Find research datasets worth reusing

opencc-by-4.0Apr 2022View details →

SMLBase: Global compilation of surface mixed layer parameters (sedimentation rate, bioturbation depth, mixing intensity) from marine environments

A global compilation of sediment surface mixed layer parameters from marine environments, compiled from published literature. The database contains parameters of advective (sedimentation rate) and diffusive (biodiffusion, bioturbation depth) particle movement estimated from tracer experiments, combined into box models. Database associated with the data report published under <a href="https://doi.org/10.3389/feart.2022.1013174">https://doi.org/10.3389/feart.2022.1013174</a>

opencc-by-4.0Mar 2019View details →

Online Real-Time Delphi Survey for the research project "MENARA" - Compilation of all Comments to Closed and Open Questions

Looking into the Futures: Delphi Survey about the MENA region In order to get a more realistic overview of the situation and trends, of the potentials, problems and potentials of the countries of the MENA region a Real Time Delphi survey was conducted. This is an important tool of modern future research. It was managed by the IZT- Institute for Future Studies in Berlin. A group of 139 experts and researchers from different institutes and organizations were invited to participate at the Online Real-Time Delphi Survey (RTD) about possible and likely futures of the MENA region. The experts were asked to answer questions and provide their opinions on twelve topics such as social unrest, youth unemployment, urbanization, gender equality, security etc. In this dataset all comments to the closed and the open questions are compiled. The output was one of the basic material used for the creation of future regional scenarios for mid-term (2025) and long-term (2050) time horizons. Focus scenarios were produced in order to exemplify selected characteristic and important future options, in terms of chances and risks (e.g. energy futures).

opencc-by-4.0Mar 2023View details →

Compilation of open asset-level data, as of Dec 2022

This dataset is a compilation of open asset-level data, which means the location of sites (e.g., operation, manufacturing, processing facilities of global supply chains), as of December 2022. This included data from 9 publicly available sources, that after data cleaning and harmonization, resulted in 189,075 data points.  <table> <tbody> <tr> <td>Data source</td> <td>Number of data points</td> </tr> <tr> <td><a href="https://opensupplyhub.org/">Open Supply Hub (former Open Apparel Registry)</a></td> <td>96,736</td> </tr> <tr> <td><a href="https://datasets.wri.org/dataset/globalpowerplantdatabase">Global Power Plant Database</a></td> <td>35,419</td> </tr> <tr> <td><a href="https://climatetrace.org/downloads">Climate trace</a></td> <td>19,945</td> </tr> <tr> <td><a href="https://www.fda.gov/drugs/drug-approvals-and-databases/drug-establishments-current-registration-site">FDA database</a></td> <td>12,898</td> </tr> <tr> <td><a href="https://www.globaldamwatch.org/database">Global Dam Watch</a></td> <td>11,017</td> </tr> <tr> <td><a href="https://www.ema.europa.eu/en/human-regulatory/research-development/compliance/good-manufacturing-practice/eudragmdp-database">EudraGMDP database</a></td> <td>5,181</td> </tr> <tr> <td><a href="https://www.cgfi.ac.uk/spatial-finance-initiative/geoasset-project/geoasset-databases/">Sustainable Finance Initiative GeoAsset Databases</a></td> <td>4,716</td> </tr> <tr> <td><a href="https://tailing.grida.no/disclosures">Global Tailings Portal</a></td> <td>1,956</td> </tr> <tr> <td><a href="https://www.fineprint.global/resources/mining-database/">Fine print Mining Database</a></td> <td>1,207</td> </tr> </tbody> </table> This data was assigned with the industry in which the asset is. The summary table below shows the number of assets by industry. <table> <tbody> <tr> <td>Industry</td> <td>Number of assets        </td> </tr> <tr> <td>Textiles, Apparel & Luxury Good Production             </td> <td>96,736</td> </tr> <tr> <td>Health Care, Pharma and Biotechnology</td> <td>18,079</td> </tr> <tr> <td>Energy - Solar, Wind</td> <td>16,282</td> </tr> <tr> <td>Energy - Hydropower</td> <td>14,515</td> </tr> <tr> <td>Energy - Geothermal or Combustion</td> <td>11,724</td> </tr> <tr> <td>Metals & Mining</td> <td>11,210</td> </tr> <tr> <td>Transportation Services</td> <td>4,872</td> </tr> <tr> <td>Construction Materials</td> <td>3,117</td> </tr> <tr> <td>Agriculture (animal products)</td> <td>2,388</td> </tr> <tr> <td>Agriculture (plant products)</td> <td>1,896</td> </tr> <tr> <td>Oil, Gas & Consumable Fuels</td> <td>1,194</td> </tr> <tr> <td>Water utilities / Water Service Providers</td> <td>892</td> </tr> <tr> <td>Hospitality Services</td> <td>294</td> </tr> <tr> <td>Fishing and aquaculture</td> <td>14</td> </tr> <tr> <td>Other</td> <td>5,862</td> </tr> </tbody> </table> Note that this compilation is based on an extensive search, however, we acknowledge that there is a significant discrepancy in data coverage/comprehensiveness among the different industries. The industry "Textiles, Apparel & Luxury Good Production" is by far the most complete, while other are clearly far from complete, for example, “Construction Materials”, "Agriculture (animal products)”, “Agriculture (plant products)”, “Oil, Gas & Consumable Fuels”, “Water utilities / Water Service Providers”, “Hospitality Services”, “Fishing and aquaculture”. Therefore, any comparison between industries should take this coverage/comprehensiveness bias into consideration.

