The QoG Institute is an independent research institute within the Department of Political Science at the University of Gothenburg. The main objective of our research is to address the theoretical and empirical problem of how political institutions of high quality can be created and maintained.

To achieve said goal, the QoG Institute makes comparative data on QoG and its correlates publicly available. To accomplish this, we have compiled several datasets that draw on a number of freely available data sources, including aggregated individual-level data.

The QoG OECD Datasets focus exclusively on OECD member countries. They have a high data coverage in terms of geography and time. In the QoG OECD TS dataset, data from 1946 to 2021 is included and the unit of analysis is country-year (e.g., Sweden-1946, Sweden-1947, etc.).

In the QoG OECD Cross-Section dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data are available for a country for 2018, data for 2019 is included. If no data for 2019 exists, data for 2017 is included, and so on up to a maximum of +/- 3 years. In the QoG OECD Time-Series dataset, data from 1946 to 2021 are included and the unit of analysis is country-year (e.g. Sweden-1946, Sweden-1947 and so on).

The QoG OECD Datasets focus exclusively on OECD member countries. They have a high data coverage in terms of geography and time. In the QoG OECD Time-Series dataset, data from 1946 to 2021 are included and the unit of analysis is country-year (e.g. Sweden-1946, Sweden-1947 and so on).

Graphic data of Town Planning Board (TPB) Planning Guidelines No. 13G for Application for Open Storage and Port Back-up Uses under Section 16 of the Town Planning Ordinance, including all geographical information system (GIS) data, data dictionary and guidelines on using the GIS data, provided by the TPB is available for download. Please note that in using the data, you have agreed to be bound unconditionally by the Terms and Conditions of Use of the digital planning data enclosed in the downloaded data. Please read carefully the Terms and Conditions of Use. Please click https://www.info.gov.hk/tpb/en/forms/Guidelines/TPB_PG_13G_e.pdf to download the TPB Planning Guidelines No.13G. For details of the graphic data, please refer to Statutory Planning Portal 3 website (http://www.ozp.tpb.gov.hk). The multiple file formats are available for dataset download in API.

This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data.

Without sufficient information about researchers’ data sharing, there is a risk of mismatching FAIR data service efforts with the needs of researchers. This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data. The advancement of FAIR data would benefit from novel methodologies that reliably examine data sharing at the level of multidisciplinary research organisations. Studies that use CRIS publication data to elicit insight into researchers’ data sharing may therefore be a valuable addition to the current interview and questionnaire methodologies.

Data was collected from the following sources:

All journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a coding framework was developed to capture the key elements of acquiring and sharing research data. Article DOIs are included in the research data.

The scientific journal articles and theirs DOIs are used in this study for the purpose of academic expression.

The raw data is compiled into a single CSV file. Rows represent specific articles and columns are the values of the data points described below. Author names and affiliations were not collected and are not included in the data set. Please, contact the author for access to the data.

The following data points were used in the analysis:

Data points

Main study types

Literature-based study (e.g. literature reviews, archive studies, studies of social media)

yes/no

Novel computational methods (e.g. algorithms, simulations, software)

yes/no

Interaction studies (e.g, interviews, surveys, tasks, ethnography)

yes/no

Intervention studies (e.g., EEG, MRI, clinical trials)

yes/no

Measurement studies (e.g. astronomy, weather, acoustics, chemistry)

yes/no

Life sciences (e.g. “omics”, ecology)

yes/no

Data acquisition

Article presents a data availability statement

yes/no

Article does not utilise data

yes/no

Original data was collected

yes/no

Open data from prior studies were used

yes/no

Open data from public authorities, companies, universities and associations

yes/no

Data sharing

Article does not use original data

yes/no

Data of the article is not available for reuse

yes/no

Article used openly available data

yes/no

Authors agree to share their data to interested readers

yes/no

Article shared data (or part of) as supplementary material

yes/no

Article shared data (or part of) via open deposition

yes/no

Article deposited code or used open code

yes/no

This dataset represents a water shortage vulnerability analysis performed by DWR using modified PLSS sections pulled from the Well Completion Report PLSS Section Summaries. The attribute table includes water shortage vulnerability indicators and scores from an analysis done by CA Department of Water Resources, joined to modified PLSS sections. Several relevant summary statistics from the Well Completion Reports are included in this table as well. This data is from the 2024 analysis.

Water Code Division 6 Part 2.55 Section 8 Chapter 10 (Assembly Bill 1668) effectively requires California Department of Water Resources (DWR), in consultation with other agencies and an advisory group, to identify small water suppliers and “rural communities” that are at risk of drought and water shortage. Following legislation passed in 2021 and signed by Governor Gavin Newsom, the Water Code Division 6, Section 10609.50 through 10609.80 (Senate Bill 552 of 2021) effectively requires the California Department of Water Resources to update the scoring and tool periodically in partnership with the State Water Board and other state agencies. This document describes the indicators, datasets, and methods used to construct this deliverable.  This is a statewide effort to systematically and holistically consider water shortage vulnerability statewide of rural communities, focusing on domestic wells and state small water systems serving between 4 and 14 connections. The indicators and scoring methodology will be revised as better data become available and stake-holders evaluate the performance of the indicators, datasets used, and aggregation and ranking method used to aggregate and rank vulnerability scores. Additionally, the scoring system should be adaptive, meaning that our understanding of what contributes to risk and vulnerability of drought and water shortage may evolve. This understanding may especially be informed by experiences gained while navigating responses to future droughts.”

A spatial analysis was performed on the 2020 Census Block Groups, modified PLSS sections, and small water system service areas using a variety of input datasets related to drought vulnerability and water shortage risk and vulnerability. These indicator values were subsequently rescaled and summed for a final vulnerability score for the sections and small water system service areas. The 2020 Census Block Groups were joined with ACS data to represent the social vulnerability of communities, which is relevant to drought risk tolerance and resources. These three feature datasets contain the units of analysis (modified PLSS sections, block groups, small water systems service areas) with the model indicators for vulnerability in the attribute table. Model indicators are calculated for each unit of analysis according to the Vulnerability Scoring documents provided by Julia Ekstrom (Division of Regional Assistance).

