Traffic analytics, rankings, and competitive metrics for dataverse.org as of June 2025

This dataset contains raw data and processed data from the Dataverse Community Survey 2022. The main goal of the survey was to help the Global Dataverse Community Consortium (GDCC; https://dataversecommunity.global/) and the Dataverse Project (https://dataverse.org/) decide on what actions to take to improve the Dataverse software and the larger ecosystem of integrated tools and services as well as better support community members. The results from the survey may also be of interest to other communities working on software and services for managing research data. The survey was designed to map out the current status as well as the roadmaps and priorities of Dataverse installations around the world. The main target group for participating in the survey were the people/teams responsible for operating Dataverse installations around the world. A secondary target group were people/teams at organizations that are planning to deploy or considering deploying a Dataverse installation. There were 34 existing and planned Dataverse installations participating in the survey.

This dataset contains the metadata of the datasets published in 77 Dataverse installations, information about each installation's metadata blocks, and the list of standard licenses that dataset depositors can apply to the datasets they publish in the 36 installations running more recent versions of the Dataverse software. The data is useful for reporting on the quality of dataset and file-level metadata within and across Dataverse installations. Curators and other researchers can use this dataset to explore how well Dataverse software and the repositories using the software help depositors describe data. How the metadata was downloaded The dataset metadata and metadata block JSON files were downloaded from each installation on October 2 and October 3, 2022 using a Python script kept in a GitHub repo at https://github.com/jggautier/dataverse-scripts/blob/main/other_scripts/get_dataset_metadata_of_all_installations.py. In order to get the metadata from installations that require an installation account API token to use certain Dataverse software APIs, I created a CSV file with two columns: one column named "hostname" listing each installation URL in which I was able to create an account and another named "apikey" listing my accounts' API tokens. The Python script expects and uses the API tokens in this CSV file to get metadata and other information from installations that require API tokens. How the files are organized ├── csv_files_with_metadata_from_most_known_dataverse_installations │ ├── author(citation).csv │ ├── basic.csv │ ├── contributor(citation).csv │ ├── ... │ └── topic_classification(citation).csv ├── dataverse_json_metadata_from_each_known_dataverse_installation │ ├── Abacus_2022.10.02_17.11.19.zip │ ├── dataset_pids_Abacus_2022.10.02_17.11.19.csv │ ├── Dataverse_JSON_metadata_2022.10.02_17.11.19 │ ├── hdl_11272.1_AB2_0AQZNT_v1.0.json │ ├── ... │ ├── metadatablocks_v5.6 │ ├── astrophysics_v5.6.json │ ├── biomedical_v5.6.json │ ├── citation_v5.6.json │ ├── ... │ ├── socialscience_v5.6.json │ ├── ACSS_Dataverse_2022.10.02_17.26.19.zip │ ├── ADA_Dataverse_2022.10.02_17.26.57.zip │ ├── Arca_Dados_2022.10.02_17.44.35.zip │ ├── ... │ └── World_Agroforestry_-_Research_Data_Repository_2022.10.02_22.59.36.zip └── dataset_pids_from_most_known_dataverse_installations.csv └── licenses_used_by_dataverse_installations.csv └── metadatablocks_from_most_known_dataverse_installations.csv This dataset contains two directories and three CSV files not in a directory. One directory, "csv_files_with_metadata_from_most_known_dataverse_installations", contains 18 CSV files that contain the values from common metadata fields of all 77 Dataverse installations. For example, author(citation)_2022.10.02-2022.10.03.csv contains the "Author" metadata for all published, non-deaccessioned, versions of all datasets in the 77 installations, where there's a row for each author name, affiliation, identifier type and identifier. The other directory, "dataverse_json_metadata_from_each_known_dataverse_installation", contains 77 zipped files, one for each of the 77 Dataverse installations whose dataset metadata I was able to download using Dataverse APIs. Each zip file contains a CSV file and two sub-directories: The CSV file contains the persistent IDs and URLs of each published dataset in the Dataverse installation as well as a column to indicate whether or not the Python script was able to download the Dataverse JSON metadata for each dataset. For Dataverse installations using Dataverse software versions whose Search APIs include each dataset's owning Dataverse collection name and alias, the CSV files also include which Dataverse collection (within the installation) that dataset was published in. One sub-directory contains a JSON file for each of the installation's published, non-deaccessioned dataset versions. The JSON files contain the metadata in the "Dataverse JSON" metadata schema. The other sub-directory contains information about the metadata models (the "metadata blocks" in JSON files) that the installation was using when the dataset metadata was downloaded. I saved them so that they can be used when extracting metadata from the Dataverse JSON files. The dataset_pids_from_most_known_dataverse_installations.csv file contains the dataset PIDs of all published datasets in the 77 Dataverse installations, with a column to indicate if the Python script was able to download the dataset's metadata. It's a union of all of the "dataset_pids_..." files in each of the 77 zip files. The licenses_used_by_dataverse_installations.csv file contains information about the licenses that a number of the installations let depositors choose when creating datasets. When I collected ... Visit https://dataone.org/datasets/sha256%3Ad27d528dae8cf01e3ea915f450426c38fd6320e8c11d3e901c43580f997a3146 for complete metadata about this dataset.

