The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

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.

Lobachevsky University Electrocardiography Database (LUDB) is an ECG signal database with marked boundaries and peaks of P, T waves and QRS complexes. The database consists of 200 10-second 12-lead ECG signal records representing different morphologies of the ECG signal. The ECGs were collected from healthy volunteers and patients of the Nizhny Novgorod City Hospital No 5 in 2017–2018. The patients had various cardiovascular diseases while some of them had pacemakers. The boundaries of P, T waves and QRS complexes were manually annotated by cardiologists for all 200 records. Also, each record is annotated with the corresponding diagnosis. The database can be used for educational purposes as well as for training and testing algorithms for ECG delineation, i.e. for automatic detection of boundaries and peaks of P, T waves and QRS complexes.

Data product is provided by ASL Marketing. It contains current college students who are attending colleges and universities nationwide. Connect with this market by: Class Year Field of Study Home/School address College Attending Ethnicity School Type Region Sports Conference Gender eSports Email

Databases with information on Latin American universities according to the webometrics ranking.

The Stanford University HIV Drug Resistance Database is a curated public database designed to represent, store, and analyze the different forms of data underlying HIVs drug resistance. HIVDB has three main types of content: (1) Database queries and references, (2) Interactive programs, and (3) Educational resources. Database queries are designed primarily for researchers studying HIV drug resistance. The interactive programs and educational resources are designed for both researchers and those wishing to learn more about HIV drug resistance. 1.DATABASE QUERY AND REFERENCE PAGES Genotype-Treatment Correlations This Genotype-Treatment section of the database links to 15 interactive query pages that explore the relationship between treatment with HIV-1 antiretroviral drugs (ARVs) and mutations in HIV reverse transcriptase (RT), protease, and integrase. There are five types of interactive query pages: Treatment Profiles (Protease and RT inhibitors) Mutation Profiles (Protease and RT mutations) Detailed Treatment Queries (Protease, RT, and integrase inhibitors) Detailed Mutation Queries (Protease, RT, and integrase mutations) Mutation Prevalence According to Subtype and Treatment Genotype-Phenotype Correlations The main page of the Genotype-Phenotype Correlations section links to four interactive query pages: three dynamically updated data summaries and one regularly updated downloadable dataset. Drug Resistance Positions Query for levels of resistance associated with known drug resistance mutations Detailed Phenotype Queries Queries for levels of resistance associated with individual mutations or mutation combinations at all positions of protease, RT, and integrase Patterns of Drug Resistance Mutations Downloadable Reference Dataset Genotype-Clinical Correlations This part of the database has two main sections: Clinical Trials Datasets Summaries of Clinical Studies References This part of the database has two main sections: one with summaries of the data from each of the references in HIVDB and one in which every primate immunodeficiency virus sequence in GenBank is annotated according to its presence or absence in HIVDB. Studies in HIVDB GenBank HIVDB New Submissions Approximately every three months, the New Submissions section lists the studies that have been entered into HIVDB. The study title links to the introductory page of the study in the References section. Database Statistics (http://hivdb.stanford.edu/pages/HIVdbStatistics.html) 2. INTERACTIVE PROGRAMS HIVDB has seven main interactive programs. 1. HIVdb Program Mutation List Analysis Sequence Analysis HIVdb Output Sierra Web Service Release Notes Algorithm Specification Interface (ASI) 2. HIValg Program 3. HIVseq Program 4. Calibrated Population Resistance (CPR) tool 5. Mutation ARV Evidence Listing (MARVEL) 6. ART-AiDE 7. Rega HIV-1 Subtyping tool Three programs in the HIV Drug Resistance Database share a common code base: HIVseq, HIVdb, and HIValg. HIVseq accepts user-submitted protease, RT, and integrase sequences, compares them to the consensus subtype B reference sequence, and uses the differences as query parameters for interrogating the HIV Drug Resistance database (Shafer, D Jung, & B Betts, Nat Med 2000; Rhee SY et al. AIDS 2006). The query result provides users with the prevalence of protease, RT and integrase mutations according to subtype and PI, nucleoside RT inhibitor (NRTI), non-nucleoside RT inhibitor (NNRTI), and integrase inhibitor (INI) exposure. This allows users to detect unusual sequence results immediately so that the person doing the sequencing can check the primary sequence output while it is still on the desktop. In addition, unexpected associations between sequences or isolates can be discovered by immediately retrieving data on isolates sharing one or more mutations with the sequence. There are three ways in which the HIVdb program can be used: (i) entering a list of protease and RT mutations, (ii) entering a complete sequence containing protease, RT, and/or integrase, and (iii) using a Web Service. HIVdb is an expert system that accepts user-submitted HIV-1 pol sequences and returns inferred levels of resistance to 20 FDA-approved ARV drugs including 8 PIs, 7 NRTIs, 4 NNRTIs, and - with this update - one INI. In the HIVdb system, each HIV-1 drug resistance mutation is assigned a drug penalty score and a comment; the total score for a drug is derived by adding the scores of each mutation associated with resistance to that drug. Using the total drug score, the program reports one of the following levels of inferred drug resistance: susceptible, potential low-level resistance, low-level resistance, intermediate resistance, and high-level resistance. HIValg is designed for users interested in comparing the results of different algorithms or who are interested in comparing and evaluating existing and newly developed algorithms. The ability to develop new algorithms that can be run on the HIV Drug Resistance Database depends on the Algorithm Specific Interface (ASI) compiler (Shafer & Betts JCM 2003). Submission of Sequences and Mutations For each of the three programs, sequences can be entered using either the Sequence Analysis Form or the Mutation List form. 3. EDUCATIONAL RESOURCES HIVDB contains several regularly updated sections summarizing data linking RT, protease, and integrase mutations and antiretroviral drugs (ARVs). These sections include (i) tabular summaries of the major mutations associated with each ARV class, (ii) detailed summaries of the major, minor, and accessory mutations associated with each ARV, (iii) the comments used by the HIVdb program, (iv) the scores used by the HIVdb program, (v) clinical studies in which baseline drug resistance mutations have been correlated with the virological response (clinical outcome) to a specific ARV, (vi) mutations that can be used for drug resistance surveillance, and (vii) a two-page PDF handout. 1. Drug Resistance Summaries Tabular Drug Resistance Summaries by ARV Class Detailed Drug Resistance Summaries by ARV Drug Resistance Mutation Comments Used by the HIVdb Program Drug Resistance Mutation Scores Used by the HIVdb Program Genotype-Clinical Outcome Correlation Studies 2. Surveillance Drug-Resistance Mutation List Section 3. PDF Handout Grant Support 1. National Institute for Allergy and Infectious Diseases (NIAID, NIH): Online HIV Drug Resistance Database (PI: Robert W. Shafer, MD, 1R01AI68581-01A1), 04/01/06 - 3/31/11 2. National Institute for Allergy and Infectious Diseases (NIAID, NIH) supplement to the grant Identification of Multidrug-Resistant HIV-1 Isolates (PI: Robert W. Shafer, MD, AI46148-01): Supplement provided 1999-2005. 3. NIH/NIGMS Program Project on AIDS Structural Biology Program Project: Targeting Ensembles of Drug Resistant Protease Variants (PI: Celia Schiffer, PhD, University of Massachusetts): 2002-2007 4. University-wide AIDS Research Program (CR03-ST-524). Community collaborative award: Optimizing Clinical HIV Genotypic Resistance Interpretation: Principal Investigators: Robert W. Shafer, MD and W. Jeffrey Fessel MD (Kaiser Permanente Medical Care Program): 2004-2005 5. Stanford University Bio-X Interdisciplinary Initiative: HIV Gene Sequence Analysis for Drug Resistance Studies: A Pharmacogenetic Challenge Principal Investigators: Robert W. Shafer, MD and Daphne Koller, Ph.D. (Computer Science): 2000-2002

