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.
8,038
datasets available to search
ShareScore release 0.9.0
Dataset results
8,038 results for “validation.”
Evaluating the potential of Digital Twin technology on pharmaceutical manufacturing efficiency in Ireland: An in-depth analysis of Machinery Validation
<p>The dissertation titled <strong>"Evaluating the Potential of Digital Twin Technology on Pharmaceutical Manufacturing Efficiency in Ireland: An In-depth Analysis of Machinery Validation"</strong> explores the transformative impact of Digital Twin (DT) technology within Ireland's pharmaceutical manufacturing sector. Conducted by MSc candidate Gayathri Gopakumar at Griffith College Dublin, the research focuses on how DT technology can enhance machinery validation processes, thereby improving operational efficiency and ensuring regulatory compliance.</p> <p>Employing a qualitative research methodology, the study includes semi-structured interviews with industry experts to gather insights into the current awareness, perceived benefits, and challenges associated with DT adoption in the pharmaceutical industry. The research aims to provide a comprehensive understanding of DT technology's role in machinery validation and its broader implications for manufacturing efficiency.</p> <p>This work contributes to the existing body of knowledge by offering a detailed analysis of DT technology's potential applications in pharmaceutical manufacturing, with a specific focus on the Irish context. It serves as a valuable resource for professionals and researchers interested in the integration of advanced digital technologies in the pharmaceutical sector.</p>
GWAS summary statistics for 9 quantitative phenotypes from the UK Biobank (5-fold cross-validation)
<p>This dataset contains GWAS summary statistics for 9 quantitative phenotypes from the UK Biobank.</p> <p>The dataset is designed to enable systematic PRS analyses with 5-fold cross validation. For each phenotype and fold, we provide GWAS summary statistics for the training, validation, and test sets. The validation summary statistics can be used for model selection/tuning. The test summary statistics can be used to evaluate PRS models via pseudo-validation metrics. Association testing for all phenotypes and samples was done with <strong>plink2</strong>.</p> <p> </p> <p>The <strong>phenotypes</strong> included in this dataset are:</p> <ul> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=50">HEIGHT</a>: Standing height (Data-Field: 50)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=21001">BMI</a>: Body mass index (Data-Field: 21001)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=48">WC</a>: Waist circumference (Data-Field: 48)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=49">HC</a>: Hip circumference (Data-Field: 49)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=20022">BW</a>: Birth weight (Data-Field: 20022)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=3062">FVC</a>: Forced vital capacity (Data-Field: 3062)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=3063">FEV1</a>: Forced expiratory volume in 1-second (Data-Field: 3063)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=30760">HDL</a>: HDL cholesterol (Data-Field: 30760)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=30780">LDL</a>: LDL cholesterol (Data-Field: 30780)</li> </ul> <p> </p> <p>To allow users to assess PRS performance as a function of sample size, we also provide <strong>subsampled training GWAS summary statistics</strong>. This is done by taking the training samples and randomly selecting (without replacement) a subset of them for conducting association testing. The training sample sizes are:</p> <ul> <li>N = 5000</li> <li>N = 10000</li> <li>N = 20000</li> <li>N = 40000</li> <li>N = 80000</li> <li>N = 160000</li> <li>Full training set (sample size varies by phenotype).</li> </ul> <p><strong>NOTE</strong>: Due to the smaller overall sample size for the Birth weight phenotype, we do not include training data for the `N=160000` setting.<br><br></p> <p>The <strong>folder structure</strong> of the GWAS data for each phenotype is as follows:</p> <ul> <li><code>train</code> <ul> <li><code>N_5000</code> <ul> <li><code> fold_1</code> <ul> <li><code>chr_1.PHENO1.glm.linear</code></li> <li><code>chr_2.PHENO1.glm.linear</code></li> <li><code>...</code></li> </ul> </li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> <li><code>N_10000</code></li> <li><code>N_20000</code></li> <li><code>N_40000</code></li> <li><code>N_80000</code></li> <li><code>N_160000</code></li> <li><code>full</code></li> </ul> </li> <li><code>validation</code> <ul> <li><code>fold_1</code> <ul> <li><code>chr_1.PHENO1.glm.linear</code></li> <li><code>chr_2.PHENO1.glm.linear</code></li> <li><code>...</code></li> </ul> </li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> <li><code>test</code> <ul> <li><code>fold_1</code></li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> </ul> <p>For more details about the GWAS study, Quality Control (QC) criteria, or other information, please consult our publication:</p> <p>Zabad, S., Gravel, S., & Li, Y. (2023). <strong>Fast and accurate Bayesian polygenic risk modeling with variational inference.</strong> The American Journal of Human Genetics, 110(5), 741–761. <a href="https://doi.org/10.1016/j.ajhg.2023.03.009" rel="nofollow">https://doi.org/10.1016/j.ajhg.2023.03.009</a></p> <p>If you use this data in your work, please cite the publication above.</p> <p> </p>
