The Institutional Data Archive on American Higher Education (IDA) contains academic data on 384 four-year colleges and universities in the United States. The IDA is one of two databases produced by the Colleges and Universities 2000 project based at the University of California, Riverside. This release, the third compilation of the IDA, is updated through academic year 2010-2011, and includes longitudinal and cross-sectional data from multiple sources. The collection is organized into nine datasets based on the unit of analysis and whether identifiers linking the data to particular institutions are present; seven of the datasets can be linked by a common identifier variable (PROJ_ID), and two cannot be linked due to confidentiality agreements. The seven identifiable datasets contain information on institutions, university systems, programs and academic departments, earned degrees, graduate schools, medical schools, and institutional academic rankings over time. Data regarding student enrollments, average SAT and ACT scores, and tuition and fees has been recorded, as well as institutional information concerning libraries, research activity, revenue and expenditures, faculty salaries, and quality rankings for program faculty. The identifiable datasets also include census information for neighborhoods surrounding IDA colleges and universities. The two non-identifiable datasets contain confidential survey responses from IDA institution presidents, chancellors, provosts, and academic vice presidents; survey questions pertained to governance structures, institutional goals and achievements, and solicited opinions on current and future issues facing the respondent's institution and higher education in general.

Abstract (en): The American College Catalog Study Database (CCS) contains academic data on 286 four-year colleges and universities in the United States. CCS is one of two databases produced by the Colleges and Universities 2000 project based at the University of California-Riverside. The CCS database comprises a sampled subset of institutions from the related Institutional Data Archive (IDA) on American Higher Education (ICPSR 34874). Coding for CCS was based on college catalogs obtained from College Source, Inc. The data are organized in a panel design, with measurements taken at five-year intervals: academic years 1975-76, 1980-81, 1985-86, 1990-91, 1995-96, 2000-01, 2005-06, and 2010-11. The database is based on information reported in each institution's college catalog, and includes data regarding changes in major academic units (schools and colleges), departments, interdisciplinary programs, and general education requirements. For schools and departments, changes in structure were coded, including new units, name changes, splits in units, units moved to new schools, reconstituted units, consolidated units, departments reduced to program status, and eliminated units. The American College Catalog Study Database (CCS) is intended to allow researchers to examine changes in the structure of institutionalized knowledge in four-year colleges and universities within the United States. For information on the study design, including detailed coding conventions, please see the Original P.I. Documentation section of the ICPSR Codebook. The data are not weighted. Dataset 1, Characteristics Variables, contains three weight variables (IDAWT, CCSWT, and CASEWEIGHT) which users may wish to apply during analysis. For additional information on weights, please see the Original P.I. Documentation section of the ICPSR Codebook. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: Approximately 75 percent of IDA institutions are included in CCS. For additional information on response rates, please see the Original P.I. Documentation section of the ICPSR Codebook. Four-year not-for-profit colleges and universities in the United States. Smallest Geographic Unit: state CCS includes 286 institutions drawn from the IDA sample of 384 United States four-year colleges and universities. CCS contains every IDA institution for which a full set of catalogs could be located at the initiation of the project in 2000. CCS contains seven datasets that can be linked through an institutional identification number variable (PROJ_ID). Since the data are organized in a panel format, it is also necessary to use a second variable (YEAR) to link datasets. For a brief description of each CCS dataset, please see Appendix B within the Original P.I. Documentation section of the ICPSR Codebook.There are date discrepancies between the data and the Original P.I. Documentation. Study Time Periods and Collection Dates reflect dates that are present in the data. No additional information was provided.Please note that the related data collection featuring the Institutional Data Archive on American Higher Education, 1970-2011, will be available as ICPSR 34874. Additional information on the American College Catalog Study Database (CCS) and the Institutional Data Archive (IDA) database can be found on the Colleges and Universities 2000 Web site.

