Harvard Dataverse => Digital Library - Projects & Theses - Prof. Dr. Scholz ----- Introduction and background information to "Digital Library - Projects & Theses - Prof. Dr. Scholz". The URL of the dataverse: http://dataverse.harvard.edu/dataverse/LibraryProfScholz The URL of this (introduction) dataset: http://doi.org/10.7910/DVN/R33RS9 YOU MAY HAVE BEEN DIRECTED HERE, BECAUSE THE CALLING PAGE HAS NO OTHER ENTRY POINT (with DOI) INTO THIS DATAVERSE. Click on the title of this page to reach the start page of the dataverse! Introduction to the Data in this Dataverse This dataverse is about: Aircraft Design Flight Mechanics Aircraft Systems This dataverse contains research data and software produced by students for their projects and theses on above topics. Get linked to all other resources from their reports using the URN from the German National Library (DNB) as given in each dataset under "Metadata": https://nbn-resolving.org/html/urn:nbn:de:gbv:18302-aeroJJJJ-MM-DD.01x Alternative sites that store the data given in this dataverse are: http://library.ProfScholz.de and https://archive.org/details/@profscholz Open an "item". Under "DOWNLOAD OPTIONS" select the file (as far as available) called "ZIP" to download DataXxxx.zip. Alternatively, go to "SHOW ALL"; In the new window select next to DataXxxx.zip click "View Contents" or select URL next to "Data-list". Download single file from DataXxxx.zip. Data Publishing Data publishing means publishing of research data for (re)use by others. It consists of preparing single files or a dataset containing several files for access in the WWW. This practice is part of the open science movement. There is consensus about the benefits resulting from Open Data - especially in connection with Open Access publishing. It is important to link the publication (e.g. thesis) with the underlying data and vice versa. General (not disciplinary) and free data repositories are: Harvard Dataverse (this one!) figshare (emphasis: multi media) Zenodo (emphasis: results from EU research, mainly text) Mendeley Data (emphasis: data associated with journal articles) To find data repositories use http://re3data.org Read more on https://en.wikipedia.org/wiki/Data_publishing

Project portal for publishing, citing, sharing and discovering research data. Software, protocols, and community connections for creating research data repositories that automate professional archival practices, guarantee long term preservation, and enable researchers to share, retain control of, and receive web visibility and formal academic citations for their data contributions. Researchers, data authors, publishers, data distributors, and affiliated institutions all receive appropriate credit. Hosts multiple dataverses. Each dataverse contains studies or collections of studies, and each study contains cataloging information that describes the data plus the actual data files and complementary files. Data related to social sciences, health, medicine, humanities or other sciences with an emphasis in human behavior are uploaded to the IQSS Dataverse Network (Harvard). You can create your own dataverse for free and start adding studies for your data files and complementary material (documents, software, etc). You may install your own Dataverse Network for your University or organization.

How to find Canada's research data using various open access data repositories.

Doing archives offers empirical data relevant to archival practice, science and management from 2013 to the present. (global) see all the data for all repositories: rankings for topics, indicator values, and detailed information like the steps required to process a collection; (subnational) all the data for a city or region: rankings for topics, indicator values, and detailed information; (topics) in-depth, cross-repository view of the data, 2013.001 Getting administration, 2013.002 Employing workers, 2013.003 Educating workers, 2013.004 Getting processing, 2013.005 Dealing with appraisal, 2013.006 Registering intellectual property, 2013.007 Enforcing contracts, 2013.008 Cooperation across repositories, 2013.009 Enforcing policies, 2013.010 Dealing with acquisitions, 2013.011 Sustainability; (distance to frontier); (good practices); (transparency); (rankings) the Ease of Doing archives index ranks all repositories at the national level. Where does your repository rank? Rank repositories within their region, budget, or size; (historical)

Test dataset

Data and code for paper: "Understanding Research Data Repositories as Infrastructures" (2021). This study discusses the properties of research data repositories and analyzes metadata about 2,646 entries in the Registry of Research Data Repositories (r3data.org) to identify which of the characteristics attributed to infrastructures they exhibit. The results reveal how research data repositories function as information infrastructure for members of the scientific community and contribute to the small body of literature that examines data repositories through a socio-technical lens.