St. Vincents University Hospital / University College Dublin Sleep Apnea Database: This database contains 25 full overnight polysomnograms with simultaneous three-channel Holter ECG, from adult subjects with suspected sleep-disordered breathing.

Comprehensive dataset of 9 University libraries in Idaho, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoceanography. The data include parameters of paleoceanography with a geographic location of Global Ocean. The time period coverage is from 100 to -50 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

The University of Wisconsin Antarctic Soils Database contains data collected by Dr. James G. Bockheim and his colleagues from 1975 through 1987. Data include site information, air and soil temperature measurements, soil profile features, and surface boulder weathering features for 482 sites in the McMurdo Sound area of Antarctica. Soil profile descriptions are provided for soils inside the McMurdo Dry Valleys from 23 December 1975 to 22 December 1987, and outside the Dry Valleys from 13 November 1978 to 04 January 1986. Chemical and physical properties of soils at 214 sites are also provided. The study area is confined to 77 deg 7.5 min S to 78 deg S, 160 deg E to 164 deg E. Data are in tab-delimited ASCII text format, and are available via ftp.

D-HaploDB genome-wide definitive haplotypes, determined using a collection of 100 Japanese complete hydatidiform moles (CHMs), each carrying a genome derived from a single sperm. The haplotypes incorporate 281 k (D-Haplo Phase I: D1), 581k (D-Haplo Phase II: D2), or 1M (D-Haplo Phase III: D3) SNPs, genotyped with high throughput array-based oligonucleotide hybridization techniques. The Definitive Haplotype Browser can be used to view various information, such as SNP alleles, haplotype blocks, LD-bins and extended shared haplotypes (ESHs) in our study.

Hymenoptera specimen database of Kyushu University

Comprehensive dataset of 9 University libraries in South Dakota, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This radar data was collected by a system in Goose Bay

.UNIVERSITY Whois Database, discover comprehensive ownership details, registration dates, and more for .UNIVERSITY TLD with Whois Data Center.