The Database of Political Institutions presents institutional and electoral results data such as measures of checks and balances, tenure and stability of the government, identification of party affiliation and ideology, and fragmentation of opposition and government parties in the legislature, among others. The current version of the database, which is now hosted at the IDB, expands its coverage to about 180 countries for 40 years, 1975-2015. Researchers at the World Bank Development Research Group first compiled the database in 2000 (see citation information below). It has become one of the most cited databases in comparative political economy and comparative political institutions. Almost 3000 studies have used this database so far as a source of institutional and political data in their empirical analysis.

The database will be used to track SSA's contributions to Minority Serving Institutions such as Historically Black Colleges and Universities (HBCU), Tribal Colleges and Universities (TCU), Hispanic Serving Institutions (HSI), and Asian American and Native American Pacific Islander Serving Institutions (AANAPISI).

This dataset contains data used in publication "Institutional Data Repository Development, a Moving Target" submitted to Code4Lib Journal. It is a tabular data file describing attributes of data files in datasets published in Illinois Data Bank 2016-04-01 to 2021-04-01.

The Database of Political Institutions presents institutional and electoral results data such as measures of checks and balances, tenure and stability of the government, identification of party affiliation and ideology, and fragmentation of opposition and government parties in the legislature, among others. The current version of the database, which is now hosted at the IDB, expands its coverage to about 180 countries for 42 years, 1975–2017. Researchers at the World Bank Development Research Group first compiled the database in 2000 (see citation information below). It has become one of the most cited databases in comparative political economy and comparative political institutions. Almost 3000 studies have used this database so far as a source of institutional and political data in their empirical analysis.

The Federal Reserve Bank of Cleveland collects data from hundreds of financial institutions, including depository institutions, bank holding companies, and other financial and non-financial entities. Data collected from these institutions by the Federal Reserve and other regulatory agencies are available on our website.

working-age population (ages 15-64)growth rate was extracted from the World Bank Open Data databases .he real GDP (Y) ,the working-age population (L) and the depreciation rate δ were extracted from the Penn World Table 9.1 . The growth rate of technological progress g is assumed to be constant and equal to 1%.the average share of real investment (inclusive of government investment) was calculated based on PWT 9.1 data. The Regulator Quality Index, Corruption Index, Voice and Accountability Index, Political Stabili-ty/No Violence Index, Government Effectiveness Index, and Rule of Law Index were obtained from the Worldwide Governance Indicators (WGI) dataset.This dataset include 4 different samples of countries, with different economic and institutional environment.

We conducted an analysis to confirm our observations that only a very small percentage of public research data is hosted in the Institutional Data Repositories, while the vast majority is published in the open domain-specific and generalist data repositories.

For this analysis, we selected 11 institutions, many of which have been our evaluation partners. For each institution, we counted the number of datasets published in their Institutional Data Repository (IDR) and tracked the number of public research datasets hosted in external data repositories via the Data Monitor API. External tracking was based on the corpus of 14+ mln data records checked against the institutional SciVal ID. One institution didn’t have an IDR.

We found out that 10 out of 11 institutions had most of their public research data hosted outside of their institution, where by research data we mean not only datasets, but a broader notion that includes, for example, software.

We will be happy to expand it by adding more institutions upon request.

Note: This is version 2 of the earlier published dataset. The number of datasets published and tracked in the Monash Institutional Data Repository has been updated based on the information provided by the Monash Library. The number of datasets in the NTU Institutional Data Repository now includes datasets only. Dataverses were excluded to avoid double counting.