All three feature classes are DWR analysis zones that are based off existing GIS datasets. The spatial data for the sections feature class is extracted from the Well Completion Reports PLSS sections to be aligned with the work and analysis that SGMA is doing. These are not true PLSS sections, but a version of the projected section lines in areas where there are gaps in PLSS. The spatial data for the Census block group feature class is downloaded from the Census. ACS (American Communities Survey) data is joined by block group, and statistics calculated by DWR have been added to the attribute table. The spatial data for the small water systems feature class was extracted from the State Water Resources Control Board (SWRCB) SABL dataset, using a definition query to filter for active water systems with 3000 connections or less. None of these datasets are intended to be the authoritative datasets for representing PLSS sections, Census block groups, or water service areas. The spatial data of these feature classes is used as units of analysis for the spatial analysis performed by DWR.

These datasets are intended to be authoritative datasets of the scoring tools required from DWR according to Senate Bill 552. Please refer to the Drought and Water Shortage Vulnerability Scoring: California's Domestic Wells and State Smalls Systems documentation for more information on indicators and scoring. These estimated indicator scores may sometimes be calculated in several different ways, or may have been calculated from data that has since be updated. Counts of domestic wells may be calculated in different ways. In order to align with DWR SGMO's (State Groundwater Management Office) California Groundwater Live dashboards, domestic wells were calculated using the same query. This includes all domestic wells in the Well Completion Reports dataset that are completed after 12/31/1976, and have a 'RecordType' of 'WellCompletion/New/Production or Monitoring/NA'.

Please refer to the Well Completion Reports metadata for more information. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.4, dated September 14, 2022. DWR makes no warranties or guarantees — either expressed or implied— as to the completeness, accuracy, or correctness of the data.

DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to GIS@water.ca.gov.

Data and code associated with "The Observed Availability of Data and Code in Earth Science
and Artificial Intelligence" by Erin A. Jones, Brandon McClung, Hadi Fawad, and Amy McGovern.

Instructions: To reproduce figures, download all associated Python and CSV files and place
in a single directory.
Run BAMS_plot.py as you would run Python code on your system.

Code:
BAMS_plot.py: Python code for categorizing data availability statements based on given data
documented below and creating figures 1-3.

Code was originally developed for Python 3.11.7 and run in the Spyder
(version 5.4.3) IDE.

Libraries utilized:
numpy (version 1.26.4)
pandas (version 2.1.4)
matplotlib (version 3.8.0)

For additional documentation, please see code file.

Data:
ASDC_AIES.csv: CSV file containing relevant availability statement data for Artificial
Intelligence for the Earth Systems (AIES)
ASDC_AI_in_Geo.csv: CSV file containing relevant availability statement data for Artificial
Intelligence in Geosciences (AI in Geo.)
ASDC_AIJ.csv: CSV file containing relevant availability statement data for Artificial
Intelligence (AIJ)
ASDC_MWR.csv: CSV file containing relevant availability statement data for Monthly
Weather Review (MWR)

Data documentation:
All CSV files contain the same format of information for each journal. The CSV files above are
needed for the BAMS_plot.py code attached.

Records were analyzed based on the criteria below.

Records:
1) Title of paper
The title of the examined journal article.
2) Article DOI (or URL)
A link to the examined journal article. For AIES, AI in Geo., MWR, the DOI is
generally given. For AIJ, the URL is given.
3) Journal name
The name of the journal where the examined article is published. Either a full
journal name (e.g., Monthly Weather Review), or the acronym used in the
associated paper (e.g., AIES) is used.
4) Year of publication
The year the article was posted online/in print.
5) Is there an ASDC?
If the article contains an availability statement in any form, "yes" is
recorded. Otherwise, "no" is recorded.
6) Justification for non-open data?
If an availability statement contains some justification for why data is not
openly available, the justification is summarized and recorded as one of the
following options: 1) Dataset too large, 2) Licensing/Proprietary, 3) Can be
obtained from other entities, 4) Sensitive information, 5) Available at later
date. If the statement indicates any data is not openly available and no
justification is provided, or if no statement is provided is provided "None"
is recorded. If the statement indicates openly available data or no data
produced, "N/A" is recorded.
7) All data available
If there is an availability statement and data is produced, "y" is recorded
if means to access data associated with the article are given and there is no
indication that any data is not openly available; "n" is recorded if no means
to access data are given or there is some indication that some or all data is
not openly available. If there is no availability statement or no data is
produced, the record is left blank.
8) At least some data available
If there is an availability statement and data is produced, "y" is recorded
if any means to access data associated with the article are given; "n" is
recorded if no means to access data are given. If there is no availability
statement or no data is produced, the record is left blank.
9) All code available
If there is an availability statement and data is produced, "y" is recorded
if means to access code associated with the article are given and there is no
indication that any code is not openly available; "n" is recorded if no means
to access code are given or there is some indication that some or all code is
not openly available. If there is no availability statement or no data is
produced, the record is left blank.
10) At least some code available
If there is an availability statement and data is produced, "y" is recorded
if any means to access code associated with the article are given; "n" is
recorded if no means to access code are given. If there is no availability
statement or no data is produced, the record is left blank.
11) All data available upon request
If there is an availability statement indicating data is produced and no data
is openly available, "y" is recorded if any data is available upon request to
the authors of the examined journal article (not a request to any other
entity); "n" is recorded if no data is available upon request to the authors
of the examined journal article. If there is no availability statement, any
data is openly available, or no data is produced, the record is left blank.
12) At least some data available upon request
If there is an availability statement indicating data is produced and not all
data is openly available, "y" is recorded if all data is available upon
request to the authors of the examined journal article (not a request to any
other entity); "n" is recorded if not all data is available upon request to
the authors of the examined journal article. If there is no availability
statement, all data is openly available, or no data is produced, the record
is left blank.
13) no data produced
If there is an availability statement that indicates that no data was
produced for the examined journal article, "y" is recorded. Otherwise, the
record is left blank.
14) links work
If the availability statement contains one or more links to a data or code
repository, "y" is recorded if all links work; "n" is recorded if one or more
links do not work. If there is no availability statement or the statement
does not contain any links to a data or code repository, the record is left
blank.