opencc-by-4.0May 2023View details →

A Modular Quantum Compilation Framework for Distributed Quantum Computing

This repository contains the data used for the plots in "A Modular Quantum Compilation Framework for Distributed Quantum Computing" by D. Ferrari, S. Carretta and M. Amoretti. Data is located in the 'data' directory in .csv format, a python script to generate the plots can be found in the main directory. The script was tested with python3.10 and needs matplotlib, pandas and seaborn packages. Plots are saved as .pdf files in the 'figures' directory.

opencc-by-4.0Dec 2022View details →

Compilation of AI4DI Demonstrator Videos

This video presents the demonstrators of the project's AI4DI technologies, across the different industry domains

opencc-by-4.0Jan 2023View details →

BioDeepTime: database and compilation code

The archive includes copies, compilation code, documentation and temporary data files for the BioDeepTime database. Deposited files: <ul> <li>Relational database in SQLite format: <code>biodeeptime_sqlite.zip</code>.</li> <li>Denormalized database in zipped .csv format: <code>biodeeptime_csv.zip</code></li> <li>Denormalized database in zipped .parquet (v1.0) format: <code>biodeeptime_parquet.zip</code>.</li> <li>Denormalized database in .rds (R version 4.0) format: <code>biodeeptime.rds</code>.</li> <li>Description of tables and columns: <code>biodeeptime.md.</code></li> <li>Database schema: <code>schema.pdf.</code></li> <li>Synonymy of sources: <code>Synonymy of sources.xlsx.</code></li> <li>Change log and known issues: <code>NEWS.md</code></li> <li>Compilation files: <code>bdt_compilation.zip</code></li> <li>References in .csv format: <code>references.csv</code></li> <li>References in .rds format: <code>references.rds</code></li> <li>Reference bibtex entries: <code>references.bib</code></li> <li>Bchron ages calculated for Neotoma: <code>neotoma_bchron.rds</code></li> </ul> This repository accompanies the study BioDeepTime: a database of biodiversity time series for modern and fossil assemblages by Smith et al. (In Press).