Temporal validity of software datasets for code metrics: an empirical assessment of sampling strategies
<p>This is the repository for the scripts and data of the study "Building and updating software datasets: an empirical assessment".</p> <h2>Data collected</h2> <p>The data generated for the study it can be downloaded as a zip file. Each folder inside the file corresponds to one of the datasets of projects employed in the study (qualitas, currentSample and qualitasUpdated). Every dataset comprised three files "class.csv", "method.csv" and "sample.csv", with class metrics, method metrics and repository metadata of the projects respectively. Here is a description of the datasets:</p> <ul> <li>qualitas: includes code metrics and repository metrics from the projects in the release 20130901r of the Qualitas Corpus.</li> <li>currentSample: includes code metrics and repository metrics from a recent sample collected with our sampling procedure.</li> <li>qualitasUpdated: includes code metrics and repository metrics from an updated version of the Qualitas Corpus applying our maintenance procedure.</li> </ul> <h2>Plot graphics</h2> <p>To plot the results and graphics in the article there is a Jupyter Notebook "Experiment.ipynb". It is initially configured to use the data in "datasets" folder.</p> <h2>Replication Kit</h2> <p>For replication purposes, the datasets containing recent projects from Github can be re-generated. To do so, the virtual environment must have installed the dependencies in "requirements.txt" file, add Github's tokens in "./token" file, re-define or leave as is the paths declared in the constants (variables written in caps) in the main method, and finally run "main.py" script. The portable versions of the source code scanner <a href="https://sourcemeter.com/" target="_blank" rel="noopener">Sourcemeter</a> are located as zip files in "./Sourcemeter/tool" directory. To install Sourcemeter the appropriate zip file must be decompressed excluding the root folder "SourceMeter-10.2.0-x64-<OS>".</p> <p>The script comprise 5 steps:</p> <ol> <li>Project retrieval from Github: at first the sampling frame with projects complying with a specific quality criteria are retrieved from Github's API.</li> <li>Create samples: with the sampling frame retrieved, the current samples are selected (currentSample and qualitasUpdated). In the case of qualitasUpdated, it is important to have first the "sample.csv" file inside the qualitas folder of the dataset originally created for the study. This file contains the metadata of the projects in Qualitas Corpus.</li> <li>Project download and analysis: when all the samples are selected from the sampling frame (currentSample and qualitasUpdated), the repositories are downloaded and scanned with SourceMeter. In the cases in which the analysis is not possible, the projects are replaced with another one with similar size.</li> <li>Outlier detection: once the datasets are collected, it is necessary to manually look for possible outliers in the code metrics under study. In the notebook "Experiment.ipynb" there are specific sections dedicated for it ("Outlier detection (Section 4.2.2)").</li> <li>Outlier replacement: when the outliers are detected, in the same notebook there is also a section for outlier replacement ("Replace Outliers") where the outliers' url have to be listed to find the appropriate replacement.</li> </ol> <ul> <li>If it is required, the metrics from the Qualitas Corpus can also be re-generated. First, it is necessary to download the release 20130901r from its <a href="http://www.qualitascorpus.com/download/" target="_blank" rel="noopener">official webpage</a>. Second, decompress the .tar files downloaded. Third, make sure that the compressed files with source code from the projects (.java files) are placed in the "compressed" folder, in some cases it is necessary to read the "QC_README" file in the project's folder. Finally, run the original main script "Generate metrics for the Qualitas Corpus (QC) dataset" part of the code. </li> </ul>
Train and external validation - HABiC
Open the record for dataset details and reuse information.
