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.
958
datasets available to search
ShareScore release 0.9.0
Dataset results
958 results for “Data quality”
Supplementary data for "Individual quality and environmental factors interact to shape reproduction and survival in a resident bird of prey"
<p><strong>Abstract</strong></p> <p>The archive contains data file and R-Script to reproduce the results presented in the paper “Individual quality and environmental factors interact to shape reproduction and survival in a resident bird of prey” published in Royal Society Open Science.</p> <p><strong><span> </span></strong></p>
Data from multi-sensor devices and reference station to monitoring urban air quality
<p>Data from electrochemical and optical sensors.</p> <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td><strong>Electrochemical sensor</strong></td> <td><strong>Optical sensor</strong></td> <td><strong>Probe</strong></td> <td><strong>Reference</strong></td> </tr> <tr> <td> <p>AE = auxiliary electrode (mV)</p> <p>WE = working electrode (mV)</p> <p>N = temperature correction </p> <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> <p>PM1, PM2.5 and PM10 in µg/m³</p> </td> <td> <p>Prs_mbar = pressure (mbar)</p> <p>Temp = temperature (°C)</p> <p>RH = relative humidity (%)</p> </td> <td> <p>DV30 = wind direction @ 30m (°)</p> <p>HR = relative humidity (%)</p> <p>NO, NO2, O3, PM10 and PM2.5 (µg/m³)</p> <p>Precipita = precipitation (mm)</p> <p>TC3 = temperature @ 3m (°C)</p> <p>VV30 = wind speed @ 30m (m/s)</p> </td> </tr> </tbody> </table> <p> </p>
Data from an experiment on the effects of using an experimental framework on the quality of research designs
Open the record for dataset details and reuse information.
Data and scripts used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Images, script and data used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
China's historical 1km resolution eco-environmental quality data (CHEQ)
<p>The China's High-resolution Eco-Environmental Quality (CHEQ) dataset provides 1-km historical eco-environmental quality (EEQ) during years 2001-2019. This dataset archived in TIFF format was jointly developed by Beijing Normal University, Southern University of Science and Technology and Peking University based on multi-source remote sensing data. The dataset covers the entire land of China with a yearly temporal resolution. The existing validation results show that the CHEQ dataset was highly consistent with the environmental index (EI) provided by the Ministry of Ecology and Environment of China. The CHEQ fills the research gap in the field of EEQ monitoring in China.<br> </p>
PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Survey at citizen level and data relative to regional performance of the Cohesion Policy and institutional quality
<p>1. Orignal PERCEIVE survey data (STATA file)</p> <p>2. description of survey questions, descriptive results (word file)</p> <p>3. EU Deliverable document with descriptive analysis of survey questions</p> <p> </p> <p>***please cite the following when using the microdata:</p> <p>Bauhr, M., & Charron, N. (2020). The EU as a savior and a saint? Corruption and public support for redistribution. <em>Journal of European Public Policy</em>, <em>27</em>(4), 509-527.</p> <p>https://www.tandfonline.com/doi/full/10.1080/13501763.2019.1578816</p>
Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality - Collected Data
<p>The deposited data files were used to perform the analysis introduced in the paper: Tamas Galli, Francisco Chiclana and Francois Siewe, "Genetic Algorithm Based Fuzzy Inference System for Describing Execution Tracing Quality", Mathematics, MDPI, 2021.</p> <p>The data were collected through an online questionnaire. The questionnaire has been exported in pdf format and uploaded as file: form_data_collection.pdf. The paper above introduces the steps of analysing, processing the data, constructing, pre-validating the model. The final validation was done over the online questionnaire exported and uploaded in pdf format as form_model_validation.pdf.</p> <p>Questionnaire Part 1, data file: all_usecases_wide.csv</p> <p>The CSV file contains the responses for each use case of part 1 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. The following variables are linked to each use case: Accuracy, Legibility, DesignAndImplementation, and Security. These form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. Each fifth column is followed by a column UseCase to designate the use case which is described by the previous five columns. The definitions of the variables can be found in the questionnaire.</p> <p>Questionnaire Part 2, data file: all_real_projects_scores.csv</p> <p>The CSV file contains the responses for real projects in part 2 of the online questionnaire enclosed. The columns contain the assigned values from the respondents, on a scale [0; 100]. Six variables are linked to each response: Accuracy, Legibility, DesignAndImplementation, and Security, which form the input variables of execution tracing quality, while the variable Quality designates the quality of execution tracing. In addition, the variable Type indicates the type of the project, such as server application, desktop application, web UI, mobile application, or embedded application. The definitions of the variables can be found in the questionnaire.</p> <p>Questionnaire Part 3, data file: all_extrem_values_wide.csv</p> <p>The CSV file contains the assigned execution tracing quality value to the provided combination of extreme input values in part 3 of the online questionnaire enclosed. The column IDs represent the question IDs in the survey. The definitions of the variables can be found in the questionnaire.<br> </p>
