. (2021). EC FAIR How-to Series: Choosing a Repository for Your Data. In EarthCube Organization Materials. UC San Diego Library Digital Collections. https://doi.org/10.6075/J02J6BQD", "locator_data": "10.6075/J02J6BQD", "locator_type": "DOI", "publisher": "EarthCube", "version": "", "created": "2021-11-06T13:11:47", "published": "2021-05-11T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [ { "name": "FAIR Data Principles", "nodes": [ { "description": "", "name": "Accessible" }, { "description": "", "name": "Findable" }, { "description": "", "name": "Re-usable" } ], "type": "framework" } ], "target_audience": [ "Early-career research scientist", "Mid-career research scientist" ], "purpose": "Instruction - detailed information about aspects or processes related to data management or data skills.", "completion_time": "Up to 1 hour", "media_type": "Static Image - a single visual representation that does not change. Example: a poster explicating a concept.", "lr_type": "Images / Visuals - media, including but not limited to pictures, graphics, diagrams, figures, illustrations, charts, and maps.", "creator": "Nhoebelheinrich", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "d57952b5-fc5a-3045-86bc-d93c5acb75fb", "contributors": [], "contributor_orgs": [ { "name": "EarthCube", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" } ], "score": 1.3269914, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Biological Observation Data Standardization - A Primer for Data Managers", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:59:03Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://doi.org/10.6084/m9.figshare.16806712.v1", "access_cost": 0, "submitter_name": "Stace Beaulieu", "submitter_email": "stace@whoi.edu", "authors": [ { "givenName": "Abigail", "familyName": "Benson", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Diana", "familyName": "LaScala-Gruenewald", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Robert", "familyName": "McGuinn", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Erin", "familyName": "Satterthwaite", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Stace", "familyName": "Beaulieu", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Mathew", "familyName": "Biddle", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Lynn", "familyName": "deWitt", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Megan", "familyName": "McKinzie", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Enrique", "familyName": "Montes", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Hassan", "familyName": "Moustahfid", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Frank", "familyName": "Muller-Karger", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Tylar", "familyName": "Murray", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Anton", "familyName": "Van de Putte", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Abigail Benson", "Diana LaScala-Gruenewald", "Robert McGuinn", "Erin Satterthwaite", "Stace Beaulieu", "Mathew Biddle", "Lynn deWitt", "Megan McKinzie", "Enrique Montes", "Hassan Moustahfid", "Frank Muller-Karger", "Tylar Murray", "Anton Van de Putte" ], "author_org": { "name": "", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "esip-biodatastandards@lists.esipfed.org", "email": "" }, "abstract_data": "Lots of standards exist for use with biological data but navigating them can be difficult for data managers who are new to them. The Earth Science Information Partners (ESIP) Biological Data Standards Cluster developed this primer for managers of biological data to provide a quick, easy resource for navigating a selection of the standards that exist. The goal of the primer is to spread awareness about existing standards and is intended to be shared online and at conferences to increase the adoption of standards for biological data and make them FAIR.", "abstract_format": "filtered_html", "subject": "Life Sciences", "keywords": [ "Agriculture applications", "Biodiversity data", "Biological data", "Community standards", "Data formats", "Data sharing", "Data stewardship", "Earth observation data", "FAIR metrics" ], "license": "Creative Commons 0 - CC0 \"No Rights Reserved\" (Public Domain)", "usage_info": "", "citation": "Benson, Abigail; LaScala-Gruenewald, Diana; McGuinn, Robert; Satterthwaite, Erin; Beaulieu, Stace; Biddle, Mathew; et al. (2021): Biological Observation Data Standardization - A Primer for Data Managers. ESIP. Online resource. https://doi.org/10.6084/m9.figshare.16806712.v1", "locator_data": "10.6084/m9.figshare.16806712.v1", "locator_type": "DOI", "publisher": "", "version": "", "created": "2021-11-08T10:11:26", "published": "2021-10-19T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [ { "name": "FAIR Data Principles", "nodes": [ { "description": "", "name": "Accessible" }, { "description": "", "name": "Findable" }, { "description": "", "name": "Interoperable" }, { "description": "", "name": "Re-usable" } ], "type": "framework" } ], "target_audience": [ "Data manager", "High school student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "Up to 1 hour", "media_type": "Static Image - a single visual representation that does not change. Example: a poster explicating a concept.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "sbeaulieu", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "05e285b9-4266-3842-8bc3-2e7739203f4e", "contributors": [], "contributor_orgs": [ { "name": "ESIP Biological Data Standards Cluster", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Conceptualization" } ], "score": 1.3269914, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Supporting Researchers in Discovering Data Repositories", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:59:00Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://www.dataone.org/webinars/repo-discovery/", "access_cost": 0, "submitter_name": "Zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Amber", "familyName": "Budden", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Erin", "familyName": "McLean", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Amber Budden", "Erin McLean" ], "author_org": { "name": "", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "How do researchers go about identifying a repository to preserve their data? Do they have all the information they need to make an informed decision? Are there resources available to help?