Learn the step-by-step process to start downloading the open data of the City of Mendoza. To access and download the open data of the City of Mendoza, you do not need to register or create a user account. Access to the repository is free, and all datasets can be downloaded free of charge and without restrictions. The homepage has access buttons to 14 data categories and a search engine where you can directly enter the topic you want to access. Each data category refers to a section of the platform where you will find the various datasets available, grouped by theme. As an example, if we enter the Security section, we find different datasets within. Once you enter the dataset, you will find a list of resources. Each of these resources is a file that contains the data. For example, the dataset Security Dependencies includes specific information about each of the dependencies and allows you to access the information published in different formats and download it. In this case, if you want to open the file with the Excel program, you must click on the download button of the second resource that specifies that the format is CSV. Likewise, in other sections, there are datasets with information in various formats, such as XLS and KMZ. Each of the datasets also contains a file with additional information where you can see the last update date, the update frequency, and which government area is generating this information, among other things. To access and download the open data of the City of Mendoza, you do not need to register or create a user account. Access to the repository is free, and all datasets can be downloaded free of charge and without restrictions. The homepage has access buttons to 14 data categories and a search engine where you can directly enter the topic you want to access. Each data category refers to a section of the platform where you will find the various datasets available, grouped by theme. As an example, if we enter the Security section, we find different datasets within. Once you enter the dataset, you will find a list of resources. Each of these resources is a file that contains the data. For example, the dataset Security Dependencies includes specific information about each of the dependencies and allows you to access the information published in different formats and download it. In this case, if you want to open the file with the Excel program, you must click on the download button of the second resource that specifies that the format is CSV. Likewise, in other sections, there are datasets with information in various formats, such as XLS and KMZ. Each of the datasets also contains a file with additional information where you can see the last update date, the update frequency, and which government area is generating this information, among other things. Translated from Spanish Original Text: Conocé el paso a paso para empezar a descargar los datos abiertos de la Ciudad de Mendoza. Para acceder y descargar los datos abiertos de la Ciudad de Mendoza, no necesitás realizar ningún tipo de registro ni crear un usuario. El acceso al repositorio es libre y todos los datasets se pueden descargar de manera gratuita y sin restricciones. La página de inicio cuenta con botones de acceso a 14 categorías de datos y un buscador en donde podés ingresar directamente al tema al que quieras acceder. Cada categoría de datos, refiere a una sección de la plataforma en donde vas a encontrar los distintos datasets disponibles agrupados por temática. A modo de ejemplo, si ingresamos en la sección Seguridad, dentro encontramos diferentes datasets. Una vez que ingresas al dataset, encontrarás una lista de recursos. Cada uno de estos recursos es un archivo que contiene los datos. Por ejemplo, el dataset Dependencias de Seguridad incluye información específica sobre cada una de las dependencias y te permite acceder a la información publicada en distintos formatos y descargarla. En este caso, si quieres abrir el archivo con el programa Excel deberás hacer clic sobre el botón descargar del segundo recurso que especifica que el formato es CSV. Así como también, en otras secciones hay datasets con la información en diversos formatos, como XLS y KMZ Cada uno de los datasets, contiene además una ficha con información adicional en donde podés ver la última fecha de actualización, la frecuencia de actualización y qué área de gobierno es la generadora de esta información, entre otros. Para acceder y descargar los datos abiertos de la Ciudad de Mendoza, no necesitás realizar ningún tipo de registro ni crear un usuario. El acceso al repositorio es libre y todos los datasets se pueden descargar de manera gratuita y sin restricciones. La página de inicio cuenta con botones de acceso a 14 categorías de datos y un buscador en donde podés ingresar directamente al tema al que quieras acceder. Cada categoría de datos, refiere a una sección de la plataforma en donde vas a encontrar los distintos datasets disponibles agrupados por temática. A modo de ejemplo, si ingresamos en la sección Seguridad, dentro encontramos diferentes datasets. Una vez que ingresas al dataset, encontrarás una lista de recursos. Cada uno de estos recursos es un archivo que contiene los datos. Por ejemplo, el dataset Dependencias de Seguridad incluye información específica sobre cada una de las dependencias y te permite acceder a la información publicada en distintos formatos y descargarla. En este caso, si quieres abrir el archivo con el programa Excel deberás hacer clic sobre el botón descargar del segundo recurso que especifica que el formato es CSV. Así como también, en otras secciones hay datasets con la información en diversos formatos, como XLS y KMZ Cada uno de los datasets, contiene además una ficha con información adicional en donde podés ver la última fecha de actualización, la frecuencia de actualización y qué área de gobierno es la generadora de esta información, entre otros.

Description. This is the data used in the experiment of the following conference paper:

N. Arınık, R. Figueiredo, and V. Labatut, “Signed Graph Analysis for the Interpretation of Voting Behavior,” in International Conference on Knowledge Technologies and Data-driven Business - International Workshop on Social Network Analysis and Digital Humanities, Graz, AT, 2017, vol. 2025. ⟨hal-01583133⟩

Source code. The code source is accessible on GitHub: https://github.com/CompNet/NetVotes

Citation. If you use the data or source code, please cite the above paper.

@InProceedings{Arinik2017, author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent}, title = {Signed Graph Analysis for the Interpretation of Voting Behavior}, booktitle = {International Conference on Knowledge Technologies and Data-driven Business - International Workshop on Social Network Analysis and Digital Humanities}, year = {2017}, volume = {2025}, series = {CEUR Workshop Proceedings}, address = {Graz, AT}, url = {http://ceur-ws.org/Vol-2025/paper_rssna_1.pdf},}

Details.

