The Public Libraries Survey (PLS) is an annual survey of U.S. public libraries. Policymakers and practitioners depend on PLS data to allocate funding and strategically manage libraries. Academics rely on PLS data to conduct original research about public libraries. Data in the PLS come from over 17,000 outlets, and it represents a "gold standard" for national information about public libraries. While the PLS is an invaluable resource for the public library community, other organizations collect data that extends the reach and significance of the PLS. This dataset extends the PLS using information from the Public Library Association (PLA), the Association of Bookmobile and Outreach Services (ABOS), and the U.S. Census Bureau. PLA data comes from Project Outcome, a free toolkit and online resource for public libraries to document the outcomes associated with public library services. Since 2015, Project Outcome has collected more than 390,000 responses to surveys at 2,200+ libraries in the U.S. and Canada describing the outcomes resulting from library services. The standardized surveys used by Project Outcome have enabled libraries to aggregate their outcome data and analyze trends by topic, type, and program. ABOS data comes from a 2023 national, non-representative survey of public libraries regarding their outreach departments, services, and vehicles. Census data is from the American Community Survey and provides demographic information regarding the geographies that public libraries serve. As part of an Institute of Museum and Library Services grant, the Inter-university Consortium for Political and Social Research curated these data for reuse and mapped them to libraries in the PLS. The result is a combined dataset that documents the impact of library programming and outreach on nationwide communities. To enhance these data, a committee led by the University of Missouri, School of Information Science and Learning Technologies identified supplemental variables (e.g., Census demographic figures) and guided data curation by creating a "data module" specifying curation enhancements.

This dataset contains data from the National Center for Education Statistics' Academic Library Survey, which was gathered every two years from 1996 - 2014, and annually in IPEDS starting in 2014 (this dataset has continued to only merge data every two years, following the original schedule). This data was merged, transformed, and used for research by Starr Hoffman and Samantha Godbey.This data was merged using R; R scripts for this merge can be made available upon request. Some variables changed names or definitions during this time; a view of these variables over time is provided in the related Figshare Project. Carnegie Classification changed several times during this period; all Carnegie classifications were crosswalked to the 2000 classification version; that information is also provided in the related Figshare Project. This data was used for research published in several articles, conference papers, and posters starting in 2018 (some of this research used an older version of the dataset which was deposited in the University of Nevada, Las Vegas's repository).SourcesAll data sources were downloaded from the National Center for Education Statistics website https://nces.ed.gov/. Individual datasets and years accessed are listed below.[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries Survey (ALS) Public Use Data File, Library Statistics Program, (2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/surveys/libraries/aca_data.asp[dataset] U.S. Department of Education, National Center for Education Statistics, Institutional Characteristics component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Enrollment component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Human Resources component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Employees Assigned by Position component, Integrated Postsecondary Education Data System (IPEDS), (2004, 2002), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Staff component, Integrated Postsecondary Education Data System (IPEDS), (1999, 1997, 1995), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7

This dataset provides information on 18,791 in United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

To determine the extent to which research data services (RDS) are supported in academic libraries and how that has changed over a decade, in 2019 a research team led by Carol Tenopir at the University of Tennessee Center for Information and Communication Studies, in collaboration with ACRL-Choice, surveyed academic library directors in the United States and Canada. This survey allowed us to compare results with a similar survey conducted in 2012. The goal of both studies was to discover the types of data services offered, the staffing deployed or anticipated for such services, the training necessary to support RDS, and RDS plans for the future.

The associated white paper can be accessed at http://www.choice360.org/librarianship/whitepaper and downloaded at https://www.research.net/r/CHOICERDSWP

Methods The data was collected through QuestionPro, hosted by the University of Tennessee, Knoxville. It was then exported into SPSS.

In Newcastle libraries we are endeavouring to open up as much of our data as possible. We will publish data here on a regular basis.

Each file is saved in CSV format and has an accompanying text file detailing what data is contained in each file, who is responsible for it and when it was last updated.

If there is any additional data you would like us to release then please contact Luke Burton (luke.burton@newcastle.gov.uk) to discuss.

You are under no obligation to do so, but since we know you will make great things with our data we would love for you to tell us about them.

Additional information

To the extent possible under law, Newcastle Libaries has waived all copyright and related or neighbouring rights to its data published below. This work is published from: United Kingdom.