openCC (other)Dec 2021View details →

Northeastern Mountain Ponds Geochemistry Compilation 1978-2019

We compiled geochemical data from published, peer-reviewed sources, gray literature, online datasets, unpublished researcher datasets, and our own data from high-elevation ponds and small lakes. Mountain ponds were defined as lakes and ponds situated at elevation >500 m (460 m in the Berkshires), and ponds surface area <60 ha. Many of our data sets are part of the US EPA LTM (Long-Term Monitoring) Network and its predecessor projects (e.g., Maine HELM, ELS-II, various scoping efforts for LTM), and state data repositories. We queried data providers and EPA staff about mountain ponds datasets in the region. We defined the region of interest (“the northeastern US”) as the Northern Appalachian Region, plus the Adirondack Mountains in New York State, ranging from latitude 42◦–46◦ north and longitude 75◦–69◦ west. We classified ponds into their respective mountain regions within Level II Ecoregion 58 – Northern Highlands, within Eastern Temperate Forest: Western Mountains (Maine’s Mahoosuc and White Mountains, to the terminus of the Appalachian Trail in Baxter State Park); White Mountains (in New Hampshire); Green Mountains (in Vermont); Berkshires (Western Massachusetts), and Adirondacks (in Adirondack Park, NY), to aid in sub-regional comparisons and statistical trend analyses.

openCC0Jan 2018View details →

Compiled long-term community composition datasets of primary producers and consumers in both freshwater and terrestrial communities

This data package consists of two files to study long-term changes in communities across a range of systems (freshwater and terrestrial) and organism types (from short-lived (sub annual) to long-lived species) that are both primary producers and consumers. We compiled many datasets from publicly available archives (41 datasets are from 14 LTER sites). All datasets must have had a measure of species level abundance to calculate more derived community ecological metrics beyond species richness. These data can be used to study community dynamics over space and time.

openCC0Jul 2024View details →

Grass Yield Compilation from University of Wisconsin Extension Grass Variety Trials (1983-2016)

Beginning in 1983, the UW Madison Extension has been conducting yield trials of grass varieties annually to be published in UW Extension Publication A1525. These trials are conducted at several research stations in the state of Wisconsin: Arlington, Lancaster, Marshfield and Spooner Agricultural Stations. Yield is collected for each grass variety at each cutting. The work was started by Dr. Michael Casler and was continued by Dr. Daniel Undersander. In this data set, the individual grass varieties have been aggregated to species level. Yield is recorded as the average annual total for each species at each location planted in a given year, in tons/acre. Grass species included are: bluegrass (Poa pratensis L.), festulolium (Festulolium braunii K.A.), Italian ryegrass (Lolium multiflorum Lam.), meadow bromegrass (Bromus riparius Rehmann), meadow fescue [Schedonorus pratensis (Huds.) P. Beauv], orchardgrass (Dactylis glomerata L.), perennial ryegrass (Lolium perenne L.), quackgrass (Elymus repens L.), reed canarygrass (Phalaris arundinacea L.), smooth bromegrass (Bromus inermis Leyss.), tall fescue [Lolium arundinaceum (Schreb.) Darbysh], and timothy (Phleum pratense L.).

openCC0Mar 2022View details →

Unregulated Wells on the Navajo Nation Data Compilation

In the United States the use of unregulated water sources – defined as sources that do not meet criteria to be classified as a public water system as defined by the Safe Drinking Water Act - are used regularly for livestock watering, agriculture, domestic, and other purposes. Nationally, more than 45 million people rely on unregulated water sources for drinking water; however, there remains infrastructure disparities for drinking water access in communities on Tribal nations. For the Navajo Nation, a sovereign Indigenous nation in the Southwestern United States, between 7% and 30% of homes lack plumbing to deliver household drinking water, so residents are compelled to access other water sources – regulated and unregulated alike. Previous unregulated water quality studies on the Navajo Nation were regionally focused and unsuitable for evaluating water quality trends across the Navajo Nation, an area that encompasses more than 71,000 square kilometers in Arizona, New Mexico, and Utah. Therefore, beginning in 2011 the Community Environmental Health Program at the University of New Mexico began to compile existing water quality datasets, principally for unregulated groundwater sources, in a single geospatial relational database. Researchers at the University of New Mexico Center for Native Environmental Health Equity Research, University of New Mexico METALS Superfund Research Program, University of Arizona, Northern Arizona University, and the Southwest Research and Information Center have compiled a database of water quality measurements from groundwater wells on the Navajo Nation using data from the U.S. Geological Survey, U.S. Army Corps of Engineers, U.S. Centers for Disease Control and Prevention, Navajo Nation Environmental Protection Agency, and data from researchers at the University of New Mexico, Dine College and Northern Arizona University. To date, this data compilation has been used for publications but has not been disseminated publicly. The purpose of t

openOpenDec 2015View details →

Vegetation composition for 84 sites in the Regional Site Network, compilation from previous surveys.

