The aim of this paper is to investigate the re-use of research data deposited in digital data archive in the social sciences. The study examines the quantity, type, and purpose of data downloads by analyzing enriched user log data collected from Swiss data archive. The findings show that quantitative datasets are downloaded increasingly from the digital archive and that downloads focus heavily on a small share of the datasets. The most frequently downloaded datasets are survey datasets collected by research organizations offering possibilities for longitudinal studies. Users typically download only one dataset, but a group of heavy downloaders form a remarkable share of all downloads. The main user group downloading data from the archive are students who use the data in their studies. Furthermore, datasets downloaded for research purposes often, but not always, serve to be used in scholarly publications. Enriched log data from data archives offer an interesting macro level perspective on the use and users of the services and help understanding the increasing role of repositories in the social sciences. The study provides insights into the potential of collecting and using log data for studying and evaluating data archive use.

Discover the Walmart Products Free Dataset, featuring 2,000 records in CSV format. This dataset includes detailed information about various Walmart products, such as names, prices, categories, and descriptions.

It’s perfect for data analysis, e-commerce research, and machine learning projects. Download now and kickstart your insights with accurate, real-world data.

This dataset is the Version 2.0 of microsoft/FStarDataSet.

  Primary-Objective

This dataset's primary objective is to train and evaluate Proof-oriented Programming with AI (PoPAI, in short). Given a specification of a program and proof in F*, the objective of a AI model is to synthesize the implemantation (see below for details about the usage of this dataset, including the input and output).

  Data Format

Each of the examples in this dataset are organized as dictionaries… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/FStarDataSet-V2.

Ecological footprint calculated in number of planets per capita. The method is based on the regional ecological footprint, as calculated by the IUU IdF (Nov 2005). Within the 5 sectors considered in the calculation: food, services, goods, mobility and housing, the latter two have been recalculated on the basis of communal data. The property sector has been allocated with a weighting according to the resources of the communal population. The other two sectors were distributed in proportion to the population. C_dep_iau c_dep_iau_Labels c_arobase_d_Classes_Empr_ecolo C_Com_iau c_Com_iau_2_ Tags C_Com_iau_Labels c_Com_iau_2

TRAINING DATASET: Hands-On Uploading Data (Download This File)

This is the official website for downloading the CA sub-dataset of the LargeST benchmark dataset. There are a total of 7 files in this page. Among them, 5 files in .h5 format contain the traffic flow raw data from 2017 to 2021, 1 file in .csv format provides the metadata for sensors, and 1 file in .npy format represents the adjacency matrix constructed based on road network distances. Please refer to https://github.com/liuxu77/LargeST for more information.

Data organization for the figures in the document: Figure 3A LineOutWithSun_SSAzi_135to225_green_Correct_ROI5_INFO.xls Figure 3b LineOutWithSun_SSAzi_m45to45_green_Correct_ROI5_INFO.xls Figure 4 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Sim_Correct_ROI5_INFO.xls Figure 5a LineOut_Camera_Elevation_SqAzi_m180to0_green_Sim_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls Figure 5b LineOut_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_0to180_green_Sim_Correct_ROI5_INFO.xls Figure 6a LineOutColor_SqAzi_m180to0_CP_20to50_Correct_ROI5_INFO.xls Figure 6b LineOutROI_SqAzi_m180to0_CP_20to50_green_Correct_INFO.xls Figure 7 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls

Description of the INSPIRE Download Service (predefined Atom): Statutes on the development plan "Im Fastnachtsstück - An den weißen Wacken I" (1st amendment) of 01.02.1995 - The link(s) for downloading the data sets is/are dynamically generated from Get Map calls to a WMS interface

This Quito Geotechnical Database has been compiled and digitised from an extensive review of soil geotechnical test results reported in published literature including reports, research papers, and dissertations. The database describes geotechnical and geophysical soil properties distributed throughout Quito, Ecuador, recorded at different locations and comprising the results of both in situ tests and geotechnical laboratory tests. A detailed User Manual is provided with the database. This database is an updated version of the original Quito Geotechnical Database v1.0 published at http://doi.org/10.5523/bris.3m2ficmw3ltjx2m0yl4cwptkqm Complete download (zip, 1 MiB) Creator(s)