For more information please visit: https://www.newcastle.gov.uk/your-council-and-democracy/open-data-and-access-information/open-data/data-sets/libraries-data-sets

Dataset available only to University of Arizona affiliates. To obtain access, you must log in to ReDATA with your NetID. Data is for research use by each individual downloader only. Sharing and/or redistribution of any portion of this dataset is prohibited.The ReferenceUSA datasets from Data Axle (formerly Infogroup) contain establishment-level data about US businesses in annual snapshots from 1997-2021 and can help users create marketing plans and conduct competitive analyses. Each year has 53-94 indicators, with documentation for layouts found in Documentation.zip. Codebooks for 2015, 2017, and 2019 are missing, but all file layouts for 2014-2021 are identical.The University of Arizona University Libraries also subscribe to Data Axle Reference Solutions which provides this data in a searchable, online database with historical data available going back to 2003.NOTE: The uncompressed datasets are very large.Detailed file descriptions and MD5 hash values for each file can be found in the README.txt file.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Context

Increasing numbers of public libraries are eliminating overdue fines for their patrons. The American Library Association passed a resolution in 2019 recognizing that the “imposition of monetary library fines creates a barrier to the provision of library and information services” (p. 2). Using data retrieved from both the Urban Libraries Council and End Library Fines in August 2019, we found that the number of libraries instituting fine-free policies has nearly doubled each year since 2015. How might such a policy change affect patrons, circulation, and library budgets?

Content

In January and February 2020, we submitted FOIA requests using MuckRock's platform to 52 public libraries who have eliminated fines in the United States. We requested information related to patrons, circulation, and budgets. The COVID-19 pandemic hindered our ability to retrieve data, but we were able to gather information from some of the libraries. This dataset contains total circulation data by month for 24 months pre-fine-elimination and 12 months post-fine-elimination for 16 public libraries in the U.S.

Acknowledgements

We wouldn't be here without the generous help of many members of the public library community. Thank you to all the libraries who assisted with our data collection efforts.

This research was funded in part by a grant from Kaggle.

A catalog of the holdings (i.e. books, documents, journals, etc) in all the EPA libraries. It is available to the public via epa.gov. In addition to being an online catalog, it also has modules that support the various operations of the libraries (circulation, serials management, dispersal tracking) that are only accessible by authorized users.

Please read metadata for additional information (https://matterhorn.co.pierce.wa.us/GISmetadata/pdbis_libraries.html). Any data download constitutes acceptance of the Terms of Use.