Supporting data for "Quality control for the target decoy approach for peptide identification"
<p>Supporting data for the manuscript "Quality control for the target decoy approach for peptide identification". Input files are raw mass spectrometry runs, to be downloaded from the PRIDE Archive (https://www.ebi.ac.uk/pride/), which can be processed with the parameter files, Nextflow workflow, and Python scripts in "workflow-scripts-parameters.zip". The resulting output files that were used for the manuscript are provided in search-results.zip.</p>
Data and Code for "Value dissonance in research(er) assessment: Individual and institutional priorities in review, promotion and tenure criteria related to research quality, quantity, openness and responsibility"
<p>This snapshot contains code and data for the preprint "Value dissonance in research(er) assessment: Individual and institutional priorities in review, promotion and tenure criteria related to research quality, quantity, openness and responsibility".</p> <p>Instructions on re-using the data and running the code can be found in the README.md.</p> <p>Changes:</p> <ul> <li>Added survey instrument and informed consent.</li> </ul>
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
<p>1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. </p> <p>2. State-space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error-prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time-consuming to fit to diverse animal tracking data sets. </p> <p>3. The R package <em><span>aniMotum</span></em> eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: 1) a simple but extensible workflow that accommodates both novice and experienced users; 2) automated processes that alleviate complexity from data processing and model specification/fitting steps; 3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. </p> <p>4. We highlight <em>aniMotum</em>'s<em> </em>capabilities through three applications to real animal tracking data. Full R code for these and additional applications are included as Supporting Information so users can gain a deeper understanding of how to use <em>aniMotum</em> for their own analyses. </p>
Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk, Data
<p>This dataset accompanies the publication, "Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk", and can be used with the code located at https://zenodo.org/badge/latestdoi/248010532 to reproduce our results.</p>
Data from: Balanced replacement of fish meal with Hermetia illucens meal allows efficient hepatic nutrient metabolism and increased fillet lipid quality in gilthead sea bream (Sparus aurata) juveniles
<p>In the present study, gilthead sea bream (<em>Sparus aurata</em>) juveniles were reared using sustainable feeds containing insect meal from <em>Hermetia illucens</em> larvae and poultry by-products meal. Proteomics and Proton Nuclear Magnetic Resonance-based metabolomics analysis were used to assess the metabolic impact of tested dietary formulations in sea bream liver, whereas the composition of muscle fillet was characterized by means of metabolomics and gas chromatography of fatty acids methyl esters. Replacing fish meal with insect meal in a 5% fish meal diet did not substantially alter metabolism of dietary nutrients, leading to small but statistically detectable effects solely on lauric acid content of sea bream fillet, and few alterations in some markers of immune response, such as leukocyte elastase inhibitor-like, granzyme B (G,H)-like, and two associated ortholog groups namely serpin B, and chymase). Liver morphology confirmed the absence of structural damage or inflammation in the insect meal-fed group, which showed a lower amount of hepatic lipid deposition and accumulation, too.</p>
Virtual NGS data used in the paper "Optimal k-mer values for good-quality triploid genome assemblies" and its assembly results
<p>Despite technological advancements, whole-genome sequencing remains technically challenging for organisms with higher-ploidy genomes. Therefore, the use of short-read sequencing platforms for this purpose has been attempted, but the conditions that result in poor-quality genomes have not been elucidated. Therefore, in the present study, simulated sequences mimicking the accumulation of insertion/deletion mutations were created to clarify the permissible differences between homologous chromosomes and the k-mer sizes for good-quality genome assembly from short-read sequencing data for triploid species. The results illustrated that a narrow range of k-mers permits the generation of high-quality assemblies for any level of difference between homologous chromosomes. <br> This dataset consists of the virtual haploid genome (O.fasta), triploid genome data (reference_sequense), NGS read data (NGS_reads), and assembly results (all_contigs) created in this study.<br> </p>
Data for: Strong effects of food quality on host life history do not scale to impact parasitoid efficacy or life history
