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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. 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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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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. 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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. 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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.
\r\n
\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.
\r\n
\r\nAlso available as a webinar from DataONE:  https://vimeo.com/481534921 
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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": 1.3688016, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Data Management and Reporting: BCO-DMO Data Management Services and Best Practices", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:47Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://darchive.mblwhoilibrary.org/handle/1912/24614", "access_cost": 0, "submitter_name": "zohreh mehrabi", "submitter_email": "zmehrabi@unm.edu", "authors": [ { "givenName": "Shannon", "familyName": "Rauch", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Danie", "familyName": "Kinkade", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Matt", "familyName": "Biddle", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Nancy", "familyName": "Copley", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Amber", "familyName": "York", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Karen", "familyName": "Soenen", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Adam", "familyName": "Shepherd", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Shannon Rauch", "Danie Kinkade", "Matt Biddle", "Nancy Copley", "Amber York", "Karen Soenen", "Adam Shepherd" ], "author_org": { "name": "", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "The University-National Oceanographic Laboratory System (UNOLS) hosted an Early Career Chief Scientist Training Workshop in June 2019. The goal of this workshop was to help early-career marine scientists plan and write effective cruise proposals, develop collaborative sampling strategies and plans, become familiar with shipboard equipment and sampling at sea, and communicate major findings through the writing of manuscripts and cruise reports. This presentation provides information on data management and reporting best practices for chief scientists. It includes information on the National Science Foundation (NSF) data policy requirements, writing a Data Management Plan (DMP), the data lifecycle, data publication, and shipboard data management recommendations.", "abstract_format": "filtered_html", "subject": "Physical Sciences and Mathematics: Environmental Sciences", "keywords": [ "Data access", "Data discovery and identification - Core Trustworthy Data Repositories Requirements", "Data lifecycle", "Data management examples", "Data management stories", "Data policy", "Data preservation", "Data publication", "Data storage", "Marine data" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "Presentation: Rauch, Shannon, Kinkade, Danie, Biddle, Matt, Copley, Nancy, York, Amber, Soenen, Karen, Shepherd, Adam, \"Data Management and Reporting: BCO-DMO Data Management Services and Best Practices\", Presented at Early Career Chief Scientist Training Workshop, Honolulu, HI, 13 June - 14 June 2019, DOI:10.1575/1912/24614, https://hdl.handle.net/1912/24614", "locator_data": "10.1575/1912/24614", "locator_type": "DOI", "publisher": "Woods Hole Scientific Community (WHOS)", "version": "", "created": "2020-10-01T10:41:32", "published": "2019-06-14T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Citizen scientist", "Data manager", "Early-career research scientist", "Graduate student", "Mid-career research scientist", "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": "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": "44cdf712-4366-3a5f-a7a3-75640639acee", "contributors": [], "contributor_orgs": [ { "name": "University-National Oceanographic Laboratory System (UNOLS)", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Organizer" }, { "name": "National Science Foundation (NSF)", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" } ], "score": 1.3688016, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "The Paper and The Data: Authors, Reviewers, and Editors Webinar on Updated Journal Practices for Data (and Software)", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:47Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://doi.org/10.5281/zenodo.3744660", "access_cost": 0, "submitter_name": "Shelley Stall", "submitter_email": "sstall@agu.org", "authors": [ { "givenName": "Shelley", "familyName": "Stall", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Randy", "familyName": "Townsend", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Erin", "familyName": "Robinson", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Shelley