Many sites in the LTER regional site network (RSN) were adopted from preexisting site networks. Prior to becoming a part of the RSN, releve surveys were conducted at or near the current RSN site using the Braun-Bluanquet cover estimate scale. These data have been compiled for use as a baseline. It is important to note that many of the young successional sites were last surveyed in 2006, 2 years after fire.

openCC (other)Jan 2020View details →

Compilation of Land Use Data in 21 and 37 Category Classifications - Ipswich and Parker River Watersheds - 1971, 1985, 1991, and 1999 - Vector Shapefile.

The MassGIS Land Use datalayer has 37 land use classifications interpreted from 1:25,000 aerial photography. This layer contains data for 21 and 37 category classifications for the years of 1971, 1985, 1991, and 1999. Coverage is complete for all towns that fall partially or completely within the Ipswich River and/or Parker River watersheds. Data compiled for 1971, 1985, 1991, and 1999.

opencc-by-4.0Dec 2019View details →

Measurement data used in "Thermal and porosity properties of meteorites: A compilation of published data and new measurements".

Measurement data used in “Thermal and porosity properties of meteorites: A compilation of published data and new measurements”. Includes the measurement data as a csv file, as well as 3D models and images of the measured meteorites as zip archives.

opencc-by-4.0Jan 2020View details →

ASPLOS20-AE Artifact Dataset for 'Noise-Aware Dynamical System Compilation for Analog Devices with Legno'

The empirical model database and dataset for the ASPLOS 2020 Paper 'Noise-Aware Dynamical System Compilation for Analog Devices with Legno'

opencc-by-4.0Jul 2020View details →

Spherical Headgear HRIR Compilation of the Neumann KU100 and the Head acoustics HMS II.3