<p><span>Parasitoids are small insects, (e.g., small wasps or flies) that reproduce by laying eggs on or within host arthropods. Parasitoids make up a large proportion of the world's biodiversity and are popular agents of biological control. Idiobiont parasitoids paralyze their hosts upon attack and thus are expected to only target hosts large enough to support offspring development. Host resources generally impact host attributes and life histories including size, development, and life span. Some argue slow host development in response to resource quality increases parasitoid efficacy (i.e., a parasitoid's ability to successfully reproduce on or within a host) due to longer host exposure to parasitoids. However, this hypothesis is not always supported and does not consider variation in other host traits in response to resources that may be important for parasitoids (e.g., variation in host size is known to impact parasitoid efficacy). In this study, we test whether trait variation within host developmental stages in response to host resources is more important for parasitoid efficacy and life histories than trait variation across host developmental stages. We exposed seed beetle hosts raised on a food quality gradient to mated female parasitoids and measured the number of hosts parasitized and parasitoid life history traits at the scale of host stage- and age-structure. Our results suggest host food quality does not cascade to impact idiobiont parasitoid life histories despite large food quality effects on host life history. Instead, variation in host life histories across host developmental stages better predicts parasitoid efficacy and life histories, suggesting finding a host in a specific instar is more important for idiobiont parasitoids than finding hosts on or within higher quality resources.</span></p>
Supporting data for the manuscript "Protein quality assessment with graph convolution guided by a loss function designed for high quality decoys"
<p>This dataset provides the predictions of the protein quality<br> assessment method Q_epsilon with respect to CASP13 and CASP14, as well<br> as a snapshot of the github repository providing the code for the<br> method.<br> The format of the file is as follows: The first column represents the<br> target and decoy names in the format <target name>_<decoy name>. The<br> second column is the true GDTTS, and the third column represents the<br> predicted GDTTS by Q_epsilon.</p>
Supporting data and tool, for the paper "A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe"
<p>This 'Zenodo' contains supporting data and tools, for the paper 'A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe'.</p> <p>The repository includes the source code (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/DELTA_7.2.zip">DELTA_7.2.zip</a>) and the dataset (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/GMD_Vitali_et_all_data_20230516.zip">GMD_Vitali_et_all_data_20230516.zip</a>).</p>
Multiparameter Water Quality Monitoring System for Continuous Monitoring of Fresh Waters Calibration and Measurement Data Set
<p>This data set contains calibration data for all sensors incorporated in the sensor node. It provides comparison measurements of TPL fluorescence taken by the node and reference spectrofluorimeter. Initial test measurements, as well as site measurements, are also provided. Finally, data from a heuristic method of TPL detection in the presence of algae and mud are also given.</p>
Dataset for the paper submitted for peer-review with the title "Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model"
<p>The proposed dataset is related to the following article submitted for peer review:</p> <p>Hasanyar, M., Flipo, N., Romary, T., Wang, S. (2023), Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model, UNDER PEER-REVIEW</p> <p>It consists of command files for the prose-pa0.74 software available here: https://gitlab.com/prose-pa/prose-pa </p> <p>To run the model :</p> <p>1. Compile prose-pa0.74</p> <p>2. Copy the executable in the current directory</p> <p>3. In a terminal launch</p> <p>> ./prose-pa0.74 simulation.COMM test.log</p> <p>The “simulation.COMM” holds the settings for the ProSe-PA simulation related to the paper mentioned in the front head of the current file. </p> <p>The information on different parameters of “simulation.COMM” are included in “bathymetrie”, “Cmds”, “Inflows”, “layers”, “meteo”, “o2_obs”, “param_bio”, “Reaches” and “Singularities” folders.</p> <p>The “bathymetrie” folder holds the geometric information of several cross-sections along the river. </p> <p>The Cmds folder holds the “simulation.COMM” file. </p> <p>The “Inflows” folder the information about the boundary condition inflows to the river such as discharge, concentration of organic carbon, etc.</p> <p>The layer folder holds data of the initial conditions of the model (Table 2 in the article).</p> <p>The “meteo” folder holds the meteorological information.</p> <p>The “o2_obs” folder holds the observed oxygen data needed to do data assimilation. </p> <p>The “param_bio” folder holds information on the physiology of bacteria, phytoplankton, and other model species.</p> <p>The “Reaches” folder holds information about river reaches and their manning coefficient. </p> <p>The “param_range” file holds the variation range of model parameters considered in data assimilation together with their perturbation percentage.</p> <p>The output files are written in $HOME/Outputs folder. It is possible to change it directly in simulation.COMM, last entry “Output_folder”.</p>
Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS"
<p>Training Data for "Training Data for "Creating Quality FAIR assessment reports and draft of Data Papers from EML metadata with MetaShRIMPS""</p>
Assessment of the quality of water for human consumption in the Laguna Verde area of Valparaiso in Central Chile (DATA)
<p>Data set paper Assessment of the quality of water for human consumption in the Laguna Verde area of Valparaiso in Central Chile .</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.