Abstract copyright UK Data Service and data collection copyright owner. This is a mixed methods data collection. The main purpose of the research was to understand what users and non-users of public libraries want from the service. Although detailed data exist on the level and frequency of public library use across the population, there was less information on how people regard the quality of service, and on their reasons for using or not using public libraries. If the library service is to retain existing users and encourage new visitors, it is vital to understand the expectations and experiences of the public, irrespective of whether people currently use public libraries or not. The study aimed to provide an up-to-date picture of what the public wants and values in library services, and help leaders to make decisions about the future development of the service. The data were collected in two different phases: Qualitative: This consisted of a series of focus groups across England with a diverse range of people who had differing attitudes to public libraries. These groups examined all aspects of people's relationships with local public libraries and crucially allowed the researchers to explore many issues not covered satisfactorily by existing data. Quantitative: This took the form of a telephone survey with a representative sample of the adult population of England. This phase's principal focus was to corroborate the findings from the qualitative phase and identify further reasons behind usage or not of libraries and the factors that would encourage people to visit libraries more often. Further information is available on the Museums, Libraries and Archives Council's (MLA) Research Evidence Resources website.

This dataset displays the locations of all the public libraries in the state of New Jersey. The data included is the name of the library, name of the library system, library's address, phone, and lat/lon coordinates. The data came from publiclibraries.com which is a updated directory of all the public libraries throughout the United States.

Using public libraries from the Institute of Museum and Library Services, via its Public Libraries Survey for 2016, this map shows the population growth or decline within 1 mile's walk of each library. The libraries were downloaded from the PLS site and added as a layer in ArcGIS Online. The layer was next enriched with Esri then-current year population estimates (2017) using an analysis tool in ArcGIS Online, and symbolized based on growth or decline of population within a short walk of each library. Citation: Pelczar, M., Frehill, L. M., Williams, K., Wan, C., & Nielsen, E. (2018). Data File Documentation: Public Libraries in the United States Fiscal Year 2016. Institute of Museum and Library Services: Washington, D.C.

A dataset showing the number of active library users by age band. An active library user is defined as someone who has borrowed/renewed a book or used a library computer within the last year

The Public Libraries Survey, Fiscal Year 2002 (PLS FY2002), is a study that is part of the Library Statistics program. PLS FY2002 (https://www.imls.gov/research/public_libraries_in_the_united_states_survey.aspx) is a cross-sectional survey that collects annual descriptive data on the universe of public libraries in the U.S. and the Outlying Areas. Information such as public service hours per year, circulation of library books, etc., number of librarians, population of legal service area, expenditures for library collection, staff salary data, and access to technology are collected. The study was conducted using paper surveys. The key respondents in this study were state library agencies. The study's response rate was 98.1 percent. The key statistics produced from PLS FY2002 were about service measures such as access to the Internet, number of users of electronic resources, other electronic services, number of Internet terminals used by staff only, number of Internet terminals used by the general public, reference transactions, public service hours, interlibrary loans, circulation, library visits, children's program attendance, and circulation of children's materials. It also includes information about size of collection, staffing, operating income and expenditures, type of geographic service area, type of legal basis, type of administrative structure, and number and type of public library service outlets.

This dataset illustrates the number of public libraries by state. Shown here are more specifically the number of central libraries and the number of branches from the central libraries. Source: Public Libraries 2005 URL: http://nces.ed.gov/programs/stateprofiles/index.asp Date Accessed: November 6, 2007

The Public Libraries Survey, Fiscal Year 2007 (PLS FY2007), is a study that is part of the Library Statistics program. PLS FY2007 (https://www.imls.gov/research/public_libraries_in_the_united_states_survey.aspx) is a cross-sectional study that includes information on population of legal service area, service outlets, public service hours, library materials, total circulation, circulation of children�s materials, reference transactions, library visits, children�s program attendance, interlibrary loans, electronic services and information, full-time-equivalent staff, operating revenue and expenditures, and capital expenditures. The study was conducted using paper surveys, web-based surveys, and email. The key respondents in this study were state library agencies. The study's response rate was 97.7 percent. The key statistics produced from PLS FY2007 were about service measures such as access to the Internet, number of users of electronic resources, number of internet terminals used by the general public, reference transactions, public service hours, interlibrary loans, circulation, library visits, children�s program attendance, and circulation of children�s materials. It also includes information about size of collection, staffing, operating revenue and expenditures, type of geographic service area, type of legal basis, type of administrative structure, number and type of public library service outlets, and square footage of outlets.