\r\nThere is a myriad of repositories available to support data preservation and they differ across multiple axes. So which one is right for your data? The answer is large, ‘it depends’. But this can be frustrating to a new researcher looking to publish data for the first time. What questions need to be asked to detangle these dependencies and where can a researcher go for answers?
\r\nConversations and sessions at domain conferences have consistently suggested that researchers need more support in navigating the landscape of data repositories and with support from ESIP Funding Friday, we sought to do that. In this webinar, we will introduce a resource under development that aims to serve as a gateway for information about repository selection. With links to existing resources, games, and outreach materials, we aim to facilitate the discovery of data repositories and we welcome contributions to increase the value of this resource.", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Data discovery and identification - Core Trustworthy Data Repositories Requirements", "Data management", "Data preservation", "Data reuse - Core Trustworthy Data Repositories Requirements", "Data storage" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "DataONE", "version": "", "created": "2021-02-22T11:35:35", "published": "2021-01-12T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Early-career research scientist", "Graduate student", "Librarian", "Publisher", "Repository manager", "Research scientist", "Undergraduate student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "Up to 1 hour", "media_type": "Event - time-based happening that is portrayed or covered by the learning resource, e.g., a webinar.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "ca9d6303-95de-3c60-a19d-66b607844873", "contributors": [], "contributor_orgs": [], "score": 1.3269914, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "RDM Onboarding Checklist", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:59Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://datamanagement.hms.harvard.edu/plan/rdm-onboarding-checklist", "access_cost": 0, "submitter_name": "Zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Julie", "familyName": "Goldman", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Sarah", "familyName": "Hauserman", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Meghan", "familyName": "Kerr", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Julie Goldman", "Sarah Hauserman", "Meghan Kerr" ], "author_org": { "name": "Harvard Medical School", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "Research Data Management is essential for responsible research and should be introduced when starting a new project or joining a new lab. Managing data across a project and/ or a team allows for accurate communication about that project. This session will review the important steps for onboarding new employees/trainees to a lab or new projects. The key takeaway from this session will be how to incorporate these steps within your individual project or lab environment. While the principles are general, these documents focus on Harvard policies and resources. Internal and external links have been provided throughout the document as supplementary resources, including a glossary of terms. 
\r\n
\r\nThere are 2 checklists as follow: 
\r\nThe RDM Onboarding Checklist: Abridged Version serves as a condensed version of the comprehensive checklist described above. This version is intended to be used as an actionable checklist, employed after reviewing the onboarding processes and resources provided in the comprehensive checklist.
\r\nThe RDM Onboarding Checklist: Comprehensive Version serves as a general, research data management-focused guide to employee/trainee onboarding as they join a new lab or begin new projects (follow one or both of these as they apply to your situation). This comprehensive version is provided as an initial introduction to the onboarding process and to the breadth of available resources; this version is intended to be reviewed first, prior to utilizing the abridged version.
\r\n
\r\n Learning Objectives:\r\n\r\n", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Data backup", "Data communication", "Data management", "Data management planning", "Data policy", "Data publication", "Data research", "Data sharing", "Data storage", "File naming", "Metadata standards", "Research lifecycle", "Researcher engagement", "Version control" ], "license": "Standard YouTube License", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "Harvard Medical School", "version": "", "created": "2021-02-08T15:28:31", "published": "2020-09-14T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data professional", "Early-career research scientist", "Funding organization", "Librarian", "Mid-career research scientist", "Repository manager", "Research faculty", "Research scientist" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "Up to 1 hour", "media_type": "Collection - a group or set of items that comprise a single learning resource, e.g., a PDF version of a slide presentation, an audio file of the presentation and a textual representation of the oral transcription of the presentation.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "ea45507f-2c11-3f2d-b800-74d3f9bdd89a", "contributors": [], "contributor_orgs": [], "score": 1.3269914, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "What we wish we had learned in Graduate School - a data management training roadmap for graduate students", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:56Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://doi.org/10.6084/m9.figshare.14384456.v1", "access_cost": 0, "submitter_name": "zohreh mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Ellie", "familyName": "Davis Pierel", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Ben", "familyName": "Roberts-Pierel", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Yuhan", "familyName": "Rao", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Ellie Davis Pierel", "Ben Roberts-Pierel", "Yuhan Rao" ], "author_org": { "name": "", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "
This Road Map is a dynamic guide to help graduate students wade through the ocean of data resources. This guide will help identify what data management practices graduate students should be considering at different graduate school milestones.