Comparative review of open access data repositories collected to inform product development for the Dataverse Project at the Harvard Institute for Quantitative Social Science More information about the scope, purpose and development of this review is at https://dataverse.org/blog/comparative-review-various-data-repositories.

This dataset contains a summary of information about known public installations of Open Journal Systems, Open Monograph Press, and Open Preprint Systems. Data are updated annually. See previous revisions for previous years' data.

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TXST Dataverse Quick Start Guide-Example RDM Dataverse

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.

experimental data. Visit https://dataone.org/datasets/sha256%3A18735774f162e6915a7d05c2276ae4ddf535e237e1559bebab64d219355e9ca8 for complete metadata about this dataset.

Piper, Andrew, 2016, "Fictionality", doi:10.7910/DVN/5WKTZV, Harvard Dataverse, V1. Contains LIWC feature tables for all ~27,000 documents used in this study, R and Python code used to generate statistical results, and all supporting tables. Original CA article published at http://culturalanalytics.org/2016/12/fictionality/ See also Community Norms [http://best-practices.dataverse.org/harvard-policies/community-norms.html] as well as good scientific practices expect that proper credit is given via citation.

To prevent people from publishing spam in Harvard Dataverse, repository staff are considering publishing workflows that prevent people from publishing datasets and/or dataverses into the repository's "Root" dataverse until repository staff can verify that account creators won't deposit spam. To help estimate the number of "Root"-depositor accounts and deposits that staff may have to vet each day, the Jupyter notebook included in this dataset analyzes two years of user account data, included in the tabular file, to try to answer: How many datasets and dataverses, that have gone on to be published, have been created in Root each day? How many accounts have created more than one of these datasets in Root? How many accounts created these datasets in Root each day, and which of those accounts are first time depositors versus returning depositors? Run the notebook in Binder