Stall", "Randy Townsend", "Erin Robinson" ], "author_org": { "name": "", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "Shelley Stall", "org": "American Geophysical Union (AGU)", "email": "" }, "abstract_data": "The Paper and The Data workshop was first presented at the Ocean Science meeting held in February 2020. \u00a0Following that conference it was updated and presented as five recorded modules for the purpose of sharing broadly. The workshop consists of 5 modules on topics related to the new practices of publishers for journals for the improvement of data and software sharing that are targeted to journal authors, reviewers, and editors.\u00a0 Both slides and video presentations of the slides are available.\u00a0 Modules include:
\n
\nModule 1: Introduction\u00a0 \n \n
\nModule 2: Data \n \nModule 3: Software\u00a0 \n \n \nModule 4: Peer Review\u00a0 \n \n \n
\nModule 5: Persistent Identifiers\u00a0 \n \n", "abstract_format": "filtered_html", "subject": "", "keywords": [ "Data citation", "Data sharing", "Digital scholarship", "Scholarly publishing", "Software citation", "Software sharing" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "Stall, Shelley, Townsend, Randy, & Robinson, Erin. (2020, April). The Paper and The Data: Authors, Reviewers, and Editors Webinar on Updated Journal Practices for Data (and Software). Zenodo. http://doi.org/10.5281/zenodo.3744660", "locator_data": "10.5281/zenodo.3744660", "locator_type": "DOI", "publisher": "Zenodo", "version": "", "created": "2020-10-07T12:01:39", "published": "2020-04-02T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Funding organization", "Librarian", "Publisher", "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": "More than 1 hour (but less than 1 day)", "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": "Nhoebelheinrich", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "82ae76e0-fcce-3c23-b07d-08940925d055", "contributors": [ { "familyName": "Brewer", "givenName": "Peter", "type": "Collaborator" }, { "familyName": "Gries", "givenName": "Corinna", "type": "Collaborator" }, { "familyName": "Smith", "givenName": "Colin", "type": "Collaborator" }, { "familyName": "Allen", "givenName": "Alice", "type": "Collaborator" }, { "familyName": "Fenner", "givenName": "Martin", "type": "Collaborator" } ], "contributor_orgs": [ { "name": "Belmont Forum", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" } ], "score": 1.3688016, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Data Science Training Camp at Woods Hole Oceanographic Institution: Syllabus and slide presentations in 2020", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:43Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://hdl.handle.net/1912/26103", "access_cost": 0, "submitter_name": "Stace Beaulieu", "submitter_email": "sbeaulieu@whoi.edu", "authors": [ { "givenName": "Stace E.", "familyName": "Beaulieu", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Lisa", "familyName": "Raymond", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Audrey", "familyName": "Mickle", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Joe", "familyName": "Futrelle", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Nick", "familyName": "Symmonds", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Roberta", "familyName": "Mazzoli", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Rich", "familyName": "Brey", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Danie", "familyName": "Kinkade", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Shannon", "familyName": "Rauch", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "Stace E. Beaulieu", "Lisa Raymond", "Audrey Mickle", "Joe Futrelle", "Nick Symmonds", "Roberta Mazzoli", "Rich Brey", "Danie Kinkade", "Shannon Rauch" ], "author_org": { "name": "", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "With data and software increasingly recognized as scholarly research products, and aiming towards open science and reproducibility, it is imperative for today's oceanographers to learn foundational practices and skills for data management and research computing, as well as practices specific to the ocean sciences. This educational package was developed as a data science training camp for graduate students and professionals in the ocean sciences and implemented at the Woods Hole Oceanographic Institution (WHOI) in 2019 and 2020. Here we provide materials for the 2020 camp.  Contents of this package include the syllabus and slide presentations for each of the four modules:
\r\n1 "Good enough practices in scientific computing,"
\r\n2 Data management,
\r\n3 Software development and research computing,
\r\nand 4 Best practices in the ocean sciences.
\r\nThe 3rd module is split into two parts. We also include a poster presented at the 2020 Ocean Science Meeting, which has some results from pre- and post-surveys.