RAW INPUT FILESThe 'itsyourparliament' folder contains all raw input files for further data processing (such as network extraction).The folder structure is as follows:* itsyourparliament/** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links.** meps: There are 870 Member of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.)** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs# NETWORKS AND CORRESPONDING PARTITIONSThis work studies the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) and "Economic and Monetary Affairs" (ECON) for each separate year of the 7th EP term (2009-10, 2010-11, 2011-12, 2012-13, 2013-14). Note that the interpretation part (section 4) of the published paper is limited to only a few of these instances (2009-10 in ECON and 2012-13 in AGRI).The extracted networks are located in the "networks" folder and the corresponding partitions are in the "partitions" folder. Both folders have the same structure, which is as follows:COUNTRY-NAME|_DOMAIN-NAME|_2009-10|_2010-11|_2011-12|_2012-13|_2013-14## NETWORKSThe networks in this folder are used in the article. All those networks are the ones obtained after the filtering step (as explained in the article). The networks are in 'Graphml' format. These networks are enriched with some MEPs' properties (such as name, political party, etc.) associated with each node.## ALL NETWORKSFor those who are interested in other countries or domains, we make available all possible networks that we can extract from raw data with vs. without filtering step.COUNTRY-NAME|_m3|_negtr=NA_postr=NA: This folder contains all filtered networks. Note that the filtering step is explained in Section 2.1.2 of the article.|_bygroup|_bycountry|_negtr=0_postr=0: This folder contains all original networks (i.e. no filtering step).|_bygroup|_bycountry## PARTITIONSThe partitions are obtained in this way: First, the Ex-CC (exact) method is run and we denote 'k' for the the number of detected cluster in output. This 'k' value is the reference point in order to run the ILS-RCC (heuristic) method by specifying the number of desired cluster in output. Then, ILS-RCC is run with various values ('k', 'k+1', 'k+2'). All those results are integrated into the initial network graphml files and then converted into gephi format so that this will help dive in the results in interactive way.Note that we need to handle the absent MEPs in clustering results. Because, those MEPs correspond to isolated nodes in networks. Each isolated node is considered a single cluster node in Ex-CC results. We simply omit those nodes in order to find the 'k' (number of detected cluster) value before running ILS-RCC. Not also that ILS-RCC does not process isolated nodes such that an isolated node can be part of a cluster.

----------------------# COMPARISON RESULTSThe 'material-stats' folder contains all the comparison results obtained for Ex-CC and ILS-CC. The csv files associated with plots are also provided.The folder structure is as follows:* material-stats/** execTimePerf: The plot shows the execution time of Ex-CC and ILS-CC based on randomly generated complete networks of different size.** graphStructureAnalysis: The plots show the weights and links statistics for all instances.** ILS-CC-vs-Ex-CC: The folder contains 4 different comparisons between Ex-CC and ILS-CC: Imbalance difference, number of detected clusters, difference of the number of detected clusters, NMI (Normalized Mutual Information)

----------------------Funding: Agorantic FR 3621, FMJH Program Gaspard Monge in optimization and operation research (Project 2015-2842H)

The QoG Institute is an independent research institute within the Department of Political Science at the University of Gothenburg. Overall 30 researchers conduct and promote research on the causes, consequences and nature of Good Governance and the Quality of Government - that is, trustworthy, reliable, impartial, uncorrupted and competent government institutions.

The main objective of our research is to address the theoretical and empirical problem of how political institutions of high quality can be created and maintained. A second objective is to study the effects of Quality of Government on a number of policy areas, such as health, the environment, social policy, and poverty.

QoG Standard Dataset is our largest data set consisting of more than 2,000 variables from sources related to the Quality of Government.

In the QoG Standard CS dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data is available for a country for 2018, data for 2019 is included. If no data exists for 2019, data for 2017 is included, and so on up to a maximum of +/- 3 years.

In the QoG Standard TS dataset, data from 1946 to 2021 is included and the unit of analysis is country-year (e.g., Sweden-1946, Sweden-1947, etc.).

This data release includes cross section survey data collected during site visits to USGS gaging stations located throughout the Willamette and Delaware River Basins and multispectral images of these locations acquired as close in time as possible to the date of each site visit. In addition, MATLAB source code developed for the Bathymetric Mapping using Gage Records and Image Databases (BaMGRID) framework is also provided. The site visit data were obtained from the Aquarius Time Series database, part of the USGS National Water Information System (NWIS), using the Publish Application Programming Interface (API). More specifically, a custom MATLAB function was used to query the FieldVisitDataByLocationServiceRequest endpoint of the Aquarius API by specifying the gaging station ID number and the date range of interest and then retrieve the QRev XML attachments associated with site visits meeting these criteria. These XML files were then parsed using another custom MATLAB function that served to extract the cross section survey data collected during the site visit. Note that because many of the site visits involved surveying cross sections using instrumentation that was not GPS-enabled, latitude and longitude coordinates were not available and no data values (NaN) are used in the site visit files provided in this data release. Remotely sensed data acquired as close as possible to the date of each site visit were also retrieved via APIs. Multispectral satellite images from the PlanetScope constellation were obtained using custom MATLAB functions developed to interact with the Planet Orders API, which provided tools for clipping the images to a specified area of interest focused on the gaging station and harmonizing the pixel values to be consistent across the different satellites within the PlanetScope constellation. The data product retrieved was the PlanetScope orthorectified 8-band surface reflectance bundle. PlanetScope images are acquired with high frequency, often multiple times per day at a given location, and so the search was restricted to a time window spanning from three days prior to three days after the site visit. All images meeting these criteria were downloaded and manually inspected; the highest quality image closest in time to the site visit date was retained for further analysis. For the gaging stations within the Willamette River Basin, digital aerial photography acquired through the National Agricultural Imagery Program (NAIP) in 2022 were obtained using a similar set of MATLAB functions developed to access the USGS EarthExplorer Machine-to-Machine (M2M) API. The NAIP quarter-quadrangle image encompassing each gaging station was downloaded and then clipped to a smaller area centered on the gaging station. Only one NAIP image at each gaging station was acquired in 2022, so differences in streamflow between the image acquisition date and the date of the site visit closest in time were accounted for by performing separate NWIS web queries to retrieve the stage and discharge recorded at the gaging station on the date the image was acquired and on the date of the site visit. These data sets were used as an example application of the framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID) and this data release also provides MATLAB source code developed to implement this approach. The code is packaged in a zip archive that includes the following individual .m files: 1) getSiteVisit.m, for retrieving data collected during site visits to USGS gaging stations through the Aquarius API; 2) Qrev2depth.m, for parsing the XML file from the site visit and extracting depth measurements surveyed along a channel cross section during a direct discharge measurement; 3) orderPlanet.m, for searching for and ordering PlanetScope images via the Planet Orders API; 4) pollThenGrabPlanet.m, for querying the status of an order and then downloading PlanetScope images requested through the Planet Orders API; 5) organizePlanet.m, for file management and cleanup of the original PlanetScope image data obtained via the previous two functions; 6) ingestNaip.m, for searching for, ordering, and downloading NAIP data via the USGS Machine-to-Machine (M2M) API; 7) naipExtractClip.m, for clipping the downloaded NAIP images to the specified area of interest and performing file management and cleanup; and 8) crossValObra.m, for performing spectrally based depth retrieval via the Optimal Band Ratio Analysis (OBRA) algorithm using a k-fold cross-validation approach intended for small sample sizes. The files provided through this data release include: 1) A zipped shapefile with polygons delineating the Willamette and Delaware River basins 2) .csv text files with information on site visits within each basin during 2022 3) .csv text files with information on PlanetScope images of each gaging station close in time to the date of each site visit that can be used to obtain the image data through the Planet Orders API or Planet Explorer web interface. 4) A .csv text tile with information on NAIP images of each gaging station in the Willamette River Basin as close in time as possible to the date of each site visit, along with the stage and discharge recorded at the gaging station on the date of image acquisition and the date of the site visit. 5) A zip archive of the clipped NAIP images of each gaging station in the Willamette River Basin in GeoTIFF format. 6) A zip archive with source code (MATLAB *.m files) developed to implement the Bathymetric Mapping using Gage Records and Image Databases (BaMGRID) framework.