This data release provides the U.S. Geological Survey (USGS) Spectral Library Version 7 and all related documents. The library contains spectra measured with laboratory, field, and airborne spectrometers. The instruments used cover wavelengths from the ultraviolet to the far infrared (0.2 to 200 microns). Laboratory samples of specific minerals, plants, chemical compounds, and man-made materials were measured. In many cases, samples were purified, so that unique spectral features of a material can be related to its chemical structure. These spectro-chemical links are important for interpreting remotely sensed data collected in the field or from an aircraft or spacecraft. This library also contains physically-constructed as well as mathematically-computed mixtures. Measurements of rocks, soils, and natural mixtures of minerals have also been made with laboratory and field spectrometers. Spectra of plant components and vegetation plots, comprising many plant types and species with varying backgrounds, are also in this library. Measurements by airborne spectrometers are included for forested vegetation plots, in which the trees are too tall for measurement by a field spectrometer. The related U.S. Geological Survey Data Series publication, "USGS Spectral Library Version 7", describes the instruments used, metadata descriptions of spectra and samples, and possible artifacts in the spectral measurements (Kokaly and others, 2017). Four different spectrometer types were used to measure spectra in the library: (1) Beckman™ 5270 covering the spectral range 0.2 to 3 µm, (2) standard, high resolution (hi-res), and high-resolution Next Generation (hi-resNG) models of ASD field portable spectrometers covering the range from 0.35 to 2.5 µm, (3) Nicolet™ Fourier Transform Infra-Red (FTIR) interferometer spectrometers covering the range from about 1.12 to 216 µm, and (4) the NASA Airborne Visible/Infra-Red Imaging Spectrometer AVIRIS, covering the range 0.37 to 2.5 µm. Two fundamental spectrometer characteristics significant for interpreting and utilizing spectral measurements are sampling position (the wavelength position of each spectrometer channel) and bandpass (a parameter describing the wavelength interval over which each channel in a spectrometer is sensitive). Bandpass is typically reported as the Full Width at Half Maximum (FWHM) response at each channel (in wavelength units, for example nm or micron). The linked publication (Kokaly and others, 2017), includes a comparison plot of the various spectrometers used to measure the data in this release. Data for the sampling positions and the bandpass values (for each channel in the spectrometers) are included in this data release. These data are in the SPECPR files, as separate data records, and in the American Standard Code for Information Interchange (ASCII) text files, as separate files for wavelength and bandpass. Spectra are provided in files of ASCII text format (files with a .txt file extension). In the ASCII files, deleted channels (bad bands) are indicated by a value of -1.23e34. Metadata descriptions of samples, field areas, spectral measurements, and results from supporting material analyses – such as XRD – are provided in HyperText Markup Language HTML formatted ASCII text files (files with .html file extension). In addition, Graphics Interchange Format (GIF) images of plots of spectra are provided. For each spectrum a plot with wavelength in microns on the x-axis is provided. For spectra measured on the Nicolet spectrometer, an additional GIF image with wavenumber on the x-axis is provided. Data are also provided in SPECtrum Processing Routines (SPECPR) format (Clark, 1993) which packages spectra and associated metadata descriptions into a single file (see the linked publication, Kokaly and others, 2017, for additional details on the SPECPR format and freely-available software than can be used to read files in SPECPR format). The data measured on the source spectrometers are denoted by the “splib07a” tag in filenames. In addition to providing the original measurements, the spectra have been convolved and resampled to different spectrometer and multispectral sensor characteristics. The following list specifies the identifying tag for the measured and convolved libraries and gives brief descriptions of the sensors. splib07a – this is the name of the SPECPR file containing the spectra measured on the Beckman, ASD, Nicolet and AVIRIS spectrometers. The data are provided with their original sampling positions (wavelengths) and bandpass values. The prefix “splib07a_” is at the beginning of the ASCII and GIF files pertaining to the measured spectra. splib07b – this is the name of the SPECPR file containing a modified version of the original measurements. The results from using spectral convolution to convert measurements to other spectrometer characteristics can be improved by oversampling (increasing sample density). Thus, splib07b is an oversampled version of the library, computed using simple cubic-spline interpolation to produce spectra with fine sampling interval (therefore a higher number of channels) for Beckman and AVIRIS measurements. The spectra in this version of the library are the data used to create the convolved and resampled versions of the library. The prefix “splib07b_” is at the beginning of the ASCII and GIF files pertaining to the oversampled spectra. s07_ASD – this is the name of the SPECPR file containing the spectral library measurements convolved to standard resolution ASD full range spectrometer characteristics. The standard reported wavelengths of the ASD spectrometers used by the USGS were used (2151 channels with wavelength positions starting at 350 nm and increasing in 1 nm increments). The bandpass values of each channel were determined by comparing measurements of reference materials made on ASD spectrometers in comparison to measurements made of the same materials on higher resolution spectrometers (the procedure is described in Kokaly, 2011, and discussed in Kokaly and Skidmore, 2015, and Kokaly and others, 2017). The prefix “s07ASD_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV95 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1995 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV95_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV96 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1996 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV96_” is at the beginning of the ASCII, and GIF files. s07_AV97 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1997 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV97_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV98 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1998 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV98_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV99 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 1999 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV99_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV00 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2000 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV00_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV01 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2001 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV01_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV05 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2005 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV05_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV06 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2006 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV06_” is at the beginning of the ASCII and GIF files pertaining to this spectrometer. s07_AV09 – this is the name of the SPECPR file containing the spectral library measurements convolved to AVIRIS-Classic with spectral characteristics determined in the year 2009 (wavelength and bandpass values for the 224 channels provided with AVIRIS data by NASA/JPL). The prefix “s07_AV09_” is at the beginning of the ASCII and GIF files pertaining to this

Comprehensive dataset of 1 National libraries in New York, 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.

The Love your Libraries Survey was part of the Townsville City Libraries' review to ensure the services were aligning with community needs. Community members were asked to complete the survey to provide information about their current habits and how they would like to see the library improve to meet their needs.

As one of our most popular downloaded datasets we would love to hear from you about how you are using this dataset and any community benefits and outcomes. You can contact us at opendata@townsville.qld.gov.au

The market for contemporary authors’ archives in the United States began when research libraries needed to cheaply provide sources for the swelling number of students and faculty following World War II. Soon, the demand for contemporary authors’ archives developed into a multimillion-dollar trade. Writers and their families enjoyed their new opportunity to make money, as did the book dealers and literary agents with the foresight to pivot their businesses to serve living authors. For a while, library directors and curators across the American Midwest and West relished their new-found opportunity increase their prestige by building collections that could compete on equal footing against British and Ivy League holdings. But as the twentieth century progressed, and public interest around celebrity writers grew more frenzied, even the most well-funded institutions found acquiring contemporary literary archives had become cost prohibitive. Researchers began to question how papers came to be housed in locales disconnected from authors’ professional and personal lives. Placing Papers: The American Literary Archives Market is the first book to chart how the market for writers’ papers became overheated to explore what happens when tourists, rather than scholars, become the designated audience for literary archives.

Comprehensive dataset of 458 Public libraries in Georgia, 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.