\r\n ", "abstract_format": "filtered_html", "subject": "Physical Sciences and Mathematics: Earth Sciences", "keywords": [ "Big data", "Coastal data", "Data citation", "Data management", "Data sharing", "Ocean data", "Open science", "Programming", "Scientific reproducibility", "Software management" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "Beaulieu, Stace E., Raymond, Lisa, Mickle, Audrey, Futrelle, Joe, Symmonds, Nick, Mazzoli, Roberta, Brey, Rich, Kinkade, Danie, Rauch, Shannon, \"Data Science Training Camp at Woods Hole Oceanographic Institution: Syllabus and slide presentations in 2020\", Presented at Data Science Training Camp, Woods Hole, MA, January, 22 - 23, 2020., DOI:10.1575/1912/26103, https://hdl.handle.net/1912/26103", "locator_data": "10.1575/1912/26103", "locator_type": "DOI", "publisher": "Woods Hole Scientific Community (WHOS)", "version": "", "created": "2020-09-09T15:20:28", "published": "2020-08-21T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Data professional", "Early-career research scientist", "Graduate student", "Librarian", "Mid-career research scientist" ], "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": "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": "Nhoebelheinrich", "md_record_id": "", "ratings": [], "rating": 0.0, "id": "f1a11daf-e19a-3854-9517-e6540a3b5cda", "contributors": [], "contributor_orgs": [ { "name": "Woods Hole Oceanographic Institution (WHOI)", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" }, { "name": "National Science Foundation (NSF)", "name_identifier": "N.A.", "name_identifier_type": "N.A.", "type": "Funding and sponsorship" } ], "score": 1.3688016, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] }, { "title": "Exploring and Analyzing Network Data with Python", "status": 1, "pub_status": "published", "modification_date": "2022-06-21T10:58:11Z", "resource_modification_date": "1900-01-01T00:00:00Z", "url": "https://programminghistorian.org/en/lessons/exploring-and-analyzing-network-data-with-python", "access_cost": 0, "submitter_name": "zohreh mehrabi", "submitter_email": "zohrehmhrb@gmail.com", "authors": [ { "givenName": "John R.", "familyName": "Ladd", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Jessica", "familyName": "Otis", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Christopher", "familyName": "N. Warren", "name_identifier": "", "name_identifier_type": "" }, { "givenName": "Scott", "familyName": "Weingart", "name_identifier": "", "name_identifier_type": "" } ], "author_names": [ "John R. Ladd", "Jessica Otis", "Christopher N. Warren", "Scott Weingart" ], "author_org": { "name": "The Programming Historian", "name_identifier": "", "name_identifier_type": "" }, "contact": { "name": "", "org": "", "email": "" }, "abstract_data": "This lesson introduces network metrics and how to draw conclusions from them when working with humanities data. You will learn how to use the Network X Python package to produce and work with these network statistics.
\r\nIn this tutorial, you will learn:
\r\n-To use the Network X package for working with network data in Python
\r\n-To analyze humanities network data to find:
\r\nNetwork structure and path lengths,
\r\nImportant or central nodes,
\r\nCommunities and subgroups.
\r\n
\r\nPrerequisites
\r\nThis tutorial assumes that you have:
\r\n-A basic familiarity with networks and/or have read “From Hermeneutics to Data to Networks: Data Extraction and Network Visualization of Historical Sources” by Martin Düring here on Programming Historian;
\r\n-Installed Python 3, not the Python 2 that is installed natively in Unix-based operating systems such as Macs (If you need assistance installing Python 3, check out the Hitchhiker’s Guide to Python); and
\r\n-Installed the pip package installer.1", "abstract_format": "filtered_html", "subject": "Arts and Humanities", "keywords": [ "Community standards", "Data analysis", "Data coding", "Data skills education", "Humanities data", "Network analysis", "Programming", "Python" ], "license": "Creative Commons Attribution 4.0 International - CC BY 4.0", "usage_info": "", "citation": "John R. Ladd, Jessica Otis, Christopher N. Warren, and Scott Weingart, \"Exploring and Analyzing Network Data with Python,\" The Programming Historian 6 (2017), https://doi.org/10.46430/phen0064.", "locator_data": "10.46430/phen0064", "locator_type": "DOI", "publisher": "The Programming Historian", "version": "", "created": "2019-11-27T09:30:30", "published": "2017-08-23T00:00:00Z", "accessibility_features": [], "accessibility_summary": "", "language_primary": "en", "languages_secondary": [], "ed_frameworks": [], "target_audience": [ "Data manager", "Data professional", "Graduate student", "Librarian", "Mid-career research scientist", "Research faculty", "Research scientist", "Software engineer", "Technology expert group" ], "purpose": "Instruction - detailed information about aspects or processes related to data management or data skills.", "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": "1fcbdca1-f93c-3f8c-aadb-2919cc0c5f1c", "contributors": [], "contributor_orgs": [], "score": 1.3688016, "accesibility_summary": [], "country_of_origin": null, "credential_status": null, "notes": [] } ] }