This ownership dataset utilizes a methodology that results in a federal ownership extent that matches the Federal Responsibility Areas (FRA) footprint from CAL FIRE's State Responsibility Areas for Fire Protection (SRA) data. FRA lands are snapped to county parcel data, thus federal ownership areas will also be snapped. Since SRA Fees were first implemented in 2011, CAL FIRE has devoted significant resources to improve the quality of SRA data. This includes comparing SRA data to data from other federal, state, and local agencies, an annual comparison to county assessor roll files, and a formal SRA review process that includes input from CAL FIRE Units. As a result, FRA lands provide a solid basis as the footprint for federal lands in California (except in the southeastern desert area). The methodology for federal lands involves: 1) snapping federal data sources to parcels; 2) clipping to the FRA footprint; 3) overlaying the federal data sources and using a hierarchy when sources overlap to resolve coding issues (BIA, UFW, NPS, USF, BLM, DOD, ACE, BOR); 4) utilizing an automated process to merge “unknown” FRA slivers with appropriate adjacent ownerships;5) a manual review of FRA areas not assigned a federal agency by this process. Non-Federal ownership information was obtained from the California Protected Areas Database (CPAD), was clipped to the non-FRA area, and an automated process was used to fill in some sliver-gaps that occurred between the federal and non-federal data. Southeastern Desert Area: CAL FIRE does not devote the same level of resources for maintaining SRA data in this region of the state, since we have no fire protection responsibility. This includes almost all of Imperial County, and the desert portions of Riverside, and San Bernardino Counties. In these areas, we used federal protection areas from the current version of the Direct Protection Areas (DPA) dataset. Due to the fact that there were draw-issues with the previous version of ownership, this version does NOT fill in the areas that are not assigned to one of the owner groups (it does not cover all lands in the state). Also unlike previous versions of the dataset, this version only defines ownership down to the agency level - it does not contain more specific property information (for example, which National Forest). The option for a more detailed future release remains, however, and due to the use of automated tools, could always be created without much additional effort.This dataset includes a representation to symbolize based on the Own_Group field using the standard color scheme utilized on DPA maps.For more details about data inputs, see the Lineage section of the metadata. For detailed notes on previous versions, see the Supplemental Information section of the metadata.This ownership dataset is derived from CAL FIRE's SRA dataset, and GreenInfo Network's California Protected Areas Database. CAL FIRE tracks lands owned by federal agencies as part of our efforts to maintain fire protection responsibility boundaries, captured as part of our State Responsiblity Areas (SRA) dataset. This effort draws on data provided by various federal agencies including USDA Forest Service, BLM, National Park Service, US Fish and Wildlife Service, and Bureau of Inidan Affairs. Since SRA lands are matched to county parcel data where appropriate, often federal land boundaries are also adjusted to match parcels, and may not always exactly match the source federal data. Federal lands from the SRA dataset are combined with ownership data for non-federal lands from CPAD, in order to capture lands owned by various state and local agencies, special districts, and conservation organizations. Data from CPAD are imported directly and not adjusted to match parcels or other features. However, CPAD features may be trimmed if they overlap federal lands from the SRA dataset. Areas without an ownership feature are ASSUMED to be private (but not included in the dataset as such). This service represents the latest release of the dataset by FRAP, and is updated twice a year when new versions are released.

This dataset is flattened and multicounty communities are unsplit by county lines. Flattened means that there are no overlaps; larger shapes like counties are punched out or clipped where smaller communities are contained within them. This allows for choropleth shading and other mapping techniques such as calculating unincorporated county land area. Multicounty cities like Houston are a single feature, undivided by counties. This layer is derived from Census, State of Maine, and National Flood Hazard Layer political boundaries.rnrnThe Community Layer datasets contain geospatial community boundaries associated with Census and NFIP data. The dataset does not contain personal identifiable information (PII). The Community Layer can be used to tie Community ID numbers (CID) to jurisdiction, tribal, and special land use area boundaries.rnrnA geodatabase (GDB) link is Included in the Full Data section below. The compressed file contains a collection of files that can store, query, and manage both spatial and nonspatial data using software that can read such a file. It bcontains all of the community layers/b, not just the layer for which this dataset page describes. rnThis layer can also be accessed from the FEMA ArcGIS viewer online: https://fema.maps.arcgis.com/home/item.html?id=8dcf28fc5b97404bbd9d1bc6d3c9b3cfrnrnrnCitation: FEMA's citation requirements for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnrnFor answers to Frequently Asked Questions (FAQs) about the OpenFEMA program, API, and publicly available datasets, please visit: https://www.fema.gov/about/openfema/faq.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

All data associated with the Town of Young Floodplain Risk Management Study and Plan. GIS Data Outputs, Hydraulics, Hydrology, Reporting, Survey. Data and Resources Data associated with Town of Young Floodplain Risk Management Study and PlanZIP (11.5 GB) All Data and GIS data associated with the Town of Young Floodplain Risk Management Study and Plan. Explore More information Download More info Creative Commons Attribution 4.0 International Public License By exercising the Licensed Rights (defined below), You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution 4.0 International Public License (“Public License”). 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A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.  