Unit (formerly BIBSYS) offers access to bibliographic data through the Protocol Search and Retrieve via URL (SRU). SRU is a standard search protocol that uses the Contextual Query Language (CQL). The standard has been developed by the Library of Congress and BIBSYS fully supports the protocol. The SRU service can be used to search for BIBSYS Library Base (overview of Norwegian academic libraries’ collections). The service can be used by everyone, but for extensive use and many calls, we ask that you contact us to ensure good performance of the service. The SRU protocol is self-explain. By using the following watch, the service tells what services/indexes it can offer: https://bibsys.alma.exlibrisgroup.com/view/sru/47BIBSYS_NETWORK?version=1.2&operation=explain Example of searching against the Library base with the keyword fur hunter life: http://bibsys.alma.exlibrisgroup.com/view/sru/47BIBSYS_NETWORK?version=1.2&operation=searchRetrieve&recordSchema=marcxml&query=all=pelsjegerliv Example of searching for the Library Base at ISBN: 8205342687: http://bibsys.alma.exlibrisgroup.com/view/sru/47BIBSYS_NETWORK?version=1.2&operation=searchRetrieve&recordSchema=marcxml&query=alma.isbn=8205342687 The information has been last updated 08/01 2020.

Dataset Card for British Library Books

This dataset is the same as https://huggingface.co/datasets/TheBritishLibrary/blbooks, however, this version is stored as parquet to avoid needing to run a datasets script. This also makes loading this dataset much quicker.

  Dataset Summary

This dataset consists of books digitised by the British Library in partnership with Microsoft. The dataset includes ~25 million pages of out of copyright texts. The majority of the texts were… See the full description on the dataset page: https://huggingface.co/datasets/biglam/blbooks-parquet.

Abstract

This dataset was curated for the digital humanities portion of the project "500 Years of Black History in South Florida" by Synatra Smith, Luling Huang, and Portia Hopkins.

Methodology

Data was curated at the U.S. Census Tract level for four counties in South Florida: Broward, Miami-Dade, Monroe, and Palm Beach.

There are two tables in this dataset:

• sociodem_enepov: This table contains several sociodemographic variables and energy burden level at the census tract level;
• airqua: This table contains monthly median air quality data at the census tract level for Year 2016.

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The sociodemographic data come from the American Community Survey (2020 5-year estimates). The variables include fraction of black population, median income, unemployment rate, and four education level variables for population 25 years or above: fraction of population below high school, fraction of population who had high school diploma only, fraction of population who had a college degree or equivalent only, and fraction of population who had a graduate degree. Here are the table numbers and relevant columns from the U.S. Census data portal:

• Education: S1501
• Black population: B01001B (column: B01001B_001E)
• Total population: B01001 (for calculating fraction of black population)
• Median income: S1903 (column: S1903_C03_001E)
• Employment: S2301 (column: S2301_C04_001E)

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The energy burden data come from the U.S. Department of Energy's Low-Income Energy Affordability Data (LEAD) tool. The air quality (PM2.5 concentration) data come from the U.S. Centers for Disease Control and Prevention's Daily Census Tract-Level PM2.5 Concentrations, 2016.

This project is conducted on behalf of the Association for the Study of African American Life and History and the National Park Service with additional funding from the Council on Library and Information Resources.

References

• Centers for Disease Control and Prevention. (n.d.). National Environmental Public Health Tracking Network [Data set]. www.cdc.gov/ephtracking

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• Ma, O. (2018). *Low-Income Energy Affordability Data - LEAD Tool - 2018 Update *[Data set]. U.S. Department of Energy. https://dx.doi.org/10.25984/1784729

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• U.S. Census Bureau. (2023). United States Census Bureau: Data [Data set]. U.S. Department of Commerce. https://data.census.gov/

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Usage

This dataset curates from data existing in the public domain and can be used for other purposes freely with attribution.

The table Building Performance Data is part of the dataset U.S. Building Performance Database, available at https://cmu.redivis.com/datasets/8yz5-3vqbyynqy. It contains 439555 rows across 32 variables.

Much of the world’s data are stored, managed, and distributed by data centers. Data centers re-quire a tremendous amount of energy to operate, accounting for around 1.8% of electricity use in the United States. Large amounts of water are also required to operate data centers, both directly for liquid cooling and indirectly to produce electricity. For the first time, we calculate spatially-detailed carbon and water footprints of data centers operating within the United States, which is home to around one-quarter of all data center servers globally. Our bottom-up approach reveals one-fifth of data center servers direct water footprint comes from moderately to highly water stressed watersheds, while nearly half of servers are fully or partially powered by power plants located within water stressed regions. Approximately 0.5% of total US greenhouse gas emissions are attributed to data centers. We investigate tradeoffs and synergies between data center’s water and energy utilization by strategically locating data centers in areas of the country that will minimize one or more environmental footprints. Our study quantifies the environmental implications behind our data creation and storage and shows a path to decrease the environmental footprint of our increasing digital footprint..

The table AK_Structures is part of the dataset USA Structures, available at https://stanford.redivis.com/datasets/d3cy-a7gxn1165. It contains 298487 rows across 29 variables.