AlphaTrans: A Neuro-Symbolic Compositional Approach for Repository-Level Code Translation and Validation
Open the record for dataset details and reuse information.
Genotyping validates the efficacy of photographic identification in a capture-mark-recapture study based on the head scale patterns of the prairie lizard (Sceloporus consobrinus)
Population studies often incorporate capture-mark-recapture (CMR) techniques to gather information on long-term biological and demographic characteristics. A fundamental requirement for CMR studies is that an individual must be uniquely and permanently marked to ensure reliable reidentification throughout its lifespan. Photographic identification involving automated photographic identification software has become a popular and efficient non-invasive method for identifying individuals based on natural markings. However, few studies have a) robustly assessed the performance of automated programs by using a double-marking system or b) determined their efficacy for long-term studies by incorporating multi-year data. Here, we evaluated the performance of the program Interactive Individual Identification System (I<sup>3</sup>S) by cross-validating photographic identifications based on the head scale pattern of the prairie lizard (<i>Sceloporus consobrinus</i>) with individual microsatellite genotyping (N=863). Further, we assessed the efficacy of the program to identify individuals over time by comparing error rates between within-year and between-year recaptures. Recaptured lizards were correctly identified by I<sup>3</sup>S in 94.1% of cases. We estimated a false rejection rate (FRR) of 5.9% and a false acceptance rate (FAR) of 0%. By using I<sup>3</sup>S we correctly identified 97.8% of within-year recaptures (FRR=2.2%; FAR=0%) and 91.1% of between-year recaptures (FRR=8.9%; FAR=0%). Misidentifications were primarily due to poor photo quality (N=4). However, two misidentifications were caused by indistinct scale configuration due to scale damage (N=1) and ontogenetic changes in head scalation between capture events (N=1). We conclude that automated photographic identification based on head scale patterns is a reliable and accurate method for identifying individuals over time. Because many lizard or reptilian species possess variable head squamation, this method has potential for successful application in many species.
Validation of temperature-controlled rheo-MRI measurements in a sub-mm gap Couette cell
<p>Validation of temperature-controlled rheo-MRI measurements in a sub-mm gap Couette cell</p>
FIGURE 5 in Reinstatement of Athis miastagma (Dyar, 1925) (Lepidoptera: Castniidae) as a valid species inhabiting the Pacific slope of Central Mexico
FIGURE 5. Terrestrial habitats and hosts plants of Athis hechtiae and A. miastagma (Castniidae). A) Xeric shrubland of the Tehuacán Valley Matorral eco-region, Tehuacán-Cuicatlán Valley, Puebla, typical habitat of A. hechtiae; B) Hechtia tehuacana (Bromeliaceae), host of A. hechtiae; C) Hechtia santanae, host of A. miastagma; D) Tropical deciduous forests, with a multilayered structure, Tuxcacuesco, Jalisco, typical habitat of A. miastagma.
FIGURE 4 in Reinstatement of Athis miastagma (Dyar, 1925) (Lepidoptera: Castniidae) as a valid species inhabiting the Pacific slope of Central Mexico
FIGURE 4. Maximum Likelihood phylogeny of some Castniidae species based on COI barcoding sequences. Numbers at each node represent support values according to 1000 ultrafast bootstrap replicates as implemented in IQ-TREE. Note: Photographs have been size-adjusted only for illustrations purposes. List of taxa with accession numbers is given on Table 1.