Nowadays Public Development Banks (PDBs) and Development Financing Institutions (DFIs) are experiencing a renaissance worldwide as their usefulness is increasingly recognized by both academics and policymakers. PDBs and DFIs are public financial institutions initiated by governments to proactively achieve public policy objectives. They are potentially useful policy instruments for fixing market failures, incubating markets, coordinating public policies with stakeholders, and promoting economic structural transformation in an equitable and sustainable manner. But not all PDBs and DFIs have achieved positive development outcomes as expected. To fulfill their development potential, it is important to rigorously examine their roles, operations, and effectiveness to avoid pitfalls. But the in-depth academic research is scarce. One major reason behind the paucity of research is the lack of systematic efforts to identify these public financial institutions and collect relevant data. To fill this gap, the development financing research program of the Institute of New Structural Economics at Peking University (INSE) initiated to build the world's first development financing institution database in September 2017, and launched the database by disclosing the first comprehensive list of PDBs and DFIs and their basic data in May 2019. Recognizing INSE's pilot effort, French Development Agency (Agence Française de Développement, AFD), aims to identify those that could form a world coalition to emphasize the importance of incorporating the Sustainable Development Goals (SDGs) into the corporate strategies of PDBs and DFIs. On that basis, since November 2019, the INSE and the AFD have collaborated to build on the INSE’s pilot effort to strengthen first ever comprehensive database on PDBs and DFIs with rigorous criteria and methodologies. Since 2023 and 2024, Foundation for Studies and Research on International Development (Fondation pour les Études et Recherches sur le Développement International, FERDI), and School of Health Humanities (SHH) at Peking University joined as collaborating institutions to co-build the PDFIs database. The database aims to allow researchers to not only identify PDBs and DFIs worldwide in a comprehensive manner but also provide the information on their basic profile and financial indicators (such as official mandate and total assets). We hope that our pilot and persistent efforts to build the comprehensive database can promote original research on the rationales, operations, governance and performance of PDBs and DFIs to enhance our understanding of such important public financial institutions and realize their full potentials. To learn more about the database, please visit its datavision website (http://www.dfidatabase.pku.edu.cn/).

Japan All Banks: Number of Institutions data was reported at 115.000 Unit in Mar 2018. This stayed constant from the previous number of 115.000 Unit for Mar 2017. Japan All Banks: Number of Institutions data is updated semiannually, averaging 121.000 Unit from Mar 1999 (Median) to Mar 2018, with 37 observations. The data reached an all-time high of 138.000 Unit in Mar 1999 and a record low of 115.000 Unit in Mar 2018. Japan All Banks: Number of Institutions data remains active status in CEIC and is reported by Financial Services Agency. The data is categorized under Global Database’s Japan – Table JP.KB056: Number of Financial Institutions: Incl Agri Coop & Shoko Chukin Bank.

Nowadays the collection of operational risk data worldwide highly relies on human labour, leading to slow updates, data inconsistency, and limited quantity. There remains a substantial shortage of publicly accessible operational risk databases for risk analysis. This study proposes a new data collection framework by aggregating text mining methods to replace the exhausting manual collection process. The news about operational risk can be automatically collected from the web page, then its content is analyzed and the key information is extracted. Finally, the Public-Chinese Operational Loss Data (P-COLD) database for financial institutions is constructed and expanded. Each record contains 12 key information, such as occurrence time, loss amount, and business lines, offering a more thorough description of operational risk events. With 3,723 data records from 1986 to 2023, the P-COLD database has become one of the largest and most comprehensive external operational risk databases in China. We anticipate the P-COLD database will contribute to advancements in operational risk capital calculations, dependence analysis, and institutional internal controls.The P-COLD-English ver.xlsx is a cross-institutional database on operational risk data in China's banking sector, collected from publicly available sources and translated into English.The P-COLD-Chinese ver.xlsx is a cross-institutional database on operational risk data in China's banking sector, collected from publicly available sources and recorded in Chinese.(The P-COLD-English ver.xlsx is the English-translated version of P-COLD-Chinese ver.xlsx.)The Data dictionary.xlsx records the description of each field in P-COLD database.

The SCImago Institutions Rankings (SIR) is a classification of academic and research-related institutions ranked by a composite indicator that combines three different sets of indicators based on research performance, innovation outputs and societal impact measured by their web visibility. It provides a friendly interface that allows the visualization of any customized ranking from the combination of these three sets of indicators. Additionally, it is possible to compare the trends for individual indicators of up to six institutions. For each large sector it is also possible to obtain distribution charts of the different indicators. For comparative purposes, the value of the composite indicator has been set on a scale of 0 to 100. However the line graphs and bar graphs always represent ranks (lower is better, so the highest values are the worst).

