Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
12,393
datasets available to search
ShareScore release 0.9.0
Dataset results
12,393 results for “materiality”
Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts (Instructor Materials)
This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Forecast uncertainty is derived from multiple sources, including model parameters and driver data, among others. Knowing the uncertainty associated with a forecast enables forecast users to evaluate the forecast and make more informed decisions. This module will guide students through an exploration of the sources of uncertainty within an ecological forecast, how uncertainty can be quantified, and steps that can be taken to reduce the uncertainty in a forecast that students develop for a lake ecosystem, using data from the National Ecological Observatory Network (NEON). Students will visualize data, build a model, generate a forecast with uncertainty, and then compare the contributions of various sources of forecast uncertainty to total forecast uncertainty. The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module6/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module6) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module6_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at https://zenodo.org/doi/10.5281/zenodo.10380759 and RMarkdown versi
Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts (Instructor Materials)
This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. To be useful for management, ecological forecasts need to be both accurate enough for managers to be able to rely on them for decision-making and include a representation of forecast uncertainty, so managers can properly interpret the probability of future events. To improve forecast accuracy, forecasts can be updated with observational data once they become available, a process known as data assimilation. Recent improvements in environmental sensor technology and an increase in the number of sensors deployed in ecosystems have increased the availability of data for assimilation to develop and improve forecasts for natural resource management. In this module, students will explore how assimilating data with different amounts of observation uncertainty and at different temporal frequencies affects forecasts of lake water quality, using data from the U.S. National Ecological Observatory Network (NEON). The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module7/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module7) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module7_R) of the module, and both code r
Abundance of stream macroinvertebrates and benthic organic material in CPCRW
During the summer of 1995 the abundance of stream macro invertebrates and benthic organic material in six of the streams in CPCRW was studied calculated. This dataset is a result of the biomass of these samples.
Biomass of stream macroinvertebrates and benthic organic material in CPCRW
Biomass of stream macroinvertebrates and benthic organic material in CPCRW collected in basins of Caribou Creek in 1995 and 1996. Data originally submitted by Jock Irons.
LCI data for materials and processes comparing energy and water use of aqueous and gas-based metalworking fluids
<p>Datasets containing life cycle inventories for materials and processes, and results of the analysis in the article titled, "Comparing energy and water use of aqueous and gas-based metalworking fluids" published in the <em>Journal of Industrial Ecology.</em></p>
Supplementary materials
<p>Supplementary materials for: Multi-tool (LA-ICPMS, EMPA and XRD) investigation on heavy minerals from selected Holocene peat-bog deposits from the upper Vistula River Valley, Poland.</p>
Supplementary Material: Patterns in research and data sharing for the study of form and function in caviomorph rodents
<p>Supplementary Material for the publication <em>Patterns in research and data sharing for the study of form and function in caviomorph rodents</em> by Luis D. Verde Arregoitia*, Pablo Teta, and Guillermo D'Elía. <em>Journal of Mammalogy</em></p>
Supplementary Materials
<p>Figure S1: Rarefaction curves of animals fed with antimicrobial-supplemented diet (AB) and unmedicated diet (UN) depending on age (prestarter I [PI] at 9 days post-weaning, starter [S] at 40 days post-weaning, and fattening [F] at 72 days post-weaning).</p> <p>Table S1: Differential abundance of minor phyla (< 1.0%) between treatments, ages, and their interaction.</p> <p>Table S2: Differential abundance of minor families (< 1.0%) between treatments, ages, and their interaction.</p> <p>Table S3: Differential abundance of minor genera (< 1.0%) between treatments, ages, and their interaction.</p>
Supplemental material to manuscript "Enhancement of adhesives strength of wood-metal joints using atmospheric plasma treatments"