[1] C. Pörschmann, J. M. Arend, and R. Gillioz, “How wearing headgear affects measured head-related transfer functions,” in Proceedings of the EAA Spatial Audio Signal Processing Symposium, 2019, pp. 49–54. DOI link: <a href="https://doi.org/10.25836/sasp.2019.27">https://doi.org/10.25836/sasp.2019.27</a> Files also available at  in SOFA file format at <a href="http://sofacoustics.org/data/database/thk/">sofacoustics.org/data/database/thk/</a> _______________________________________________________________________________________________________ The spatial representation of sound sources is an essential element of virtual acoustic environments (VAEs). When determining the sound incidence direction, the human auditory system evaluates monaural and binaural cues, which are caused by the shape of the pinna and the head. While spectral information is the most important cue for elevation of a sound source, we use differences between the signals reaching the left and the right ear for lateral localization. These binaural differences manifest in interaural time differences (ITDs) and interaural level differences (ILDs). In many headphone-based VAEs, head-related transfer functions (HRTFs) are used to describe the sound incidence from a source to the left and right ear, thus integrating both monaural and the binaural cues. Specific aspects, like for example the individual shape of the head and the outer ears (e.g. Bomhardt, 2017), of the torso (Brinkmann et al., 2015), and probably even of headgear (Wersenyi, 2005; Wersenyi, 2017) influence the HRTFs and thus probably as well localization and other perceptual attributes. Generally speaking, spatial cues are modified by headgear, for example by wearing a baseball cap, a bicycle helmet, or a head-mounted display, which nowadays is often used in VR applications. In many real life situations, however, a good localization performance is important when wearing such items, e.g. in order to determine approaching vehicles when cycling. Furthermore, when performing psychoacoustic experiments in mixed-reality applications using head-mounted displays, the influence of the head-mounted display on the HRTFs must be considered. Effects of an HTC Vive head-mounted display on localization performance have already been shown in Ahrens et al. (2018). To analyze the influence of headgear for varying directions of incidence, measurements of HRTFs on a dense spherical sampling grid are required. However, HRTF measurements of a dummy head with various headgear are still rare, and to our knowledge only one dataset measured for an HTC Vice on a sparse grid with 64 positions is freely accessible (Ahrens, 2018). This work presents high-density measurement data of HRTFs from a Neumann KU100 and a HEAD acoustics HMS II.3 dummy head, either equipped with a bicycle helmet, a baseball cap, an Oculus Rift head-mounted display, or a set of extra-aural AKG K1000 headphones. For the measurements, we used the VariSphear measurement system (Bernschütz, 2010), allowing precise positioning of the dummy head at the spatial sampling positions. The various HRTF sets were captured on a full spherical Lebedev grid with 2702 points. In our study, we analyze the measured datasets in terms of their spectrum, their binaural cues, and regarding their localization performance based on localization models, and compare the results to reference measurements of the dummy heads without headgear. The results show that differences to the reference without headgear vary significantly depending on the type of the headgear. Regarding the ITDs and ILDs, the analysis reveals the highest influences for the AKG K1000. While for the Oculus Rift head-mounted display, the ITDs and ILDs are mainly affected for frontal directions, only a very weak influence of the bicycle helmet and the baseball cap on ITDs and ILDs was observed. For the spectral differences to the reference the results show maximal deviations for the AKG K1000, the lowest for the Oculus Rift and the baseball cap. Furthermore, we analyzed for which incidence directions the spectrum is influenced most by the headgears. For the Oculus Rift and the baseball cap, the strongest deviations were found for contralateral sound incidence. For the bicycle helmet, the directions mostly affected are as well contralateral, but shifted upwards in elevation. Finally, the AKG K1000 headphones generally has the highest influence on the measured HRTFs, which becomes maximal for sound incidence from behind. The results of this study are relevant for applications where headgears are worn and localization or other aspects of spatial hearing are considered. This could be the case, for example in mixed-reality applications where natural sound sources are presented while the listener is wearing a head-mounted display, or when investigating localization performance in certain situations, e.g. in sports activities where headgears are used. However, it is an important intention of this study to provide a freely available database of HRTF sets which is well suited for auralization purposes and which allows to further investigate the influence of headgear on auditory perception. The HRTF sets will be publicly available in the SOFA format under a Creative Commons CC BY-SA 4.0 license. ________________________________________________________________________________________________________ Contact: Christoph Pörschmann TH Köln - University of Applied Sciences Institute of Communications Engineering Department of Acoustics and Audio Signal Processing Betzdorfer Str. 2, D-50679 Cologne, Germany <a href="https://www.th-koeln.de/akustik">https://www.th-koeln.de/akustik</a> _________________________________________________________________

opencc-by-4.0Nov 2020View details →

Higher Education Research: A Compilation of Journals and Abstracts 2016

Revised dataset to original publication: Hertwig, Alexandra (2017): Higher Education Research: A Compilation of Journals and Abstracts 2016. Kassel: INCHER-Kassel. <a href="http://www.uni-kassel.de/einrichtungen/fileadmin/datas/einrichtungen/incher/Higher_Education_Research_-_A_Compilation_of_Journals_and_Abstracts_2016.pdf">http://www.uni-kassel.de/einrichtungen/fileadmin/datas/einrichtungen/incher/Higher_Education_Research_-_A_Compilation_of_Journals_and_Abstracts_2016.pdf</a> (accessed November 04, 2020) Datasets to volume 2013 to 2018 might vary with regard to covered journals of the original publication. The datasets include journals and respective publication data providing persistent identifiers, explicitly. The Research Information Service (RIS) of INCHER-Kassel, Germany provides annual compilation of academic journals since 2013. The datasets allow for further evaluation of single or multiple volumes. For more information on original publications and available datasets please visit INCHER’s RIS websites. http://www.uni-kassel.de/einrichtungen/en/incher/risspecial-research-library/ris-documents.html