Nathan A. Slaton, Rajveer Singh, Uzair Ahmad, Cheri Villines, Russell Delong, and Otis Robinson[Note: Updated for 2025 release]. The database contains select properties of 16,728 dairy, poultry, and swine manure samples submitted between 1 January 2005 and 31 December 2024 to the University of Arkansas Division of Agriculture Fayetteville Agricultural Diagnostic Laboratory (FADL). Most samples were submitted by clients with active animal production farms to determine manure properties for nutrient management planning. Most samples are from farms within Arkansas (4,862) followed by Tennessee (386), and Oklahoma (206). Many of the samples from 2005–2022 do not include a county and state of origin, but Arkansas is the primary state of origin for these samples in the database. Metadata describing the production system, manure collection and storage, age, and bedding was provided by clients and assumed to be reasonably accurate. Animal type, Bedding type, and Manure type metadata not provided by the client were listed as “Unknown”. Metadata for Sample age (days), State, County, and some analytes were sometimes missing and left as blank cells.We could not find a single literature source that describes all production systems and manure/litter types, but the information in Malone (1992), Key et al. (2011), and USDA-NRCS (2012), describe animal production systems, manure forms, and the factors that influence litter/manure production in animal production systems in the USA that may help understand the types of litter/manure forms included in this database.Poultry litter (Dry) SamplesThe database includes information for >14,000 poultry samples submitted from 1 January 2005 through 31 December 2024. Samples in the database represented Broiler, Hen, Pullet, Turkey, Cornish, Rooster, and Unknown (no animal-specific production system noted). An example manure submission form is shown in Figure 1. Manure types include Cake, Cleanout, Compost, Dead bird compost, Deep stack, Dry stack, Fresh litter, In-house, Lagoon liquid, Lagoon sludge, Loose, Pellets, Sludge, and Unknown. Bedding materials include Rice (Oryza sativa L.) hulls, Sawdust, Wood shavings, mixtures of Rice hulls and Sawdust, Rice hulls and Wood shavings, Wood shavings and Sawdust, Straw and Wood shavings, and Unknown.Arkansas clients usually deliver samples directly to the FADL or a local county Extension office where a sample submission form (Figure 1) is completed, and the sample is shipped to the laboratory. Samples from Oklahoma are often delivered directly to the FADL. When a sample arrives at the lab, the date received and the lab identification number are added to the sample’s submission form, which is filed for record-keeping. The lab identification numbers contain 5-6 digits, are numbered sequentially in the order received at the lab, and represent information including (from left to right): Letter M (Manure; note some samples include M and others do not because “M” was omitted when entered into the database); first or second number (1-10 or 20) stands for the year; and the last 4 numbers in the lab number are the order the sample was logged in at the FADL. The dataset also includes columns for the year and date received.Using a scoop or spatula, the bulk manure sample (as received) is split into two representative subsamples (~100 mL or cm3 each) and placed into plastic bags. The subsamples are refrigerated at 4°C until further analysis. One of the subsamples is homogenized and ground using a coffee bean grinder for pH, electrical conductivity, and total nutrient analysis. The second subsample remains unaltered (as-received) and is used for moisture determination and water-extractable phosphorus (WEP) analysis. A homogenized, ground subsample was initially used for WEP, but starting in 2009, the unaltered, “as-received” sample has been used for WEP analysis. The change was made because of speculation that homogenizing the subsample increased the WEP, and the research performed to develop the Arkansas P index used unaltered, “as-received” litter. Any remaining bulk sample is stored at room temperature until analysis is complete and the results are reported to the client. The FADL has participated in the Minnesota Manure Proficiency Program (https://www.mda.state.mn.us/pesticide-fertilizer/certified-testing-laboratories-manure-soil) as part of the quality assurance and control program since 2005.The database includes two columns for WEP data (i.e., Arkansas WEP and Universal WEP). Water-extractable P was originally performed using the 10:1 water/litter (v:w) ratio, identified as the Arkansas method (Wolf et al., 