FIGURE 3 in Reinstatement of Athis miastagma (Dyar, 1925) (Lepidoptera: Castniidae) as a valid species inhabiting the Pacific slope of Central Mexico
FIGURE 3. Average genetic distances (y-axes) of intraspecific A. hechtiae, genetic distances of A. miastagma to A. hechtiae, and intraspecific A. miastagma.
FIGURE 2 in Reinstatement of Athis miastagma (Dyar, 1925) (Lepidoptera: Castniidae) as a valid species inhabiting the Pacific slope of Central Mexico
FIGURE 2. Male genitalia of Athis hechtiae (left) from the Tehuacán-Cuicatlán Valley (C, drawing by Simeão Moraes) and A. miastagma (right) from D, Jalisco; E, F, Guerrero (The latter based on the holotype genitalia). Each set shows the genital armature (top) and aedeagus (bottom).
FIGURE 6 in Reinstatement of Athis miastagma (Dyar, 1925) (Lepidoptera: Castniidae) as a valid species inhabiting the Pacific slope of Central Mexico
FIGURE 6. Maps of a section of Mexico showing known collecting localities for Athis hechtiae (blue circles) (Tehuacán-Cuicatlán Valley) and Athis miastagma (blue squares) (mainly in the Balsas Basin, flowing through the Sierra Madre del Sur and into the Pacific, limited in the northwest by the western mountainous section of the Trans-Mexican Volcanic Belt; to the East it does not enter the Tehuacán Valley, and to the Southeast does not cross the Mixtec region). The red square indicates only the presence of A. miastagma in Michoacán. It is located in the center of the state because the supposed collecting locality (Putja) could not be identified. The red circles are doubtful localities of occurrence of A. hechtiae in Presidio, Veracruz, and Totolapán, Oaxaca. Top figure is an ecoregion map based on Dinerstein et al. (2017); the bottom one is a hypsographic map. (Maps: Bolívar Garcete)
FIGURE 1 in Reinstatement of Athis miastagma (Dyar, 1925) (Lepidoptera: Castniidae) as a valid species inhabiting the Pacific slope of Central Mexico
FIGURE 1. Males (top row) and females (bottom row) of Athis hechtiae (left) and A. miastagma (right), from Mexico.
ARCH-COMP 2021: Validation Data from the Falsification Category
<p>This artifact documents the results from the falsification category of the ARCH 2021 friendly competition.</p>
Validation of the predictive accuracy of health-state utility values based on the Lloyd model for metastatic or recurrent breast cancer in Japan
<p>Although there is a lack of data on health-state utility values (HSUVs) for calculating quality-adjusted life years in Japan, Cost-utility analysis has been introduced by the Japanese government to inform decision-making in the medical field since 2016. This study aimed to determine whether the Lloyd model which was a predictive model of HSUVs for metastatic breast cancer (MBC) patients in the United Kingdom can accurately predict actual HSUVs for Japanese patients with MBC. The prospective observational study, followed by the validation study of the clinical predictive model.<b> </b>Forty-four Japanese patients with MBC were studied at 336 survey points. This study consisted of two phases. In the first phase, we constructed a database of clinical data prospectively and HSUVs for Japanese patients with MBC to evaluate the predictive accuracy of HSUVs calculated using the Lloyd model. In the second phase, Bland-Altman analysis was used to determine how accurately predicted HSUVs (based on the Lloyd model) correlated with actual HSUVs obtained using the EuroQol 5-Dimension 5-Level questionnaire, a preference-based measure of HSUVs in patients with MBC. In the Bland-Altman analysis, the mean difference between HSUVs estimated by the Lloyd model and actual HSUVs, or systematic error, was -0.106. The precision was 0.165. The 95% limits of agreement ranged from -0.436 to 0.225. The t value was 4.6972, which was greater than the t value with 2 degrees of freedom at the 5% significance level (p=0.425). There were acceptable degrees of fixed and proportional errors associated with the prediction of HSUVs based on the Lloyd model for Japanese patients with MBC. We recommend that sensitivity analysis be performed when conducting cost-effectiveness analyses with HSUVs calculated using the Lloyd model.</p>