<p>This dataset includes the supplementary information<strong> </strong>related to the manuscript:</p> <p>Authors: Žigon J, Kovač J, Zaplotnik R, Saražin J, Šernek M, Petrič M, Dahle S</p> <p>Title: <strong>Enhancement of adhesives strength of wood-metal joints using atmospheric plasma treatments</strong></p>
Supplement material for the paper "On the Relationship between User Churn and So!ware Issues"
<p>The data represented as CSV files containing the alternatives, the comments, the reviews, and the issue reports studied in the paper.</p>
Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model
<p>This is the supplementary material to the masters thesis:</p> <p>"NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model"</p> <p><strong>LICENSE</strong></p> <p>All output data provided is under Creative Commons Attribution 4.0 International Public License. All Python scripts provided are under Apache License, Version 2.0. Refer to the ‘Input data documentation’ file for source and data license information for the input data. For license information, refer to the LICENSE files.</p> <p><strong>DATASET DESCRIPTION</strong></p> <p>The supplementary material is organized into four different subdirectories:</p> <ul> <li>The subdirectory ‘Industrial processes’ contains the input data, Python scripts, and output data for the estimation of NUTS-3 load shifting potential of suitable electrically powered industrial processes (cement milling, mechanical pulping, paper production, air separation).</li> <li>The subdirectory ‘Process heat’ contains the input data, Python scripts, and output data for the estimation of NUTS-3 load shifting potential of industrial process heat applications that are powered by electricity.</li> <li>The subdirectory ‘Future projections’ contains the input data, Python scripts, and output data for the estimation of NUTS-0 annual average industrial processes load shifting potential in the future up until 2050.</li> <li>The subdirectory ‘Other tables for reference’ contains data tables that are not used as input data to the Python scripts, but which may serve as useful further reference for the reader. These tables are the original or intermediate tables from which the input data tables were created.</li> </ul> <p>For more information refer to the README file.</p> <p>For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the masters thesis "NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model", available here: <a href="https://elib.dlr.de/134116/">https://elib.dlr.de/134116/</a></p> <p>In case of questions please contact: bruno.schyska@dlr.de or wilko.heitkoetter@dlr.de</p> <p> </p>
Supplemental material to manuscript "Enhancement of strength of adhesive bond between wood and metal using atmospheric plasma treatment"
<p>This dataset includes the supplementary information<strong> </strong>related to the manuscript:</p> <p>Authors: Žigon J, Kovač J, Zaplotnik R, Saražin J, Šernek M, Petrič M, Dahle S</p> <p>Title: <strong>Enhancement of strength of adhesive bond between wood and metal using atmospheric plasma treatment</strong></p>
Supplementary material for "Measurement of volume change and mass transfer during serpentinisation: insights from the Oman Drilling Project" (JGR Solid Earth)
<p>The three supplementary files include one figure displaying the results of X-ray microtomography (File S1), two compilations of figures dealing with the volume change measurement (Files S2 and S3) and a table with the measured compositions in major and trace elements (Table S1).</p> <p>File S1 (Malvoisin_ds01.pdf). Result of X-ray microtomography. Olivine and Fe-brucite have a similar attenuation. The grains segmented by selecting this attenuation are displayed with a circle with a size proportional to the radius of a sphere of equivalent volume. The color of each circle depends on <em>e</em>, the aspect ratio (ratio of the length of the minimum and maximum axes of an ellipsoid fitted to each grain). The white arrow indicates the location of the main central vein. Grains near the main central vein have a lower <em>e</em> attributed to the platy habitus of Fe-brucite. The other grains are considered as olivine and used for the calculation of the extent of reaction.</p> <p>File S2 (Malvoisin_ds02.pdf). Optical photomicrographs used to calculate volume change during reaction (see main text for details about the calculation). Olivine grains (white) are surrounded by the serpentine + brucite mixture (light green to yellowish). The magnetite/clinopyroxene platelets are mapped in blue in olivine and in red in the serpentine + brucite mixture. The results of the model fitting the best the average orientation of the platelets in olivine are shown in green. The title of each photomicrograph provides the minimum and maximum estimates for volume change calculated with this model (see details about the calculation in the main text), and the size of the bottom right scale bar.