2009). The Universal WEP method (Spargo, 2022; Wolf et al., 2009) is now used to determine water-extractable nutrients in manure samples. The Arkansas WEP method was used on poultry litter samples through 2009 since this was required for samples submitted from the Eucha-Spavinaw watershed (Sharpley et al., 2009; 2010). Beginning in 2010, the laboratory switched WEP analyses to the Universal WEP method. The Universal water-extraction method (100:1) is the only method used for the determination of water-extractable potassium (WEK).The counties and states of sample origin were not recorded in the original poultry litter dataset but were added for samples submitted beginning 1 January 2023. The county and state details were added to random samples that were checked for accuracy of analytical information. Please note that even when the county of litter origin is provided, it may not be accurate since the county of Extension office that received the sample may not be consistent with the county of production. Information included in the column identified as “Clients” has two levels: “ESWMT” (Eucha-Spavinaw Watershed Management Team) and “Other”. Samples with the client identified as ESWMT were submitted from poultry farms located within the Eucha-Spavinaw watershed (DeLaune et al., 2006; Sharpley et al., 2009). The ESWMT label identified these samples for the analysis requirements set by the watershed regulations, requiring all poultry litter samples be analyzed for WEP (OCCWQD, 2007).Dairy and Swine Liquid Manure SamplesThe database includes dairy and swine manure properties and metadata for 678 dairy and 1934 swine samples submitted from 1 January 2007 through 31 December 2024. The dairy and swine data include samples of dry and liquid manure forms. Most samples include geographic origin metadata at the state and county levels. Metadata for dairy and swine sample manure types include Cleanout, Compost, Dry stack, Fresh from floor, Lagoon sludge, Lagoon liquid, Milk wash water, Pit, Holding Pond, Settling basin liquid, Settling basin sludge, Sludge, Tank, Wash water, and Unknown. Sample age metadata should be used with caution since some values are very low (e.g., 1-7 days) and may misrepresent the requested information.Clients are provided with 500 ml (16.9 oz; 73×164 mm D×H: 53 mm cap) leakproof bottles and shipping boxes (Figure 2). Upon delivery, samples are refrigerated until the analyses are completed. The analyses performed were based on client requests and include the percent solids for liquid samples or percent moisture for dry samples.References1. DeLaune, P.B., Haggard, B.E., Daniel, T.C., Chaubey, I., & Cochran, M.J. (2006). The Eucha/Spavinaw phosphorus index: A court mandated index for litter management. J. Soil Water Cons., 61(2), 96–105.2. Key, N., McBride, W.D., Ribaudo, M., & Sneeringer, S. (2011). Trends and developments in hog manure management: 1998-2009. EIB-81. USDA, Econ. Res. Serv., Washington, DC.3. Malone, G.W. (1992). Nutrient enrichment in integrated broiler production systems. Poult. Sci., 71(7), 1117–1122.4. Oklahoma Conservation Commission Water Quality Division (OCCWQD). (2007). Watershed based plan for the lake Eucha/lake Spavinaw watershed. Oklahoma Conservation Commission. https://conservation.ok.gov/wp-content/uploads/2021/07/Eucha_Spavinaw-Watershed-Based-Plan-2009.pdf5. Sharpley, A., Herron, S., West, C., & Daniel, T. (2009). Outcomes of phosphorus-based nutrient management in the Eucha-Spavinaw watershed. In A.J. Franzluebbers (Ed), Farming with grass: Achieving sustainable mixed agricultural landscapes (pp. 192–204). Soil and Water Conservation Society, Ankeny, IA.6. Sharpley, A., Moore, P., VanDavender, K., Daniels, M., Delp, W., Haggard, B., Daniel, T., & Baber, A. (2010). Arkansas phosphorus index. FSA-9531. University of Arkansas Coop. Ext. Serv. https://www.uaex.uada.edu/publications/PDF/FSA-9531.pdf7. Spargo, J.T. (2022). M-6.1 Water extractable phosphorus, 100:1 solution to solids ratio. In M.L. Wilson & S. Cortus (Eds.), Recommended Methods of Manure Analysis (2nd ed., pp. 83–86). University of Minnesota Libraries Publishing, Minneapolis, MN.8. United States Department of Agriculture, Natural Resources Conservation Service (USDA-NRCS). (2012). Chapter 4: Agricultural waste characteristics. In Part 651: Agricultural Waste Management Field Handbook. USDA, Soil Cons. Serv., Washington, DC.9. Wolf, A.M., Moore, P.A., Jr., Kleinman, P.J.A., & Sullivan, D.M. (2009). Water-extractable phosphorus in animal manure and biosolids. In J.L. Kovar & G.M. Pierzynski (Eds.), Methods of Phosphorus Analysis for Soils, Sediments, Residuals, and Waters