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Gender * The City collects information on gender identity using these guidelines.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’.

Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">Learn more about our data collection guidelines pertaining to sexual orientation.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews.

Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
• 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.

On 11June 2024 you clarified your request as follows: I am specifically looking for data on the Covid vaccines, so just figures relating to those please. And the date range would start at the vaccine rollout so let’s say a start date of January 2021 The NHS Business Services Authority (NHSBSA) received your request on 11 June 2024 as this is when we received your clarification. We have handled your request under the Freedom of Information Act (FOIA) 2000. Our response I can confirm that the NHSBSA holds some of the information you have requested. All data is as of 19 June 2024. All data relates to claims received by the NHSBSA and those transferred from the Department for Work and Pensions (DWP) on 1 November 2021. All figures provided for questions 1 to 3 relate to COVID-19 vaccines. Section 40(2) Freedom of Information Act Whilst this some of the requested information is held by the NHSBSA, we have exempted some of the figures under section 40(2) subsections 2 and 3(a) of the FOIA because it is personal data of applicants to the VDPS. This is because it would breach the first data protection principle as: a - it is not fair to disclose individual’s personal details to the world and is likely to cause damage or distress. b - these details are not of sufficient interest to the public to warrant an intrusion into the privacy of the individual. https://www.legislation.gov.uk/ukpga/2000/36/section/40 Information Commissioner Office (ICO) Guidance is that information is personal data if it ‘relates to’ an ‘identifiable individual’ regulated by the UK General Data Protection Regulation (UK GDPR) or the Data Protection Act 2018. The information relates to personal data of the VDPS claimants and is special category data in the form of health information. As a result, the claimants could be identified, when combined with other information that may be in the public domain or reasonably available. Online communities exist for those adversely affected by vaccines they have received. This further increases the likelihood that those may be identified by disclosure of this information. Section 40(2) is an absolute, prejudice-based exemption and therefore is exempt if disclosure would contravene any of the data protection principles. To comply with the lawfulness, fairness, and transparency data protection principle, we either need the consent of the data subject(s) or there must be a legitimate interest in disclosure. In addition, the disclosure must be necessary to meet the legitimate interest and finally, the disclosure must not cause unwarranted harm. The NHSBSA has considered this and does not have the consent of the data subjects to release this information and believes that it would not be possible to obtain consent that meets the threshold in Article 7 of the UK GDPR. The NHSBSA acknowledges that you have a legitimate interest in disclosure of the information to provide the full picture of data held by the NHSBSA; however, we have concluded that disclosure of the requested information would cause unwarranted harm and therefore, section 40(2) is engaged. This is because there is a reasonable expectation that patient data processed by the NHSBSA remains confidential, especially special category data. There are no reasonable alternative measures that could meet the legitimate aim. As the information is highly confidential and sensitive, it outweighs the legitimate interest in the information. Section 41 FOIA This information is also exempt under section 41 of the FOIA (information provided in confidence). This is because the requested information was provided to the NHSBSA in confidence by a third party - another individual, company, public authority or any other type of legal entity. In this instance, details have been provided by the claimants. For Section 41 to be engaged, the following criteria must be fulfilled:

The missing section grids describe geographic variations in erosion associated with unconformities in the 3D petroleum systems model. The grid values provide inputs for the model to restore layer thicknesses prior to erosion events and at the time of deposition. Each missing section grid here describes the depth of material that was eroded at 70, 50, 43, and 20 million years ago in the model. This is a child item of a larger data release titled "Data release for the 3D petroleum systems model of the Williston Basin, USA".

Discrete volumetric and mid-section stream discharge measurements were conducted from July through October 2020 in H.J. Andrews Experimental Forest near Blue River, OR. The measured streams are part of the Lookout Creek basin, draining into Blue River and subsequently the McKenzie River on the west slope of the Cascade Range. ORWSC Streamflow measurements supplemented an eco-drought low-flow modeling project in partnership with the Forest and Rangeland Ecosystem Science Center (FRESC) and the USGS Water Mission Area (WMA). Measurements were collected at 25 selected sites with co-located HOBO data loggers and 7 miscellaneous (MISC) sites with no data loggers present. HOBO logger data were collected and processed by FRESC team members. Volumetric measurements were collected by placing a modified weir into the stream and directing the entire streamflow over the weir, easing the collection of water into a bucket. Collections of water were timed to the hundredth of a second using a stopwatch, and volumes were measured with a graduated cylinder in 20 ml increments. Mid-section measurements were conducted following USGS protocol, wading in streams using SonTek FlowTracker-2 handheld acoustic Doppler velocimeters. Sites were measured during two field runs occurring July 6th through 10th 2020 and August 10th through 14th 2020. Two of the sites were measured a third time on October 26th and 27th, 2020, to compare midsection measurements with volumetric measurements. In the streams with HOBO dataloggers, the volumetric measurements achieved satisfactory channeling of the streams with the weir method and are considered more accurate with less uncertainty than the mid-section measurements. Steep channel conditions, large boulders and cobbles through the channel cross-section, large stream velocity angles, non-standard stream velocity profiles, and the channelization of flows created unideal conditions for midsection measurements. The MISC streamflow measurements were conducted at adequate channel cross-sections detailed by USGS protocols.

Graphic data of Town Planning Board (TPB) Planning Guidelines No. 12C for Application for Developments within Deep Bay Area under Section 16 of the Town Planning Ordinance, including all geographical information system (GIS) data, data dictionary and guidelines on using the GIS data, provided by the TPB is available for download. Please note that in using the data, you have agreed to be bound unconditionally by the Terms and Conditions of Use of the digital planning data enclosed in the downloaded data. Please read carefully the Terms and Conditions of Use. Please click https://www.info.gov.hk/tpb/en/forms/Guidelines/pg12c_e.pdf to download the TPB Planning Guidelines No.12C. For details of the graphic data, please refer to Statutory Planning Portal 3 website (http://www.ozp.tpb.gov.hk). The multiple file formats are available for dataset download in API.