opencc-by-4.0Feb 2024View details →

Danum/Malua Compiled Climate Data 1985 to 2024

<div> <h2>Description</h2> A compiled dataset containing key climatic variables collected at the weather stations within the Danum Valley Field Center and Malua basecamp between 1985 to 2024.  Key climatic variables that were collected include daily minimum and maximum temperatures (in celcius), daily relative humidities at 8 am and 2 pm, daily rainfall (in mm), and periods when the Sun is present (in hours). Note for users: 1. In the case for Danum, measurements taken for temperatures and relative humidities were inconsistent prior to 1990 so do not be alarmed with the huge amount of NAs during this period. There is also long periods of no measurements (>6 months) in 2017 due to data loggers not working properly. 2. In the case for Malua, consistent measurements for temperatures and relative humidities were taken only after 2008. Also, measurements taken between January 2020 to July 2023 were inconsistent due to the COVID-19 pandemic. 3. In all cases, we included period of Sun only after 2008. Version 3.0: 1. We have included climate data collected from 2024. <h2>Funding</h2> These data were collected as part of research funded by: <ul> <li>Swansea University (Standard grant )</li> <li>Royal Society (Standard grant )</li> </ul>   This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs. <h2>Files</h2> This dataset consists of 1 file: SEARPP_compiled_climate_data_2024.xlsx <h3>SEARPP_compiled_climate_data_2024.xlsx</h3> This file contains dataset metadata and 1 data tables: <h3>Danum/Malua Compiled Climate Data 1985 to 2024</h3> <ul> <li>Worksheet: SEARPP_compiled_climate_data</li> <li>Description: A compiled dataset containing key climatic variables collected at the weather stations within the Danum Valley Field Center and Malua basecamp between 1985 to 2024.  Key climatic variables that were collected include daily minimum and maximum temperatures (in celcius), daily relative humidities at 8 am and 2 pm, daily rainfall (in mm), and periods when the Sun is present (in hours).</li> <li>Number of fields: 10</li> <li>Number of data rows: 28858</li> <ul> <li>year: Year of survey (type: replicate)</li> <li>month: Month of survey (type: replicate)</li> <li>day: Day of survey (type: replicate)</li> <li>location: Location of weather stations (type: location)</li> <li>tmax: Maximum daily temperature (type: numeric)</li> <li>tmin: Minimum daily temperature (type: numeric)</li> <li>rh8: Relative humidity at 8 am (type: numeric)</li> <li>rh14: Relative humidity at 2 pm (type: numeric)</li> <li>rain: Daily rainfall amount (type: numeric)</li> <li>sun: Duration of bright sunshine (type: numeric)</li> </ul> </ul> <h2>Extents</h2> <ul> <li>Date range: 1985-07-01 to 2024-12-31</li> <li>Latitudinal extent: 4.9° to 5.1°</li> <li>Longitudinal extent: 117.6° to 117.9°</li> </ul> </div>

opencc-by-4.0Nov 2023View details →

Low-temperature thermochronology database of the Carpathian fold-and-thrust belt (ZFT; ZHe; AFT; AHe) compilation from 1999 to 2023

The following tables contain the low-temperature thermochronology dataset used in the Carpathian belt's exhumation model inversion. This dataset includes information from four thermochronometers: Apatite and Zircon Fission Track (AFT and ZFT), as well as (U-Th)/He on Apatites and Zircons (AHe and ZHe). Compiled from 1999 to 2023, this database encompasses available literature data related to low-temperature thermochronology in the region. Excel table as well as a PDF file describe the database.

opencc-by-4.0Nov 2024View details →

Compilations of Palaeogene deep-sea diatom-bearing sediments and associated data

deep_sea_diatoms.xls contains a compilation of Palaeogene deep-sea diatom-bearing sediments and associated cherts. rads_from_smear_slides.csv is an update on the radiolarian dataset reported in Renaudie (2016). ageprofiles_tab.csv contains a compilation of deep-sea drilling sites containing sediments of specific ages.