Public species occurrence database such as GBIF provides specimen geographical records for global bee distribution, which is an invaluable resource for researchers studying bee diversity and pollination ecology. However, most records are biased toward specimens collected in North America and Europe. On the contrary, bees from Southeast Asia (SEA) are poorly understood and are not well represented in public databases. The Chulalongkorn University Natural History Museum (CUNHM) in Thailand holds a collection of more than 12,000 bee specimens from 4 families across more than 500 localities in the country's 77 provinces.

The initial purpose of this project is to mobilize at least 8,000 Thai bee specimen records deposited at CUNHM and publish in GBIF. Activities include photographing specimens, assigning QR codes, transcribing labels, formatting transcription of the data to enable publication in GBIF.org, mapping species distributions, and holding a workshop to showcase and demonstrate the use of the database.

For long-term sustainability of the project, we aim to establish an accurate and reliable digital bee database for the global audience and researchers whose interest are in pollination biology, conservation, bee taxonomy, and biodiversity informatics in Southeast Asia, a lesser-known area of bee diversity. Research fields in climate change, invasive species, and ecology of pollinators will benefit from this work, since information from tropical Asia is often limited and sometimes inaccessible.

Beside producing and publishing the database to GBIF, this effort provides a template for hosting other biodiversity information hosted and stored in Thailand by the National Science and Technology Development Agency (NSTDA), a partner that is providing matching funds. The processes and methods of digitization of bee records will be disseminated and shared with the country's other research collections, universities, and institutions through workshop and university lectures. Through these outreach activities, we hope to familiarize and educate audiences on how to utilize the data efficiently—both through the database and GBIF—and to persuade them the importance of pollinators to the public.

The Integrated Postsecondary Education Data System (IPEDS) is a system of interrelated surveys conducted annually by the U.S. Department of Education's National Center for Education Statistics (NCES). IPEDS annually gathers information from about 6,400 colleges, universities, and technical and vocational institutions that participate in the federal student aid programs.

Access Database: To eliminate the step of downloading IPEDS separately by survey component or select variables, IPEDS has made available the entire survey data for one collection year in the Microsoft Access format beginning with the 2004-05 IPEDS data collection year. Each database contains the relational data tables as well as the metadata tables that describe each data table, the variable titles, descriptions and variables types. Value codes and value labels are also available for all categorical variables. When downloading an IPEDS Access Database, the file is compressed using WinZip.

This data collection is an expanded version of UNITED STATES SUPREME COURT JUDICIAL DATABASE, 1953-1996 TERMS (ICPSR 9422), encompassing all aspects of United States Supreme Court decision-making from the beginning of the Vinson Court in 1946 to the end of the Warren Court in 1968. Two major differences distinguish the expanded version of the database from the original collection: the addition of data on the decisions of the Vinson Court, and the inclusion of the conference votes of the Vinson and Warren courts. Whereas the original collection contained only the vote as reported in the UNITED STATES SUPREME COURT REPORTS, the expanded database includes all votes cast in conference. Concomitant with the expansion of the database is a shift in its basic unit of analysis. The original collection contained every case in which at least one justice wrote an opinion, and cases without opinions were excluded. This version includes every case in which the Court cast a conference vote, with and without opinions. The justices cast many more votes than they wrote opinions, and hence, the number of Warren Court records in this version increased by more than a factor of two over the original version. As in the original collection, distinct aspects of the Court's decisions are covered by six types of variables: (1) identification variables including case citation, docket number, unit of analysis, and number of records per unit of analysis, (2) background variables offering information on origin of case, source of case, reason for granting cert, parties to the case, direction of the lower court's decision, and manner in which the Court takes jurisdiction, (3) chronological variables covering date of term of court, chief justice, and natural court, (4) substantive variables including multiple legal provisions, authority for decision, issue, issue areas, and direction of decision, (5) outcome variables supplying information on form of decision, disposition of case, winning party, declaration of unconstitutionality, and multiple memorandum decisions, and (6) voting and opinion variables pertaining to the vote in the case and to the direction of the individual justices' votes.

Comprehensive dataset of 245 University libraries in California, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.