FIGURE 5 in An assessment of the taxonomic validity of three species of marsupial frogs (Anura: Hemiphractidae: Gastrotheca) from the Yungas of Bolivia based on external morphology and cranial osteology
FIGURE 5. CT-scan images of the skull of a specimen identified as Gastrotheca splendens (CBG 1034) in dorsal (A), ventral (B), lateral (C), and frontal view (D) with the principal bones indicated. Abbreviations: alary p = alary process; angspl = angulosplenial; cr par = crista parotica; exoc = exoccipital; fpar = frontoparietal; max = maxilla; mmk = mentomeckelian bone; neopal = neopalatine; premax = premaxilla; pro = prootic; prsph = parasphenoid; pter = pterygoid; quad = quadratojugal; septo = septomaxilla; spheth = sphenethmoid; squa = squamosal. Images not at the same scale.
FIGURE 7 in An assessment of the taxonomic validity of three species of marsupial frogs (Anura: Hemiphractidae: Gastrotheca) from the Yungas of Bolivia based on external morphology and cranial osteology
FIGURE 7. Female alive specimen belonging to the Gastrotheca populations from the cloud forests (Yungas) of department La Paz (Bolivia) for which the name G. coerulemaculatus (Werner, 1899) is available. A) Dorsolateral view; B) ventral view; note the blue, green, and yellow ventral pattern, and the orange lower surface of thighs (Photos: Mauricio Pacheco).
FIGURE 3 in An assessment of the taxonomic validity of three species of marsupial frogs (Anura: Hemiphractidae: Gastrotheca) from the Yungas of Bolivia based on external morphology and cranial osteology
FIGURE 3. Cranial co-ossification with different degrees of development (increasing from A to D) in preserved individuals preliminary identified as Gastrotheca splendens: (A) female, CBG 1035; (B) female, CBG 1034; (C) female, CBG 1033; (D) female, CBG 1032.
FIGURE 1 in An assessment of the taxonomic validity of three species of marsupial frogs (Anura: Hemiphractidae: Gastrotheca) from the Yungas of Bolivia based on external morphology and cranial osteology
FIGURE 1. Three nominal species of Gastrotheca from the Yungas of central Bolivia: (A) G. lauzuricae: female holotype, EBD 37379H, 34.3 mm SVL, from La Siberia, 2800 m, province Carrasco, Cochabamba, Bolivia; (B) G. piperata: female EBD 37243H, 41.9 mm SVL, from Yunga de Mairana, 2300 m, province Florida, Santa Cruz, Bolivia; (C) G. splendens: female MNK 1988, 44.7 mm SVL, from Abra de la Cruz, province Caballero, Santa Cruz, Bolivia. Photos by I. De la Riva. Note the variation on the dorsal coloration pattern (A, B) and the cranial co-ossification externally visible (C).
FIGURE 2 in An assessment of the taxonomic validity of three species of marsupial frogs (Anura: Hemiphractidae: Gastrotheca) from the Yungas of Bolivia based on external morphology and cranial osteology
FIGURE 2. Localities of Gastrotheca from the cloud forests (Yungas) of Bolivia. Red dot: type locality of G. lauzuricae; yellow dot: type locality of G. piperata; purple dot: only known locality for G. splendens; white star: type locality of G. coerulemaculatus; black star: type locality of Nototrema bolivianum; white dots: rest of specimens examined. See Appendix I for detailed locality data. Localities lying very close are represented by a single symbol.
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.