Description of INSPIRE Download Service (predefined Atom): The first amendment to the development plan “Unterer Schwelbel” served to change the use of the 2. Upper floor of the furniture market in a non-essentially disturbing commercial use. This was done in the 2nd. Upstairs a restricted commercial area – The link(s) for downloading the records is/are generated dynamically from Get Map Calling a WMS Interface

About this dataset

Context

The dataset tabulates the United States population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of United States across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of United States was 333,287,557, a 0.38% increase year-by-year from 2021. Previously, in 2021, United States population was 332,031,554, an increase of 0.16% compared to a population of 331,511,512 in 2020. Over the last 20 plus years, between 2000 and 2022, population of United States increased by 51,125,146. In this period, the peak population was 333,287,557 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the United States is shown in this column.
• Year on Year Change: This column displays the change in United States population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States Population by Year. You can refer the same here

Description of INSPIRE Download Service (predefined Atom): Change of the city of Remagen (Kripp) – The link(s) for downloading the records is/are generated dynamically from Get Map Calling a WMS Interface

Description of the INSPIRE Download Service (predefined Atom): Change Industrial Estate Seenplatte - The link(s) for downloading the datasets is/are dynamically generated from Get Map calls to a WMS interface

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This child item describes Python code used to retrieve gridMET climate data for a specific area and time period. Climate data were retrieved for public-supply water service areas, but the climate data collector could be used to retrieve data for other areas of interest. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Data retrieved by the climate data collector code were used as input feature variables in the public supply delivery and water use machine learning models. This page includes the following file: climate_data_collector.zip - a zip file containing the climate data collector Python code used to retrieve climate data and a README file.

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains terrain elevation above sea level (ELE) in [m a.s.l.] covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km).

The data is hyperlinked under 'resources' with the following characeristics:
ELE - GISdata (GeoTIFF)
Data format: GEOTIFF
File size : 826.8 MB

There are two temporal representation of solar resource and PVOUT data available:
• Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals)
• Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals)

Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations:
• LTAy_YearlyTotals = LTAy_DailyTotals * 365.25
• LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month

*For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest)
*For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world.

For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

This dataset analyzes waste from the the British Petroleum Deepwater Horizon Rig Explosion Emergency Response, providing opportunity to query data sets by metadata criteria and find resulting raw datasets in CSV format.The data query tool allows users to download air, water and sediment sampling and monitoring data that has been collected in response to the BP oil spill. All sampling and monitoring data that has been collected to date is available for download as raw structured data.The query tools enables CSV file creation to be refined based on the following search criteria: date range (between April 28, 2010 and 9/29/2010); location by zip, city, or county; media (solid waste, weathered oil, air, surface water, liquid waste, tar, sediment, water); substance categories (based on media selection) and substances (based on substance category selection).

Overview

This dataset contains version 3.0 (March 2025 release) of the Global Fishing Watch apparent fishing effort dataset. Data is available for 2012-2024 and based on positions of >190,000 unique automatic identification system (AIS) devices on fishing vessels, of which up to ~96,000 are active in a given year. Fishing vessels are identified via a machine learning model, vessel registry databases, and manual review by GFW and regional experts. Vessel time is measured in hours, calculated by assigning to each AIS position the amount of time elapsed since the previous AIS position of the vessel. The time is counted as apparent fishing hours if the GFW fishing detection model - a neural network machine learning model - determines the vessel is engaged in fishing behavior during that AIS position.

Data are spatially binned into grid cells that measure 0.01 or 0.1 degrees on a side; the coordinates defining each cell are provided in decimal degrees (WGS84) and correspond to the lower-left corner. Data are available in the following formats:

1. Daily apparent fishing hours by flag state and gear type at 100th degree resolution
2. Monthly apparent fishing hours by flag state and gear type at 10th degree resolution
3. Daily apparent fishing hours by MMSI at 10th degree resolution

The fishing effort dataset is accompanied by a table of vessel information (e.g. gear type, flag state, dimensions).

File structure

Fishing effort and vessel presence data are available as .csv files in daily formats. Files for each year are stored in separate .zip files. A README.txt and schema.json file is provided for each dataset version and contains the table schema and additional information. There is also a README-known-issues-v3.txt file outlining some of the known issues with the version 3 release.

