This submission contains an open-source library of transient events in distributed system with high solar PV. The library includes the collected data, related documents and scripts for loading the data. The data library is built for transient event detection and machine learning based analysis algorithm development. The data was collected via both field test and software simulation. The units for the data are included in the data file headers for each data series. A text editor or spreadsheet software, such as Excel, and Matlab is required to view the data.

Location of libraries in York. For further information, please visit Explore York website. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the ""Last Updated"" field of each GIS resource reflects when the data was first published.

Point feature layer of City of San Diego library locations with associated website and contact information, created by the County of San Diego Department of Public Works GIS, in conjunction with San Diego County Library (SDCL).

The Public Libraries data set aggregates individual library services and finance data to the town level. Public libraries provide free borrowing privileges and services to their patrons and receive financial support from local tax funds. Public libraries may be municipal, which are established by and administrative units of local government, or association, which are not units of town government but receive some public funding. Some towns are served by more than one public library. Library visits include all persons entering a library for any purpose, including persons attending meetings or activities and persons requiring no staff assistance. Circulation counts all library materials of all formats lent out for use outside the library, including renewals. Registered borrowers are all town residents to whom a library has issued membership. Reference questions counts all interactions in which library staff provide information, knowledge, or recommendations to patrons. Town tax appropriation indicates the funds allotted to the library's operation budget from the town. The Adjusted Equalized Net Grand List per Capita (AENGLC) measures town wealth based on property tax and income per capita.

The Integrated Library System (ILS) is composed of bibliographic records including inventoried items, and patron records including circulation data. The data is used in the daily operation of the library, including circulation, online public catalog, cataloging, acquisitions, collection development, processing, and serials control. This dataset represents the usage of inventoried items by active patrons. Per the California State Library definition, San Francisco Public Library defines active patrons as a) patrons with unexpired library cards and b) patrons who had circulation activity within the last three years.

Compiled count of database access for all branches, by month, by fiscal year. Numbers represent “any action performed by the user in relation to a content item," i.e., number of times databases were used to access materials such as articles, videos, PDFs, abstracts etc. Updated annually.

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.

Carnegie Library of Pittsburgh locations including address, coordinates, phone number, square footage, and standard operating hours. The map below does not display locations that are temporarily closed due to renovation.

The IRI Data Library is a powerful and freely accessible online data repository and analysis tool that allows a user to view, manipulate, and download over 400 climate-related data sets through a standard web browser. The Data Library contains a wide variety of publicly available data sets, including station and gridded atmospheric and oceanic observations and analyses, model-based analyses and forecasts, and land surface and vegetation data sets, from a range of sources. It includes a flexible, interactive data viewer that allows a user to visualize. multi-dimensional data sets in several combinations, create animations, and customize and download plots and maps in a variety of image formats. The Data Library is also a powerful computational engine that can perform analyses of varying complexity using an extensive array of statistical analysis tools. Online tutorials and function documentation are available to aid the user in applying these tools to the holdings available in the Data Library. Data sets and the results of any calculations performed by the user can be downloaded in a wide variety of file formats, from simple ascii text to GIS-compatible files to fully self-describing formats, or transferred directly to software applications that use the OPeNDAP protocol. This flexibility allows the Data Library to be used as a collaborative tool among different disciplines and to build new data discovery and analysis tools.

This dataset reports lending of materials between Asotin County (WA) Library District and other libraries. The library district reports the figures annually to the Public Libraries Survey, administered by the Washington State Library in cooperation with the U.S. Institute of Museum and Library Services. For a detailed glossary of terms and more information about the survey, visit the Statistics page at the Washington State Library: https://www.sos.wa.gov/library/libraries/libdev/publications.aspx#WAStats

Across the country, public land managers make hundreds of decisions each year that influence landscapes and ecosystems within the lands they manage. Many of these decisions involve vegetation manipulations known as land treatments. Land treatments include activities such as removal or alteration of plant biomass, seeding burned areas, and herbicide applications. Data on these land treatments historically have been stored at local offices and gathering information across large spatial areas was difficult. These valuable data needed to be centralized and stored for Federal agencies involved in land treatments because these data are useful to land managers for policy and management and to scientists for developing sampling designs and studies. In 2008, the Land Treatment Digital Library (LTDL) was created by the U.S. Geological Survey (USGS) to catalog information about land treatments on federal lands in the western United States. The flexible framework of the library allows for the storage of a wide variety of data in different formats. The library contains data in text, tabular, spatial, and image formats. Specific examples include project plans and implementation reports, monitoring data, spatial data files from geographic information systems, digitized paper maps, and digital images of land treatments. The data are entered by USGS employees and are accessible through a searchable website. The LTDL can be used to respond to information requests, conduct analyses and other forms of information syntheses, produce maps, and generate reports for federal managers, scientists, and other authorized users. This data release includes the most up to date data available in the LTDL at the time of release. However, most field offices were last visited to collect their comprehensive treatment data between 2011-2014. Users should be aware that while treatments may exist in some field offices past the date of last collection, it is not a comprehensive representation of land treatments that have occurred on BLM lands during the most recent time span. Offices in southern Idaho and eastern Oregon were revisited in the winter of 2019 and the data collected during those visits are available in this release. Post wildfire emergency stabilization and rehabilitation treatments are included for fires up to 2021.