This dataset contains information about Norfolk Public Library active users by month starting in July 2016. This information will be updated annually. Active users include users whose accounts have had any activity during the previous three years including card renewal, fines added, payments made, or computer usage. This dataset is updated monthly.

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

A dataset showing the number of active library users by age band. An active library user is defined as someone who has borrowed/renewed a book or used a library computer within the last year

This dataset displays the locations of all the public libraries in the state of Arkansas. The data included is the name of the library, name of the library system, library's address, phone, and lat/lon coordinates. The data came from publiclibraries.com which is a updated directory of all the public libraries throughout the United States.

IFLA stands for The International Federation of Library Associations and Institutions. The IFLA World Library and Information Congress 2016 and 2nd IFLA General Conference and Assembly, ‘Connections. Collaboration. Community’ took place 13–19 August 2016 at the Greater Columbus Convention Center (GCCC) in Columbus, Ohio, United States. The official hashtag of the conference was #WLIC2016.This spreadsheet contains the results of a text analysis of 22327 Tweets publicly labeled with #WLIC2016 between Sunday 14 and Thursday 18 August 2015. The collection of the source dataset was made with a Twitter Archiving Google Spreadsheet and the automated text analysis was done with the Terms tool from Voyant Tools. The spreadsheet contains:A sheet containing a table summarising the source archive A sheet containing a table detailing tweet counts per day. Sheets containing the 'raw' (no stop words, no manual refining) tables of top 300 most frequent terms and their counts for the Sun-Thu corpus and each individual corpus (1 per day).Sheets containing the 'edited' (edited English stop word filter applied, manually refined) tables of top 50 Most frequent terms and their counts for the Sun-Thu corpus and each individual corpus (1 per day).A sheet containing a comparison table of the top 50 per day.Other ConsiderationsOnly Tweets published by accounts with at least one follower were included in the source archive.Both research and experience show that the Twitter search API is not 100% reliable. Large Tweet volumes affect the search collection process. The API might "over-represent the more central users", not offering "an accurate picture of peripheral activity" (González-Bailon, Sandra, et al, 2012).Apart from the filters and limitations already declared, it cannot be guaranteed that each and every Tweet tagged with #WLIC2016 during the indicated period was analysed. The dataset was shared for archival, comparative and indicative educational research purposes only.Only content from public accounts, obtained from the Twitter Search API, was analysed. The source data is also publicly available to all Twitter users via the Twitter Search API and available to anyone with an Internet connection via the Twitter and Twitter Search web client and mobile apps without the need of a Twitter account.This file contains the results of analyses of Tweets that were published openly on the Web with the queried hashtag; the source Tweets are not included. The content of the source Tweets is responsibility of the original authors. Original Tweets are likely to be copyright their individual authors but please check individually. This work is shared to archive, document and encourage open educational research into scholarly activity on Twitter. The resulting dataset does not contain complete Tweets nor Twitter metadata. No private personal information was shared. The collection, analysis and sharing of the data has been enabled and allowed by Twitter's Privacy Policy. The sharing of the results complies with Twitter's Developer Rules of the Road. A hashtag is metadata users choose freely to use so their content is associated, directly linked to and categorised with the chosen hashtag. The purpose and function of hashtags is to organise and describe information/outputs under the relevant label in order to enhance the discoverability of the labeled information/outputs (Tweets in this case). Tweets published publicly by scholars or other professionals during academic conferences are often publicly tagged (labeled) with a hashtag dedicated to the conference in question. This practice used to be the confined to a few 'niche' fields; it is increasingly becoming the norm rather than the exception. Though every reason for Tweeters' use of hashtags cannot be generalised nor predicted, it can be argued that scholarly Twitter users form specialised, self-selecting public professional networks that tend to observe scholarly practices and accepted modes of social and professional behaviour. In general terms it can be argued that scholarly Twitter users willingly and consciously tag their public Tweets with a conference hashtag as a means to network and to promote, report from, reflect on, comment on and generally contribute publicly to the scholarly conversation around conferences. As Twitter users, conference Twitter hashtag contributors have agreed to Twitter's Privacy and data sharing policies. Professional associations like the Modern Language Association and the American Pyschological Association recognise Tweets as citeable scholarly outputs. Archiving scholarly Tweets is a means to preserve this form of rapid online scholarship that otherwise can very likely become unretrievable as time passes; Twitter's search API has well-known temporal limitations for retrospective historical search and collection.Beyond individual Tweets as scholarly outputs, the collective scholarly activity on Twitter around a conference or academic project or event can provide interesting insights for the contemporary history of scholarly communications. Though this work has limitations and might not be thoroughly systematic, it is hoped it can contribute to developing new insights into a discipline's public concerns as expressed on Twitter over time.As it is increasingly recommended for data sharing, the CC-0 license has been applied to the resulting output in the repository. It is important however to bear in mind that some terms appearing in the dataset might be licensed individually differently; copyright of the source Tweets -and sometimes of individual terms- belongs to their authors. Authorial/curatorial/collection work has been performed on the shared file as a curated dataset resulting from analysis, in order to make it available as part of the scholarly record. If this dataset is consulted attribution is always welcome.Ideally for proper reproducibility and to encourage other studies the whole archive dataset should be available. Those wishing to obtain the whole Tweets should still be able to get them themselves via text and data mining methods.