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International Organization Evaluation Report Dataset (IOEval) and replication data for ‘The Performance of International Organizations: A New Measure and Dataset Based on Computational Text Analysis of Evaluation Reports, Review of International Organizations, DOI: 10.1007/s11558-023-09489-1.’ Eckhard, Steffen; Jankauskas, Vytautas; Leuschner, Elena; Burton, Ian; Kerl, Tilman; Sevastjanova, Rita, 2023, "International Organization Evaluation Report Dataset (IOEval) and replication data for ‘The Performance of International Organizations: A New Measure and Dataset Based on Computational Text Analysis of Evaluation Reports’", https://doi.org/10.7910/DVN/0SI2VX, Harvard Dataverse, V1, UNF:6:fBGGclS7HUPoO8PEGwGFZg== [fileUNF] This dataset contains: • the sentence-level text of 1,082 evaluation reports published by nine international organizations of the United Nations (UN) system between 2012 to 2021); • a fine-tuned BERT language model that allows classifying individual sentences in evaluation reports as containing a positive, negative or neutral assessment of the evaluated activity; • and replication files for our publication DOI: 10.1007/s11558-023-09489-1. When using the data, please cite: “Eckhard, Steffen; Jankauskas, Vytautas; Leuschner, Elena; Burton, Ian; Kerl, Tilman; Sevastjanova, Rita (2023). The Performance of International Organizations: A New Measure and Dataset Based on Computational Text Analysis of Evaluation Reports. Review of International Organizations, DOI: 10.1007/s11558-023-09489-1.” Summary of the IOEval Dataset: The IOEval dataset contains the sentence-level text of 1,082 evaluation reports published by nine international organizations of the United Nations (UN) system between 2012 to 2021. Raw text was cleaned by applying standard procedures of natural language processing (e.g., removal of special characters and numbers) and split into sentences. The text is taken from evaluation reports by International Labor Organization (ILO), the UN Development Program (UNDP), the UN International Children's Emergency Fund (UNICEF), the Food and Agricultural Organization (FAO), the UN Educational, Scientific and Cultural Organization (UNESCO), the World Health Organization (WHO), the International Organization for Migration (IOM), the UN High Commissioner for Refugees (UNHCR) and the UN Entity for Gender Equality and the Empowerment of Women (UN WOMEN). At a sentence level, the dataset specifies to which text section a sentence belongs (executive summary, main text, appendix). The IOEval dataset also includes metadata variables at the level of reports: report title, publication date, evaluation type (project, program, institutional or thematic), evaluation level (country (specifying its name), regional, global), and commissioning unit (centralized or decentralized). Summary of language model: The fine-tuned BERT language model (Devlin et al., 2019) allows classifying individual sentences in evaluation reports as containing a positive, negative or neutral assessment of the evaluated activity. It was fine-tuned and evaluated on around 10,000 hand-coded sentences from evaluation reports, reaching a recall of 89 percent.

This is data for Courtney

The Sicpa_OpenData libraries allow to facilitate the publication of data to the INRAE dataverse in a transparent way 1/ by simplifying the creation of the metadata document from the data already present in the information systems, 2/ by simplifying the use of dataverse.org APIs.

This database includes all secondary data used in the project "Epistemic Market Objects: How Sponsored Content Disrupted Marketing." It includes data from Adweek, podcast data, and third-party platform reviews. Unfortunately, we are not allowed to share primary interviews as we signed a confidentiality form with participants. This form is also protected by the Canadian Tri-Council for Research.

The Sicpa_OpenData libraries facilitate the publication of data to the INRAE dataverse in a transparent way 1/ by simplifying the creation of the metadata document from the data already present in the information systems, 2/ by simplifying the use of the dataverse.org APIs. Available as a WHL file, the SicpaOpenData for Python library can be used from all the developments of the Python ecosystem

Attachments include replication data, replication code, and a short document explaining the dataset with variable descriptions for the paper: "An Investigation of Social Media Labeling Decisions Preceding the 2020 U.S. Election" by Samantha Bradshaw, Shelby Grossman, and Miles McCain.

I have Dataset+Dataverse Creator role in this dataverse.

This paper aims at understanding the governance of public budgeting in large metropolises with the use of comparative analysis. The analysis is focused on budgetary governance in London and São Paulo and uses qualitative and quantitative data from 2008 to 2019 to understand whether analytical categories such as incrementalism of expenditures, complexity of budgetary rules, bureaucratic hierarchy, bargaining, and muddling through are useful to compare two metropolises, especially to determine the discretionary power of mayors in making budget allocation decisions. The analytical categories are derived from the studies of theorists of economics and political sociology, notably Wildavsky (1975, 1969), Wildavsky and Caiden (2004), Schick (2009, 1976), Caiden (2010) Lascoumes and Le Galès (2005), Baumgartner and Jones (2005), and Fuchs (2012, 2010). The main argument of the paper is that, despite the administrative and political differences between London and São Paulo, similar dimensions can explain decisions about budget allocation and the political discretionary power of mayors. The study shows that mayors have little discretionary power, particularly in contexts of fiscal austerity; it also highlights the importance of property tax as a means to protect such power.