University of Pittsburgh librarians at the Health Sciences Library System and the University Library System conducted an 18-question online survey to learn what roles other academic and health sciences libraries are playing at their institutions in providing services and support to their users regarding electronic lab notebooks (ELNs). The survey was administered via Qualtrics. Questions included self-identification of being a health sciences library, whether their university offers an enterprise ELN license, if so which one and when did they start offering it, involvement in the selection process, types of services provided, service utilization, library staff involvement and workload, and whether other units at the university provide support. Questions were a mix of multiple choice and free text. Survey logic was used so depending on their answers respondents did not see all questions.Participants were recruited from September 7, 2017 through October 6, 2017. An email message and reminder were targeted to numerous library listservs requesting participation from ONLY academic libraries, including those in the health sciences. The email also stated "The information gathered will be used in developing our service model, and we also expect to incorporate it into one or more presentations or articles for publication. Although we ask for information identifying your library/institution, it will only be used for data analysis purposes. No library/institution will be identified publicly or linked to any particular response." We therefore removed any identifying responses from the data response spreadsheet posted here. Q1 and Q9 were removed, and identifying information within Q10, Q13, and Q18 was redacted.The CSV file contains de-identifed survey responses (indicated as #####). We did not include incomplete data (surveys with the majority of questions unanswered) and those responses that did not meet inclusion criteria of an academic/medical library. Also included is a PDF of the survey questions. This data was described here: Iwema, C.L. and Ratajeski, M.A. (2018, May). Creating New Research Services: Library Support for Electronic Lab Notebooks. Paper presented at Medical Library Association Annual Conference, Atlanta, GA.

Survey responses from e-resource managers at Swedish library organizations affected by the Swedish Elsevier cancellation

Stations and a table of download links for time-series data, from DWR's continuous environmental monitoring database. For more information, see DWR's Water Data Library, continuous data section: https://wdl.water.ca.gov/ContinuousData.aspx, where this data is also available.

The library patronage indicator measures the percentage of the total resident population served by each public library (the percentage of eligible residents that holds an unexpired library card). Ten public libraries and public library districts in Champaign County are included: the Champaign Public Library, the Homer Community Library, the Mahomet Public Library District, the Ogden Rose Public Library, the Philo Public Library District, the Rantoul Public Library, the St. Joseph Township-Swearingen Memorial Library, the Sidney Community Library, the Tolono Public Library District, and the Urbana Free Library. Public libraries often serve as community hubs and offer a number of educational and social opportunities and services for their population served. Registration for and maintenance of a library card is one way a resident can engage in recreation and other community involvement.

In 2021, five of the ten libraries analyzed had residential participation rates between 20 and 30 percent: Champaign Public Library, 27.57 percent; St. Joseph Township-Swearingen Memorial Library, 25.12 percent; Mahomet Public Library District, 22.38 percent; Tolono Public Library District, 21.82 percent; and the Philo Public Library District, 21.3 percent.

The libraries with the greatest percentage of the resident population with unexpired library cards were the Homer Community Library, at 38.96 percent, and the Urbana Free Library at 30 percent. The libraries with the smallest percentage of the resident population with unexpired library cards were the Sidney Community Library, 18.13 percent; Rantoul Public Library, 17.22 percent; and the Ogden Rose Public Library, at 13.85 percent.

All ten public libraries in Champaign County saw the percentage of their resident population with unexpired library cards decrease between 2015 and 2021. It is worth noting that many library buildings were closed during part of 2020 due to the COVID-19 pandemic, and that along with statewide stay-at-home orders may have deterred residents from renewing or obtaining library cards.

The release of the 2020 Census results in 2021 shows that the population in eight of the ten library districts decreased from 2010 to 2020. It is important to note that the population of a library district sometimes differs than the population of the municipality where it is located (e.g., Tolono).

The two library districts that saw a population increase in 2020 were the Champaign Public Library and Tolono Public Library District. However, the number of unexpired library cards in those districts decreased in 2021, so the decrease in the percentage of the population with library cards cannot be explained by population growth.