Data sets as well as R and Python code of the use cases in the book "Impact evaluation in firms and organizations"

This entry provides access to the data elements available in the Operational Data Store (ODS) for my.Harvard Student Information System. These data are available through a request process. What are the goals of the Operational Data Store? Provide data in a more real-time environment than the Warehouse (refresh 1x a day) while not putting additional load on the transactional my.harvard system. Provide a single (university-wide) standard set of exports and then web-services for retrieving key Student data. Provide the ability to incrementally load the SIS Data warehouse star schemas, making it possible to refresh certain stars more than once a day. Provide Institutional Research and Registrar power-users the ability to investigate the Student data via direct SQL access. What is the SIS Operational Data Store (SIS ODS)? A database schema on the SIS Datawarehouse that will contain replicated core tables of the my.harvard transactional system along with standardized, simplified and performant views for extracting that data. We intend to make most data available through web services before the end of academic year 2015-2016. However, our first iteration will to be make data available via db views. The refresh schedule for the SIS ODS tables for this first release will be: Academic Class Data - 1x a day between 5:30am and 6:00am. What data will be available in the SIS ODS? ODS - Academic Class v SISODS_1.0.6.xlsx follow link to get to older versions ODS - Bio Demo v SISODS_1.0.5.xlsx follow link to get to older versions ODS - Class Enrollment.xlsx ODS - Student Career Program Plan v SISODS_1.0.6.xlsx ODS - Admissions v. SISODS_1.0.7 Document coming Snapshots - non-FAS. For FAS Snapshots, please contact Harvard College Institutional Research. How can I request access to the SIS ODS? Send an email to myharvard_support@harvard.edu to request access Please indicate what data you want to access through the ODS: School & Component Available components: Academic Class (course descriptors). Biographic - Demographic Class Enrollment Student Career Program Plan Please indicate whether the request is for a personal account or for an application integration account. For personal accounts, please provide the HUIDs of the individuals to be set up. How do I connect to the SIS ODS? SIS ODS connections are currently limited to ODBC/JDBC connections to a database. The attached instructions explain how to install SQL Developer and configure a connection.

Data set from 20-21 VALE-K study students.. Visit https://dataone.org/datasets/sha256%3Ae86a90be8f723baaba22f0a7502954f518d407bcc219cfbbeb6fd1cbf3f260bc for complete metadata about this dataset.

Description and organization of the data in this repository

Questions, results and our assessment of a survey that the Dataverse UX research team conducted to learn how researchers rate different qualities of a data repository when determining which to use. The survey informed our work to redesign the Harvard Dataverse homepage.

This document describes data collected from the Main Collection of the Web of Science database. Records of published studies addressing the intersection of Open Science and data repository were searched up to January 15th, 2024, and the final dataset was comprised of 545 records for bibliometric analysis.

DASH is Harvard's digital repository for scholarly articles, theses and dissertatinos, and other Harvard-affiliate generated literature. Harvard Library makes the bibliographic data openly available for all uses, with a standard set of APIs.