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\r\nData management training for graduate students is a very important but often undervalued area of graduate school education. Many graduate students will go on and become professionals who are using, producing, and/or managing data that have tremendous benefits for both the research community and society. However, our personal experiences as graduate students show that data lifecycle and data management training are not part of the core curriculum in graduate school. As Earth Science Information Partners (ESIP) Community Fellows, we understand that data management is a critical skill in earth science and we all wished we had an opportunity to integrate it from the beginning in our graduate school experience. To the issue of lack of formal data management training in graduate education, we convened a working session during the 2020 ESIP Summer Meeting called “What we wish we had learned in Graduate School?” The session was initially planned as a working session for early career professionals to share resources and lessons learned during our own graduate school experiences. The session has sparked broad interests from the Earth science data community and attracted participants across different career stages and with different levels of expertise. The outcome of the session has been summarized as a roadmap that follows the DataONE Data Lifecycle. This roadmap projects the data lifecycle into the traditional graduate school timeline and highlights the benefits and resources of data management training for each component in the data lifecycle. This roadmap for graduate data management training will be distributed via ESIP and be continued as part of the ESIP Community Program in the future to promote data management training for graduate students in Earth sciences and beyond.
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\r\nAlso available as a webinar from DataONE:  https://vimeo.com/481534921 
\r\n", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Data collection", "Data lifecycle", "Data management" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "Roberts-Pierel, Ben; Davis, Eleanor; Rao, Yuhan (2021): A Graduate Student's Road Map to Data Management Training. ESIP. Educational resource. https://doi.org/10.6084/m9.figshare.14384456.v1 ", "locator_data": "10.6084/m9.figshare.14384456.v1", "locator_type": "DOI", "publisher": "Earth Science Information Partners (ESIP)", "version": "", "created": "2020-11-25T10:13:03", "published": "2021-07-04T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Data manager", "Educator", "Graduate student", "Librarian" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "Up to 1 hour", "media_type": "Presentation - representation of the particular way in which an author shows, describes or explains one or more concepts, e.g., a set of Powerpoint slides.", "lr_type": "Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "dd70d3a6-8313-3fab-87cd-4131fa7b2da0", "contributors": [], "contributor_orgs": [ { "name": "DataONE", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Collaborator" } ], "score": 1.3269914, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Train-the-Trainer Concept on Research Data Management", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:55Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://doi.org/10.5281/zenodo.4071471", "access_cost": 0, "submitter_name": "Katarzyna Biernacka", "submitter_email": "biernack@hu-berlin.de", "authors": [ { "givenName": "Katarzyn", "familyName": "Biernacka", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Maik", "familyName": "Bierwirth (Dr.)", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Petra", "familyName": "Buchholz", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Dominika", "familyName": "Dolzycka", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Kerstin", "familyName": "Helbig", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Janna", "familyName": "Neumann (Dr.)", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Carolin", "familyName": "Odebrecht (Dr.)", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Cord", "familyName": "Wiljes", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Ulrike", "familyName": "Wuttke (Dr.)", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Katarzyn Biernacka", "Maik Bierwirth (Dr.)", "Petra Buchholz", "Dominika Dolzycka", "Kerstin Helbig", "Janna Neumann (Dr.)", "Carolin Odebrecht (Dr.)", "Cord Wiljes", "Ulrike Wuttke (Dr.)" ], "author_org": { "name": "", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "
Within the project\u00a0FDMentor, a German Train-the-Trainer Programme on Research Data Management (RDM) was developed and piloted in a series of workshops. The topics cover many aspects of research data management, such as data management plans and the publication of research data, as well as didactic units on learning concepts, workshop design, and a range of didactic methods.
\nAfter the end of the project, the concept was supplemented and updated by members of the Sub-Working Group Training/Further Education (UAG Schulungen/Fortbildungen) of the DINI/Nestor Working Group Research Data (DINI/Nestor-AG Forschungsdaten). The newly published English version of the Train-the-Trainer Concept contains the translated concept, the materials, and all methods of the Train-the-Trainer Programme. Furthermore, additional English references and materials complement this version.