Monthly number of unique users who connected to Newcastle Libraries Wi-Fi from April 2019 to present. Additional notes No login is required for the Wi-Fi; users do not need to be library members to connect to the network. There is no data for Walker Library as it does not have its own Wi-Fi; citizens visiting the library use the Walker Activity Dome Wi-Fi network. Only partial data is available for April 2019.

This dataset displays the locations of all the public libraries in the state of Rhode Island. The data included is the name of the library, name of the library system, library's address, phone, and lat/lon coordinates. The data came from publiclibraries.com which is a updated directory of all the public libraries throughout the United States.

The dataset contains the complete list from 2013 to 2015 of the District Library System of the city of Milan. The following information is reported for each library: library holdings, number of subscribers (adults and children), loans, internet service with number of workstations, subscribers and sessions. Note: the data relating to the Accursio library are partial due to the fire which seriously damaged it. The path to use to find the original dataset on sisi.comune.milano.it is: sisi.comune.milano.it - ​​Education and Culture - Culture - Libraries - Districts and Sormani

The dataset contains the complete list of the District Library System of the city of Milan. The following information is reported for each library: library holdings, number of members (adults and children), loans. The path to use to find the original dataset on sisi.comune.milano.it is: sisi.comune.milano.it - ​​Education and Culture - Culture - Libraries - Districts and Sormani

This is a database (parquet format) containing publicly available multiple cause mortality data from the US (CDC/NCHS) for 2014-2022. Not all variables are included on this export. Please see below for restrictions on the use of these data imposed by NCHS. You can use the arrow package in R to open the file. See here for example analysis; https://github.com/DanWeinberger/pneumococcal_mortality/blob/main/analysis_nongeo.Rmd . For instance, save this file in a folder called "parquet3":

library(arrow)

library(dplyr)

pneumo.deaths.in <- open_dataset("R:/parquet3", format = "parquet") %>% #open the dataset
filter(grepl("J13|A39|J181|A403|B953|G001", all_icd)) %>% #filter to records that have the selected ICD codes
collect() #call the dataset into memory. Note you should do any operations you canbefore calling 'collect()" due to memory issues

The variables included are named: (see full dictionary:https://www.cdc.gov/nchs/nvss/mortality_public_use_data.htm)

year: Calendar year of death

month: Calendar month of death

age_detail_number: number indicating year or part of year; can't be interpreted itself here. see agey variable instead

sex: M/F

place_of_death:

Place of Death and Decedent’s Status
Place of Death and Decedent’s Status
1 ... Hospital, Clinic or Medical Center
- Inpatient
2 ... Hospital, Clinic or Medical Center
- Outpatient or admitted to Emergency Room
3 ... Hospital, Clinic or Medical Center
- Dead on Arrival
4 ... Decedent’s home
5 ... Hospice facility
6 ... Nursing home/long term care
7 ... Other
9 ... Place of death unknown

all_icd: Cause of death coded as ICD10 codes. ICD1-ICD21 pasted into a single string, with separation of codes by an underscore

hisp_recode: 0=Non-Hispanic; 1=Hispanic; 999= Not specified

race_recode: race coding prior to 2018 (reconciled in race_recode_new)

race_recode_alt: race coding after 2018 (reconciled in race_recode_new)

race_recode_new:

1='White'

2= 'Black'

3='Hispanic'

4='American Indian'

5='Asian/Pacific Islanders'

agey:

age in years (or partial years for kids <12months)

https://www.cdc.gov/nchs/data_access/restrictions.htm

Please Read Carefully Before Using NCHS Public Use Survey Data

The National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention (CDC), conducts statistical and epidemiological activities under the authority granted by the Public Health Service Act (42 U.S.C. § 242k). NCHS survey data are protected by Federal confidentiality laws including Section 308(d) Public Health Service Act [42 U.S.C. 242m(d)] and the Confidential Information Protection and Statistical Efficiency Act or CIPSEA [Pub. L. No. 115-435, 132 Stat. 5529 § 302]. These confidentiality laws state the data collected by NCHS may be used only for statistical reporting and analysis. Any effort to determine the identity of individuals and establishments violates the assurances of confidentiality provided by federal law.

Terms and Conditions

NCHS does all it can to assure that the identity of individuals and establishments cannot be disclosed. All direct identifiers, as well as any characteristics that might lead to identification, are omitted from the dataset. Any intentional identification or disclosure of an individual or establishment violates the assurances of confidentiality given to the providers of the information. Therefore, users will:

1. Use the data in this dataset for statistical reporting and analysis only.

1. Make no attempt to learn the identity of any person or establishment included in these data.

1. Not link this dataset with individually identifiable data from other NCHS or non-NCHS datasets.

1. Not engage in any efforts to assess disclosure methodologies applied to protect individuals and establishments or any research on methods of re-identification of individuals and establishments.

By using these data you signify your agreement to comply with the above-stated statutorily based requirements.

Sanctions for Violating NCHS Data Use Agreement

Willfully disclosing any information that could identify a person or establishment in any manner to a person or agency not entitled to receive it, shall be guilty of a class E felony and imprisoned for not more than 5 years, or fined not more than $250,000, or both.

Taken from "Classifying document types to enhance search and recommendations in digital libraries"https://www.overleaf.com/read/zzzrvmzmwdckAbstract: In this paper, we address the problem of classifying documents available from the global network of (open access) repositories according to their type. We show that the metadata provided by repositories enabling us to distinguish research papers, thesis and slides are missing in over 60% of cases. While these metadata describing document types are useful in a variety of scenarios ranging from research analytics to improving search and recommender (SR) systems, this problem has not yet been sufficiently addressed in the context of the repositories infrastructure. We have developed a new approach for classifying document types using supervised machine learning based exclusively on text specific features. We achieve 0.96 F1-score using the random forest and Adaboost classifiers, which are the best performing models on our data. By analysing the SR system logs of the CORE digital library aggregator, we show that users are an order of magnitude more likely to click on research papers and thesis than on slides. This suggests that using document types as a feature for ranking/filtering SR results in digital libraries has the potential to improve user experience.The descriptors, as featured in the study, are encoded in the dataset as follows:authors_len: Number of authors associated with the document entry.num_of_pages: Number of pages the document has in total.avg_word_per_page: Average words per page in the document.total_words: Total words in the document.source: The online service from which the document originated (can be either "CORE" or "SlideShare").id: Identifier with which the source's API can be queried to retrieve the corresponding document.label: The document's type, from "research", "thesis" or "slides".

This dataset contains information about Norfolk Public Library active users by month starting in July 2016. This information will be updated annually. Active users include users whose accounts have had any activity during the previous three years including card renewal, fines added, payments made, or computer usage. This dataset is updated monthly.

This dataset displays the locations of all the public libraries in the state of South Carolina. The data included is the name of the library, name of the library system, library's address, phone, and lat/lon coordinates. The data came from publiclibraries.com which is a updated directory of all the public libraries throughout the United States.