Supplementary and additional Datasets (raw and preprocessed) in relation to future work of the paper Towards Detecting Inauthentic Coordination in Twitter Likes Data by Laura Jahn and Rasmus K. Rendsvig The supplementary data contains liking and retweeting user data and tweet IDs, supplemented with e.g. Botometer's botscores and later lookups regarding existence. A README facilities ## Repository Structure: - [1] Data from Danish Twitter on National Election - [2] Data from German Twitter - [3] Supplementary data to paper_Towards Detecting Inauthentic Coordination in Twitter Likes Data ## Folder content - [1] - Raw Data: Raw data of liking and retweeting users (you might come across #fv22 in file naming: the hashtag #fv22 is an election hashtag about the Danish National Election) - Preprocessed Data: - Binary like-user and retweet-user matrices - Botscores: Botometer v4 and lite scores for all likers and retweeters, also conveniently summarized in feature-frame tables - Clusters: Bins of perfectly correlated users - Later User and Tweets Lookups: Later (January, February 2023) lookup of previously collected users and tweets they likes/retweeted - Likers Retweeters Pagination: Later (January, February 2023) lookup of likers and retweeters using new pagination parameter - [2] - Raw Data: Raw data of liking and retweeting users (you might come across #bundestag in file naming: the hashtag #bundestag is a German political hashtag) - Preprocessed Data: Binary like-user and retweet-user matrices - [3] - Additional dataset dkpol July - Raw Data: Raw data of liking and retweeting users - Preprocessed Data: - Binary like-user and retweet-user matrices - Supp data to data used in paper_Towards Detecting Inauthentic Coordination in Twitter Likes Data - Botscores: Botometer v4 and lite scores for all likers and retweeters, also conveniently summarized in feature-frame tables - Later User and Tweets Lookups: Later (January, February 2023) lookup of previously collected users and tweets they likes/retweeted - Likers Retweeters Pagination: Later (January, February 2023) lookup of likers and retweeters using new pagination parameter

Alabama Shapefile and Election Results. Visit https://dataone.org/datasets/sha256%3Aeb7e3a59bc84b7f8de6e2d5050b117c1af72fc5e4e47e861767fd32606e7952d for complete metadata about this dataset.

BackgroundAccording to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), scientific research data should be findable, accessible, interoperable, and reusable. The COVID-19 pandemic has led to massive research activities and an unprecedented number of topical publications in a short time. However, no evaluation has assessed whether this COVID-19-related research data has complied with FAIR principles (or FAIRness).ObjectiveOur objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness.MethodsWe conducted a comprehensive search and retrieved all open-access articles related to COVID-19 from journals indexed in PubMed, available in the Europe PubMed Central database, published from January 2020 through June 2023, using the metareadr package. Using rtransparent, a validated automated tool, we identified articles with links to their raw data hosted in a public repository. We then screened the link and included those repositories that included data specifically for their pertaining paper. Subsequently, we automatically assessed the adherence of the repositories to the FAIR principles using FAIRsFAIR Research Data Object Assessment Service (F-UJI) and rfuji package. The FAIR scores ranged from 1–22 and had four components. We reported descriptive analysis for each article type, journal category, and repository. We used linear regression models to find the most influential factors on the FAIRness of data.Results5,700 URLs were included in the final analysis, sharing their data in a general-purpose repository. The mean (standard deviation, SD) level of compliance with FAIR metrics was 9.4 (4.88). The percentages of moderate or advanced compliance were as follows: Findability: 100.0%, Accessibility: 21.5%, Interoperability: 46.7%, and Reusability: 61.3%. The overall and component-wise monthly trends were consistent over the follow-up. Reviews (9.80, SD = 5.06, n = 160), articles in dental journals (13.67, SD = 3.51, n = 3) and Harvard Dataverse (15.79, SD = 3.65, n = 244) had the highest mean FAIRness scores, whereas letters (7.83, SD = 4.30, n = 55), articles in neuroscience journals (8.16, SD = 3.73, n = 63), and those deposited in GitHub (4.50, SD = 0.13, n = 2,152) showed the lowest scores. Regression models showed that the repository was the most influential factor on FAIRness scores (R2 = 0.809).ConclusionThis paper underscored the potential for improvement across all facets of FAIR principles, specifically emphasizing Interoperability and Reusability in the data shared within general repositories during the COVID-19 pandemic.

Open data OPEN REPOSITORY. Visit https://dataone.org/datasets/sha256%3A4e6b0784c0143f30c839f9d498e41b8bfaf35b903cf7e8e573068378fe25e0c3 for complete metadata about this dataset.

A copy of the GitHub repository containing various code and data supporting the publication of Bella Hwang's 2022 Tufts thesis.

This repository contains data and replication code for the article "Income volatility and mobility: A conceptual exploration of two frameworks", published in the journal, Research in Social Stratification and Mobility (2018-02)

This is the repository for the replication data and R files of "Money First? Strategic and Economic Interests in the International Arms Trade Network, 1920–1936".