📚 Institutional Books 1.0

Institutional Books is a growing corpus of public domain books. This 1.0 release is comprised of 983,004 public domain books digitized as part of Harvard Library's participation in the Google Books project and refined by the Institutional Data Initiative. Use of this data is governed by the IDI Terms of Use for Early-Access.

983K books, published largely in the 19th and 20th centuries 242B o200k_base tokens 386M pages of text, available in both original… See the full description on the dataset page: https://huggingface.co/datasets/institutional/institutional-books-1.0.

The Texas Department of Insurance, Division of Workers' Compensation (DWC) maintains a database of institutional medical billing services (SV2). It contains charges, payments, and treatments billed on a CMS-1450 form (UB-92, UB-04) by hospitals and medical facilities that treat injured employees, excluding ambulatory surgical centers, with dates of service more than five years old. For datasets from the past five years, see institutional medical billing services (SV2) header information. The header identifies insurance carriers, injured employees, employers, place of service, and diagnostic information. The bill header information groups individual line items reported in the detail section. The bill selection date and bill ID must be used to group individual line items into a single bill. Find more information in our institutional medical billing services (SV2) header data dictionary. See institutional medical billing services (SV2) detail information- historical for the corresponding detail records related to this dataset. Go to our page on DWC medical state reporting public use data file (PUDF) to learn more about using this information.

Our sample consists of annual data from firms listed on the A-share markets of the Shanghai and Shenzhen Stock Exchanges in China, covering the period from 2003 to 2022. We gather the necessary data on listed firm from two databases: Chinese Innovation Research Database (CIRD) for firms’ innovation, China Stock Market & Accounting Research Database (CSMAR) for common ownership. CIRD not only includes patent data filed or granted to different entities, distinguishing between three types of patents—invention, utility model, and design—but also provides key information such as the nature of applications (independent or joint), classification numbers, and patent statistics. CSMAR database is positioned as a research-oriented precision database, referring to the standards of authoritative databases such as CRSP and COMPUSTAT, with the aim of researching and quantifying investment analysis. We match the innovation data to the financial data for each firm, and we exclude financial listed companies, exclude ST and * ST listed companies and delete samples with missing data. To avoid extreme value interference, we winsorize all continuous variables at the 1% level. With these filters, our final sample of 48,956 firm-year observations for 4957 firms.

[Metadata] Description: Postsecondary Institutions and Programs in Hawaii as of April, 2017.
Source: Downloaded from the US DOE (https://ope.ed.gov/accreditation/index.aspx), December, 2017.

[taken from the ED OPE website] “The Database of Accredited Postsecondary Institutions and Programs contains information reported to the U.S. Department of Education directly by recognized accrediting agencies and state approval agencies that have been asked to provide information for each institution and/or program accredited by that agency. This reported information is not audited. The database reflects additional information as it is received from recognized accrediting agencies and state approval agencies. The U.S. Department of Education cannot, therefore, guarantee that the information contained in the database is accurate, current, or complete. For the most accurate and current information, contact the appropriate agency.”

Human lifestyles vary enormously over time and space and so understanding the origins of this diversity has always been a central focus of anthropology. A major source of this cultural variation is the variation in institutional complexity; the cultural packages of rules, norms, ontologies, and expectations passed down through societies across generations. In this paper we study the emergence of institutions in small-scale societies. There are two primary schools of thought. The first is that institutions emerge top-down as rules are imposed by elites on their societies in order to gain asymmetrical access to power, resources, and influence over others through coercion. The second is that institutions emerge bottom-up to facilitate interactions within populations as they seek collective solutions to adaptive problems. Here, we use Bayesian networks to infer the causal structure of institutional complexity in 172 small-scale societies across ethnohistoric western North America reflecting the wide diversity of indigenous lifestyles across this vast region immediately prior to European colonization. Our results suggest that institutional complexity emerges from underlying socioecological complexity because institutions are solutions to coordination problems in more complex environments where human-environment interactions require increased management.

List of proposed community anchor institutions submitted for BEAD funding as of Jan 2024