</p> <p>File S3 (Malvoisin_ds03.pdf). Histograms of magnetite/clinopyroxene platelet orientation (in degree). Each histogram corresponds to a photomicrograph of File S1. The order of the histograms and the photomicrographs is the same. The orientations in the olivine grains, the serpentine + brucite mixture and for the model fitting the best the average orientation in olivine are displayed in blue, red and green, respectively. The title of the histograms provides the standard deviation of the platelet orientation in the olivine grains (STD<sub>oli</sub>), the serpentine + brucite mixture (STD<sub>ori</sub>) and calculated with the model fitting the best the orientation in olivine (STD<sub>cal</sub>).</p> <p>Table S1 (Malvoisin_ds01.xlsx). Composition in major and trace elements measured with LA-ICPMS along five profiles perpendicular to the main vein. The composition as a function of the distance to the main vein is provided. The name of the tabs indicates if the profile was acquired in spot or in continuous mode (the two modes are described in the main text).</p>
Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2019 Competition (Pisa, Italy).</p><p><strong>Contents:</strong></p><ol><li>IPIN2019_Call4Competition: Call for competition and main rules</li><li>IPIN2019_Track03_TechnicalAnnex: Technical annex describing the Track 3 of the competition.</li><li>01-Logfiles: This folder contains a subfolder with the 50 (40 + 10) training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.</li><li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes.</li><li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li></ol><p><strong>Please, cite the following works when using the datasets included in this package:</strong></p><ul><li>Jiménez, A. R.; Perez-Navarro, A.; Crivello, A.; Mendoza-Silva, G.; Ortiz, M.; Perul, J.; Seco, F. and Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site), Zenodo 2019. <a href="http://dx.doi.org/10.5281/zenodo.3606765">http://dx.doi.org/10.5281/zenodo.3606765</a></li><li>Potorti, F.; Park, S.; Palumbo, F.; Girolami, M.; Barsocchi, P.; Lee, S.; Torres-Sospedra, J.; Jimenez Ruiz, A. R.; Perez-Navarro, A.; Mendoza-Silva, G. M.; Seco, F.; Ortiz, M.; Perul, J.; Renaudin, V.; Kang, H.; Park, S. Y.; Lee, J. H.; Park, C. G.; Ha, J.; Han, J.; Park, C.; KIM, K.; Lee, Y.; GYE, S.; Lee, K.; Kim, E.; Choi, J.-S.; Choi, Y.-S.; Talwar, S.; Cho, S. Y.; Ben-Moshe, B.; Sansano, E.; Chidlovskii, B.; Kronenwett, N.; Prophet, S.; Landay, Y.; Marbel, R.; Peng, A.; Wu, B.; MA, C.; Poslad, S.; Selviah, D.; Wu, W.; Ma, Z.; Zhang, W.; Wei, D.; Yuan, H.; Jiang, J.-B.; Liu, J.-W.; Su, K.-W.; Leu, J.-S.; Nishiguchi, K.; Bousselham, W.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.-I.; Cortés, V.; Lungenstrass, T.; Ashraf, I.; Lee, C.; Usman Ali, M.; Im, Y.; Kim, G.; Eom, J.; Hur, S.; Park, Y.; Opiela, M.; Moreira, A.; Nicolau, M. J.; Pendão, C.; Silva, I.; Meneses, F.; Costa, A.; Trogh, J.; Plets, D.; Chien, Y.-R.; Chang, T.-Y.; Fang, S.-H.; Tsao, Y. The IPIN 2019 Indoor Localisation Competition - Description and Results IEEE Access Vol. 8, pp. 206674-206718, 2020. https://doi.org/10.1109/ACCESS.2020.3037221</li></ul><p><strong>Additional information can be found at:</strong></p><ul><li><a href="http://evaal.aaloa.org/2019/call-for-competitions">http://evaal.aaloa.org/2019/call-for-competitions</a></li></ul><p><strong>For any further questions about the database and this competition track, please contact: </strong></p><ul><li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>,<a href="mailto:torres@ubikgs.com?subject=Contact%20from%20Zenodo%203606765">torres@ubikgs.com</a>) UBIK Geospatial Solutions S.L., Spain<br>Institute of New Imaging Technologies, Universitat Jaume I, Spain.</li><li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain.</li></ul>
Training data for 'Unicycler assembly of SARS-CoV-2 genome with preprocessing to remove human genome reads' tutorial (Galaxy Training Material)
<p>The data here is a copy of the corresponding SRR records in the NCBI SRA. The duplication serves a dual purpose:</p> <ol> <li>as a backup should there be problems connecting to NCBI servers, e.g., during Galaxy user trainings.</li> <li>to illustrate how to obtain raw sequencing data from alternative sources, and to organize the data into the same collection structure in a Galaxy history that is generated by specialized Galaxy SRA download tools.</li> </ol>
Test materials for the Adaptive Melodic Discrimination Test