How To Cite?

Alinaghi, N., Giannopoulos, I., Kattenbeck, M., & Raubal, M. (2025). Decoding wayfinding: analyzing wayfinding processes in the outdoor environment. International Journal of Geographical Information Science, 1–31. https://doi.org/10.1080/13658816.2025.2473599

Link to the paper: https://www.tandfonline.com/doi/full/10.1080/13658816.2025.2473599

Folder Structure

The folder named “submission” contains the following:

1. “pythonProject”: This folder contains all the Python files and subfolders needed for analysis.
2. ijgis.yml: This file lists all the Python libraries and dependencies required to run the code.

Setting Up the Environment

1. Use the ijgis.yml file to create a Python project and environment. Ensure you activate the environment before running the code.
2. The pythonProject folder contains several .py files and subfolders, each with specific functionality as described below.

Subfolders

1. Data_4_IJGIS

• This folder contains the data used for the results reported in the paper.
• Note: The data analysis that we explain in this paper already begins with the synchronization and cleaning of the recorded raw data. The published data is already synchronized and cleaned. Both the cleaned files and the merged files with features extracted for them are given in this directory. If you want to perform the segmentation and feature extraction yourself, you should run the respective Python files yourself. If not, you can use the “merged_…csv” files as input for the training.

2. results_[DateTime] (e.g., results_20240906_15_00_13)

• This folder will be generated when you run the code and will store the output of each step.
• The current folder contains results created during code debugging for the submission.
• When you run the code, a new folder with fresh results will be generated.

Python Files

1. helper_functions.py

• Contains reusable functions used throughout the analysis.
• Each function includes a description of its purpose and the input parameters required.

2. create_sanity_plots.py

• Generates scatter plots like those in Figure 3 of the paper.
• Although the code has been run for all 309 trials, it can be used to check the sample data provided.
• Output: A .png file for each column of the raw gaze and IMU recordings, color-coded with logged events.
• Usage: Run this file to create visualizations similar to Figure 3.

3. overlapping_sliding_window_loop.py

• Implements overlapping sliding window segmentation and generates plots like those in Figure 4.
• Output:
  • Two new subfolders, “Gaze” and “IMU”, will be added to the Data_4_IJGIS folder.
  • Segmented files (default: 2–10 seconds with a 1-second step size) will be saved as .csv files.
  • A visualization of the segments, similar to Figure 4, will be automatically generated.

4. gaze_features.py & imu_features.py (Note: there has been an update to the IDT function implementation in the gaze_features.py on 19.03.2025.)

• These files compute features as explained in Tables 1 and 2 of the paper, respectively.
• They process the segmented recordings generated by the overlapping_sliding_window_loop.py.
• Usage: Just to know how the features are calculated, you can run this code after the segmentation with the sliding window and run these files to calculate the features from the segmented data.

5. training_prediction.py

• This file contains the main machine learning analysis of the paper. This file contains all the code for the training of the model, its evaluation, and its use for the inference of the “monitoring part”. It covers the following steps:

a. Data Preparation (corresponding to Section 5.1.1 of the paper)

• Prepares the data according to the research question (RQ) described in the paper. Since this data was collected with several RQs in mind, we remove parts of the data that are not related to the RQ of this paper.
• A function named plot_labels_comparison(df, save_path, x_label_freq=10, figsize=(15, 5)) in line 116 visualizes the data preparation results. As this visualization is not used in the paper, the line is commented out, but if you want to see visually what has been changed compared to the original data, you can comment out this line.

b. Training/Validation/Test Split

• Splits the data for machine learning experiments (an explanation can be found in Section 5.1.1. Preparation of data for training and inference of the paper).
• Make sure that you follow the instructions in the comments to the code exactly.
• Output: The split data is saved as .csv files in the results folder.

c. Machine and Deep Learning Experiments

This part contains three main code blocks:

iii. One for the XGboost code with correct hyperparameter tuning:
Please read the instructions for each block carefully to ensure that the code works smoothly. Regardless of which block you use, you will get the classification results (in the form of scores) for unseen data. The way we empirically test the confidence threshold of

• MLP Network (Commented Out): This code was used for classification with the MLP network, and the results shown in Table 3 are from this code. If you wish to use this model, please comment out the following blocks accordingly.
• XGBoost without Hyperparameter Tuning: If you want to run the code but do not want to spend time on the full training with hyperparameter tuning (as was done for the paper), just uncomment this part. This will give you a simple, untuned model with which you can achieve at least some results.
• XGBoost with Hyperparameter Tuning: If you want to train the model the way we trained it for the analysis reported in the paper, use this block (the plots in Figure 7 are from this block). We ran this block with different feature sets and different segmentation files and created a simple bar chart from the saved results, shown in Figure 6.

Note: Please read the instructions for each block carefully to ensure that the code works smoothly. Regardless of which block you use, you will get the classification results (in the form of scores) for unseen data. The way we empirically calculated the confidence threshold of the model (explained in the paper in Section 5.2. Part II: Decoding surveillance by sequence analysis) is given in this block in lines 361 to 380.

d. Inference (Monitoring Part)

• Final inference is performed using the monitoring data. This step produces a .csv file containing inferred labels.
• Figure 8 in the paper is generated using this part of the code.

6. sequence_analysis.py

• Performs analysis on the inferred data, producing Figures 9 and 10 from the paper.
• This file reads the inferred data from the previous step and performs sequence analysis as described in Sections 5.2.1 and 5.2.2.

Licenses

The data is licensed under CC-BY, the code is licensed under MIT.