Files are names according to the following convention:

• Daily file format:

• [fleet/mmsi]-daily-csvs-[100/10]-v3-[year].zip

• [fleet/mmsi]-daily-csvs-[100/10]-v3-[date].csv

• Monthly file format:

• fleet-monthly-csvs-10-v3-[year].zip

• fleet-monthly-csvs-10-v3-[date].csv

• Fishing vessel format: fishing-vessels-v3.csv

• README file format: README-[fleet/mmsi/fishing-vessels/known-issues]-v3.txt

File identifiers:

• [fleet/mmsi]: Data by fleet (flag and geartype) or by MMSI

• [100/10]: 100th or 10th degree resolution

• [year]: Year of data included in .zip file

• [date]: Date of data included in .csv files. For monthly data, [date]corresponds to the first date of the month

Examples: fleet-daily-csvs-100-v3-2020.zip; mmsi-daily-csvs-10-v3-2020-01-10.csv; fishing-vessels-v3.csv; README-fleet-v3.txt; fleet-monthly-csvs-10-v3-2024.zip; fleet-monthly-csvs-10-v3-2024-08-01.csv

Key documentation

• For an overview of how GFW turns raw AIS positions into estimates of fishing hours, see this page.

• The models used to produce this dataset were developed as part of this publication: D.A. Kroodsma, J. Mayorga, T. Hochberg, N.A. Miller, K. Boerder, F. Ferretti, A. Wilson, B. Bergman, T.D. White, B.A. Block, P. Woods, B. Sullivan, C. Costello, and B. Worm. "Tracking the global footprint of fisheries." Science 361.6378 (2018). Model details are available in the Supplementary Materials.

• The README-known-issues-v3.txt file describing this dataset's specific caveats can be downloaded from this page. We highly recommend that users read this file in full.

• The README-mmsi-v3.txt file, the README-fleet-v3.txt file, and the README-fishing-vessels-v3.txt files are downloadable from this page and contain the data description for (respectively) the fishing hours by MMSI dataset, the fishing hours by fleet dataset, and the vessel information file. These readmes contain key explanations about the gear types and flag states assigned to vessels in the dataset.

• File name structure for the datafiles are available below on this page and file schema can be downloaded from this page.

• A FAQ describing the updates in this version and the differences between this dataset and the data available from the GFW Map and APIs is available here.

Use Cases

The apparent fishing hours dataset is intended to allow users to analyze patterns of fishing across the world’s oceans at temporal scales as fine as daily and at spatial scales as fine as 0.1 or 0.01 degree cells. Fishing hours can be separated out by gear type, vessel flag and other characteristics of vessels such as tonnage.

Potential applications for this dataset are broad. We offer suggested use cases to illustrate its utility. The dataset can be integrated as a static layer in multi-layered analyses, allowing researchers to investigate relationships between fishing effort and other variables, including biodiversity, tracking, and environmental data, as defined by their research objectives.

A few example questions that these data could be used to answer:

• What flag states have fishing activity in my area of interest?

• Do hotspots of longline fishing overlap with known migration routes of sea turtles?

• How does fishing time by trawlers change by month in my area of interest? Which seasons see the most trawling hours and which see the least?

Caveats

This global dataset estimates apparent fishing hours effort. The dataset is based on publicly available information and statistical classifications which may not fully capture the nuances of local fishing practices. While we manually review the dataset at a global scale and in a select set of smaller test regions to check for issues, given the scale of the dataset we are unable to manually review every fleet in every region. We recognize the potential for inaccuracies and encourage users to approach regional analyses with caution, utilizing their own regional expertise to validate findings. We welcome your feedback on any regional analysis at research@globalfishingwatch.org to enhance the dataset's accuracy.

Caveats relating to known sources of inaccuracy as well as interpretation pitfalls to avoid are described in the README-known-issues-v3.txt file available for download from this page. We highly recommend that users read this file in full. The issues described include:

• Data from 2024 should be considered provisional, as vessel classifications may change as more data from 2025 becomes available.

• MMSI is used in this dataset as the vessel identifier. While MMSI is intended to serve as the unique AIS identifier for an individual vessel, this does not always hold in practice.

• The Maritime Identification Digits (MID), the first 3 digits of MMSI, are the only source of information on vessel flag state when the vessel does not appear on a registry. The MID may be entered incorrectly, obscuring information about an MMSI’s flag state.

• AIS reception is not consistent across all areas and changes over time.

Alternative ways to access

1. Query using SQL in the Global Fishing Watch public BigQuery dataset: global-fishing-watch.fishing_effort_v3

2. Download the entire dataset from the Global Fishing Watch Data Download Portal (https://globalfishingwatch.org/data-download/datasets/public-fishing-effort)