The two library districts that saw an increase in the percentage of the population with library cards from 2020 to 2021 are the Homer Community Library and Urbana Free Library. The number of unexpired library cards at the Homer Community Library increased from 2020 to 2021, which explains the percentage increase. However, the number of unexpired library cards at the Urbana Free Library decreased from 2020 to 2021, so the percentage increase is due to the library district’s population decrease.

Data was sourced from the Illinois Public Library Annual Report (IPLAR), an annual report from the Illinois State Library and Office of the Illinois Secretary of State. The population data included in the IPLAR dataset is sourced from the 2020 Census. To be consistent with the data source, we have also calculated the percentage of residents with library cards based on the number of cardholders divided by the total 2020 Census population.

Source: Illinois State Library, Office of the Illinois Secretary of State.

The monthly optimum interpolation (OI) fields are derived by a linear interpolation of the weekly OI fields to daily fields then averaging the daily values over a month. The monthly fields are in the same format and spatial resolution as the weekly fields.

The OI sea surface temperature (SST) analysis is produced weekly on a one-degree grid. The analysis uses in situ and satellite SST's plus SST's simulated by sea-ice cover. Before the analysis is computed, the satellite data is adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). A description of the OI analysis can be found in Reynolds and Smith (1994). The bias correction improves the large scale accuracy of the OI. Examples of the effect of recent corrections is given by Reynolds (1993).

For the more recent period, 1990-present, the in situ data were obtained from radio messages carried on the Global Telecommunication System. The satellite observations were obtained from operational data produced by the National Environmental Satellite, Data and Information Service (NESDIS).

During the period 1981-1989, the in situ data were obtained from the Comprehensive Ocean Atmosphere Data Set (COADS) for the 1980s. These data (see Slutz, et al., 1985, and Woodruff, et al., 1993) consist of logbook and radio reports. The satellite data were obtained from analyses of NESDIS data produced at the University of Miami's Rosentiel School of Marine and Atmospheric Sciences.

The OI analysis is done over all ocean areas. There is no analysis over land. The land values are filled by a Cressman interpolation to produce a complete grid for possible interpolation.

Data from the Joint World Meteorological Organization/Intergovernmental Oceanographic Commission Technical Commision for Oceanography and Marine Meteorology (JCOMM) Products Bulletin Data Products. The organization was formally known as the Integrated Global Ocean Services System (IGOSS) Data Products Bulletin.

For further data products see: "http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/"

"http://ingrid.ldeo.columbia.edu/SOURCES/.IGOSS/.data_products.html"

and "http://iri.ldeo.columbia.edu/climate/monitoring/ipb/"

The State Library of Oregon collects annual service measures, financial data, and other statistics from all legally-established public libraries in the state, as per Oregon Revised Statue 357.520 (Annual report). The data reporting period matches the state fiscal year (July 1 through June 30). This dataset includes all Oregon Public Library Statistical Report data from each year starting in FY2009-2010, and is updated annually. Reporting periods are identified as the year the report was submitted (i.e., FY2009-2010 data is identified as 2010 in the Year column).

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

Update frequency: Datasets are refreshed every night to ensure the most current information is available. Even if there are no changes, the data will be updated nightly

Dataset contains Milwaukee Public Library branch names, locations and hours of operation.

Shapefile is projected in Wisconsin State Plane South NAD27 (WKID 32054)

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

Through collecting 16 relatively small-scale motion datasets and conducting a series of in-lab expreiment, we established a 3D skeleton dataset for recognizing construction worker actions. All skeleton data were processed in four major steps, including uniform data extraction, skeleton structure alignment, resampling, and coordination transformation. Then all the aligned skeleton data will be manually annotated into four activity categories and assigned with labels. Experiment version: It contains over 61,275 samples (10 million frames) from 73 classes performed by about 300 different subjects.The dataset includes four fundamental categories of activities, including Production Activities(12), Unsafe Activities(38), Awkward Activities(10), and Common Activities(13).
However, We have carefully reviewed the licenses of all the current datasets. We found more than half of the datasets did not specify their licenses and usage policy. Therefore, in this version, we only shared the tagged and processed dataset that clearly allows redistribution and modification. For the rest of the datasets, we highlighted their URL and doi (all of them are publicly accessible and free for use). Instead of providing the processed data, we public the full preprocess codes on GitHub, which could be used to retag and process (such as converting to predefined .bvh files). All readers and users could process the source dataset by themselves. Public version： Construction Motion Data Library(CML) contains 6131 samples(ALL_DATA); among them, and 4333 samples are highly related to construction activities ( Construction_Related_Data). GitHub: https://github.com/YUANYUAN2222/Integrated-public-3D-skeleton-form-CML-library.