<pre>This repository contains stimuli and psychometric data for the adaptive melodic discrimination test constructed in Harrison et al. (2017). These materials can be used to construct a computerised adaptive melodic discrimination test. An implementation of the test is available from the authors upon request. This version (v5) corresponds to the final version of the test constructed in Harrison et al. (2017); it includes the easier items added to the item bank in Study 3. It also contains a corrected melodic_discrimination_psych_data.csv file; the previous version was incomplete. These test materials may be freely used for research purposes. Please cite Harrison et al. (2017) in any publications using these materials (see below for full citation info). ==Reference== Harrison, P. M. C., Collins, T., & Müllensiefen, D. (2017). Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item generation. Scientific Reports, 7, 1–18. https://doi.org/10.1038/s41598-017-03586-z</pre>
The relative influence of catchment and site variables on fish and macroinvertebrate richness in Cerrado biome streams. Supplemental Materials Table SM2
<p>Table SM2. Environmental variable descriptions (mean, standard deviation and transformation applied) and<br> relationships with assemblage richness across all 80 sites</p>
Datasets and images of publication: Self-healing and high interfacial strength in multi-material soft pneumatic robots via reversible Diels-Alder bonds
<p>Data and Figures of the publication:</p> <p>Terryn, S.; Roels, E.; Brancart, J.; Assche, G.V.; Vanderborght, B. Self-Healing and High Interfacial Strength in Multi-Material Soft Pneumatic Robots via Reversible Diels–Alder Bonds. <em>Actuators</em> <strong>2020</strong>, <em>9</em>, 34.</p>
supplementary materials (source code and raw experimental results) for the paper Precision, Recall, and Sensitivity of Monitoring Partially Synchronous Distributed Programs
<p>This is the supplementary materials (source code and raw experimental results) for the paper Precision, Recall, and Sensitivity of Monitoring Partially Synchronous Distributed Systems.</p> <p>Please contact Duong Nguyen (<a href="mailto:nguye476@msu.edu">nguye476@msu.edu</a>) for any questions.</p> <p>Content:</p> <pre><code>TPDS-Precision-Recall-Upload | +- program: source code | +- aws-experiment: source code for running programs and monitors on Amazon Web Service (AWS) | +- interval-based | +- lib: relevant libraries for compiling code | +- wcp-process: source code of programs | +- wcp_process_compile_and_export_jar.sh: script for compiling program | +- simulations: source code for simulations | +- data: experiment and simulation results</code></pre>
Supplementary Material for the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting
<p>This is the dataset used in the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting. </p> <p>It is composed of: </p> <p>- Pre-trained models using active learning for document screening on HealthCLEF and Epistemonikos datasets. </p> <p>- Epistemonikos and HealthCLEF datasets containing medical questions and relevant/non relevant articles. </p> <p>- Embeddings and Document Representations used for experiments on both datasets. </p> <p>Scripts to run experiments can be found at: <a href="https://github.com/afcarvallo/active_learning_document_screening">https://github.com/afcarvallo/active_learning_document_screening</a></p> <p> </p> <p><strong>Paper abstract:</strong></p> <p>Document screening is a fundamental task within Evidence-based Medicine (EBM), a practice that provides scientific evidence to support medical decisions. Several approaches have tried to reduce physicians' workload of screening and labeling vast amounts of documents to answer clinical questions. Previous works tried to semi-automate document screening, reporting promising results, but their evaluation was conducted on small datasets, which hinders generalization. Moreover, recent works in natural language processing have introduced neural language models, but none have compared their performance in EBM. In this paper, we evaluate the impact of several document representations such as TF-IDF along with neural language models (BioBERT, BERT, Word2vec, and GloVe) on an active learning-based setting for document screening in EBM. Our goal is to reduce the number of documents that physicians need to label to answer clinical questions. We evaluate these methods using both a small challenging dataset (HealthCLEF 2017) as well as a larger one but easier to rank (Epistemonikos). Our results indicate that word as well as textual neural embeddings always outperform the traditional TF-IDF representation. When comparing among neural and textual embeddings, in the HealthCLEF dataset the models BERT and BioBERT yielded the best results. On the larger dataset, Epistemonikos, Word2Vec and BERT were the most competitive, showing that BERT was the most consistent model across different corpuses. In term of active learning, an uncertainty sampling strategy combined with logistic regression achieved the best performance overall, above other methods under evaluation, and in fewer iterations.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.