\nThis document is primarily intended for trainers who want to conduct a Train-the-Trainer workshop on research data management. It contains background knowledge on the PowerPoint slides and teaching scripts as well as further information on the individual subject areas required for reuse and implementation of a two-day workshop of seven and a half hours a day.
\n
\nEach unit of this guide contains information about how to teach the unit including\u00a0the unit's learning objectives, key aspects, contents, didactic methods and exercises, training materials, addiitional sources, template, and teaching scripts.
\n
\nTopics of the units inlclude orientation, didactic approach, digital research data, research data policies, data management plans, order and structure, documentation and metadata, storage and backup, long term archiving, access control, formal framework, data publication, re-use of research data, legal aspects, institutional infrastructure, training exercises, concept development and didactic methods.\u00a0\u00a0
\n\u00a0
\n", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Access rights", "Data analysis", "Data archiving", "Data documentation", "Data management", "Data management planning", "Data policy", "Data preservation", "Data publication", "Data reuse - Core Trustworthy Data Repositories Requirements", "Data storage", "Metadata" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "Biernacka, Katarzyna, Bierwirth, Maik, Buchholz, Petra, Dolzycka, Dominika, Helbig, Kerstin, Neumann, Janna, \u0085 Wuttke, Ulrike. (2020). Train-the-Trainer Concept on Research Data Management (Version 3.0). Zenodo. https://doi.org/10.5281/zenodo.4071471", "locator_data": "10.5281/zenodo.4071471", "locator_type": "DOI", "publisher": "Zenodo", "version": "3.0", "created": "2020-11-09T10:58:54", "published": "2020-11-04T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "de", "languages_secondary": [ "en" ], "ed_frameworks": [], "target_audience": [ "Data manager", "Data professional", "Educator", "Graduate student", "Librarian", "Publisher", "Repository manager" ], "purpose": "Instruction - detailed information about aspects or processes related to data management or data skills.", "completion_time": "More than 1 Week", "media_type": "Text - an explanation of a concept or a story using human readable characters formed into words, usually distinguished from graphical images.", "lr_type": "Educator Curriculum Guide - document that defines what concepts should be taught and/or how a concept should be taught effectively.", "creator": "", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "dd15cd74-3413-3827-a0f9-d96e03960c2f", "contributors": [ { "familyName": "Bierwirth (Dr.)", "givenName": "Maik", "type": "Translation" } ], "contributor_orgs": [ { "name": "Federal Ministry of Education and Research (BMBF)", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" } ], "score": 1.3269914, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Content-based Identifiers for Iterative Forecasts: A Proposal", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:55Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://www.dataone.org/webinars/iterative-forecasts/", "access_cost": 0, "submitter_name": "zohreh Mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Carl", "familyName": "Boettiger", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Carl Boettiger" ], "author_org": { "name": "University of California, Berkeley", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "Iterative forecasts pose particular challenges for archival data storage and retrieval. In an iterative forecast, data about the past and present must be downloaded and fed into an algorithm that will output a forecast data product. Previous forecasts must also be scored against the realized values in the latest observations. Content-based identifiers provide a convenient way to consistently identify input and outputs and associated scripts. These identifiers are:
\r\n(1) location-agnostic – they don’t depend on a URL or other location-based authority (like DOI)
\r\n(2) reproducible – the same data file always has the same identifier
\r\n(3) frictionless – cheap and easy to generate with widely available software, no authentication or network connection
\r\n(4) sticky – the identifier cannot become unstuck or separated from the content
\r\n(5) compatible – most existing infrastructure, including DataONE, can quite readily use these identifiers.
\r\n
\r\nIn this webinar, the speaker will illustrate an example iterative forecasting workflow. In the process, he will highlight some newly developed R packages for making this easier.", "abstract_format": "filtered_html", "subject": "Physical Sciences and Mathematics: Environmental Sciences", "keywords": [ "Data archiving", "Data skills education", "Data storage", "Persistent Identifiers (PID)", "Programming", "R software" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "", "locator_data": "", "locator_type": "", "publisher": "DataONE", "version": "", "created": "2020-11-12T10:35:13", "published": "2020-10-13T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Data professional", "Data supporter", "Early-career research scientist", "Graduate student", "Librarian", "Mid-career research scientist", "Repository manager", "Research scientist", "Undergraduate student" ], "purpose": "Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.", "completion_time": "Up to 1 hour", "media_type": "Interactive Resource - requires a user to take action or make a request in order for the content to be understood, executed or experienced.", "lr_type": "Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.", "creator": "zmehrabi", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "38de3dde-104f-3cff-a0a9-7036e7c53cb9", "contributors": [], "contributor_orgs": [], "score": 1.3269914, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] } ] }