COVID-19 SDU Acute Hospital Time Series Summary. Published by Tailte Éireann. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Please see FAQ for latest information on COVID-19 Data Hub Data Flows: https://covid-19.geohive.ie/pages/helpfaqs. Notice: Please note that data for the 30th of May 2023 is missing from this dataset.If you are downloading this data set as a CSV please follow these steps to sort the dataset by date.1. Click the 'Download' button.2. In the download pane that opens on the left, click the 'Download' button under CSV. This should be the first option.3. Open the file.4. Highlight column D by click 'D'.5. In the ribbon, in the Editing group click 'Sort & Filter'.6. From the drop down menu that appears select the first option to sort from oldest to newest.7. In the pop-up window that appears make sure that 'Expand the selection' is selected.8. Click 'Sort', the dataset will now be sorted by date. See the section What impact has the cyber-attack of May 2021 on the HSE IT systems had on reporting of COVID-19 data on the Data Hub? in the FAQ for information about issues in data from May 2021.** Between 14th May 2021 and 29th July 2021 only the fields 'Number of confirmed COVID-19 cases Admitted on site' (SUM_number_of_confirmed_covid_19_ca) and 'Number of new COVID-19 cases confirmed in the past 24 hrs' (SUM_number_of_new_covid_19_cases_co) in this service were updated.The fields 'Number of New Admissions COVID-19 Positive previous 24hrs' (SUM_no_new_admissions_covid19_p) and 'Number of Discharges COVID-19 Positive previous 24hrs' (SUM_no_discharges_covid19_posit) have no data during this period of time. **Detailed dataset containing a range of COVID-19 related indicators for Acute Hospitals in Ireland. Data is provided for Confirmed COVID-19 cases and the number of new admissions and discharges. Data is based on an aggregate of 29 Acute Hospitals. Data has been provided by the HSE Performance Management Improvement Unit (PMIU).This service is used in Ireland's COVID-19 Data Hub, produced as a collaboration between Tailte Éireann, the Central Statistics Office (CSO), the Department of Housing, Planning and Local Government, the Department of Health, the Health Protection Surveillance Centre (HPSC), and the All-Island Research Observatory (AIRO). This service and Ireland's COVID-19 Data Hub are built using the GeoHive platform, Ireland's Geospatial Data Hub. ...

OVERVIEW

This data file, compiled from multiple online sources, presents 2013–2017 publication counts—articles, articles in high-impact journals, books, and books from high-impact publishers—for 2,132 professors and associate professors in 426 U.S. departments of sociology. It also includes information on institutional characteristics (e.g., institution type, highest sociology degree offered, department size) and individual characteristics (e.g., academic rank, gender, PhD year, PhD institution).

The data may be useful for investigations of scholarly productivity, the correlates of scholarly productivity, and the contributions of particular individuals and institutions. Complete population data are presented for the top 26 doctoral programs, doctoral institutions other than R1 universities, the top liberal arts colleges, and other bachelor's institutions. Sample data are presented for Carnegie R1 universities (other than the top 26) and master's institutions.

USER NOTES

Please see our paper in Scholarly Assessment Reports, freely available at https://doi.org/10.29024/sar.36 , for full information about the data set and the methods used in its compilation. The section numbers used here refer to the Appendix of that paper. See the References, below, for other papers that have made use of these data.

The data file is a single Excel file with five worksheets: Sampling, Articles, Books, Individuals, and Departments. Each worksheet has a simple rectangular format, and the cells include just text and values—no formulas or links. A few general notes apply to all five worksheets.

• The yellow column headings represent institutional (departmental) data. The blue column headings represent data for individual faculty.

• iType is institution type, as described in section A.2—TopR (top research universities), R1 (other R1 universities), OD (other doctoral universities), M (master's institutions), TopLA (top liberal arts colleges), or B (other bachelor's institutions). nType provides the same information, but as a single-digit code that is more useful for sorting the rows; 1=TopR, 2=R1, 3=OD, 4=M, 5=TopLA, and 6=B.

• Inst is a four-digit institution code. The first digit corresponds to nType, and the last three digits allow for alphabetical sorting by institution name. Indiv is a one- or two-digit code that can be used to sort the individuals by name within each department. The Inst, nType, and Indiv codes are consistent across the five worksheets.

• For binary variables such as Full professor and Female, 1 indicates yes (full professor or female) and 0 indicates no (associate professor or male).

The five worksheets represent five distinct stages in the data compilation process. First, the Sampling worksheet lists the 1,530 base-population institutions (see section A.3) and presents the characteristics of the faculty included in the data file. Each row with an entry in the Individual column represents a faculty member at one of the 426 institutions included in the data set. Each row without an entry in the Individual column represents an institution that either (a) did not meet the criteria for inclusion (section A.1) or (b) was not needed to attain the desired sample size for the R1 or M groups (section A.3).

The Articles worksheet includes the data compiled from SocINDEX, as described in section A.6. Each row with an entry in the Journal column represents an article written by one of the 2,132 faculty included in the data. Each row without an entry in the Journal column represents a faculty member without any article listings in SocINDEX for the 2013–2017 period. (Note that SocINDEX items other than peer-reviewed articles—editorials, letters, etc.—may be listed in the Journal column but assigned a value of 1 in the Excluded column and a value of 0 in the Article credit and HI article credit columns. We assigned no credit for items such as editorial and letters, but other researchers may wish to include them.) The N and i columns represent, for each article, the number of authors (N) and the faculty member's place in the byline (i), as described in section A.8. The CiteScore and Highest percentile columns were used to identify high-impact journals, as indicated in the HI journal column. The Article credit and HI article credit columns are article counts, adjusted for co-authorship.

The Books worksheet includes data compiled from Amazon and other sources, as described in section A.7. Each row with an entry in the Book column represents a book written by one of the 2,132 faculty. Each row without an entry in the Book column represents a faculty member without any book listings in Amazon during the 2013–2017 period. The publication counts in the Books worksheet—Book credit and HI book credit—follow the same format as those in the Articles worksheet.

The Individuals worksheet consolidates information from the Articles and Books worksheets so that each of the 2,132 individuals is represented by a single row. The worksheet also includes several categorical variables calculated or otherwise derived from the raw data—Years since PhD, for instance, and the three corresponding binary variables. We suspect that many data users will be most interested in the Individuals worksheet.

The Departments worksheet collapses the individual data so that each of the 426 institutions (departments) is represented by a single row. Individual characteristics such as Female and Years since PhD are presented as percentages or averages—% Female and Avg years since PhD, for instance. Each of the four productivity measures is represented by a departmental total, an average (the total divided by the number of full and associate professors), a departmental standard deviation, and a departmental median.