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.
7,195
datasets available to search
ShareScore release 0.9.0
Dataset results
7,195 results for “SET”
August von Gneisenau - Set
experimenting with the upload size limit and partial manual reconstruction. what do you think? Source: Objaverse 1.0 / Sketchfab
Aurelius (Meshroom default settings)
Portrait bust of Marcus Aurelius, Art Institute, Chicago, Roman c. A.D. 170-80 Marble [24 photos](http://www.flickr.com/photos/ryanfb/sets/72157639515175486/), Canon EOS 70D, processed with [Meshroom](https://alicevision.github.io/) default settings See also: * [PhotoScan reconstruction](https://sketchfab.com/models/cad3a072546a457fa1e2e5a90b25063a) * [ReCap 360 reconstruction](https://sketchfab.com/models/9f7cb9cd988444f695d8512f73926ad4) Source: Objaverse 1.0 / Sketchfab
Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features - Validation Data Sets
<p>Two public validation cohorts were utilized in the MT-Pilot study, the Cancer Genome Atlas (TCGA) and cohort-1 Johns Hopkins University (JHU). These datasets included DNA, RNA, clinical data, and tissue protein analytes analyzed for survival outcome prediction using AI/Machine Learning modeling. </p>
Data sets for phylogenomic analyses in: Ant backbone phylogeny resolved by modelling compositional heterogeneity among sites in genomic data
<p>Ants are the most ubiquitous and ecologically dominant arthropods on Earth, and understanding their phylogeny is crucial for deciphering their character evolution, species diversification, and biogeography. Although recent genomic data have shown promise in clarifying intrafamilial relationships across the tree of ants, inconsistencies between molecular datasets have also emerged. Here I re-examine the most comprehensive published Sanger-sequencing and genome-scale datasets of ants using model comparison methods that model among-site compositional heterogeneity to understand the sources of conflict in phylogenetic studies. My results under the best-fitting model, selected on the basis of Bayesian cross-validation and posterior predictive model checking, identify contentious nodes in ant phylogeny whose resolution is <a>modelling-dependent. </a>I show that the Bayesian infinite mixture CAT model outperforms empirical finite mixture models (C20, C40 and C60) and that, under the best-fitting CAT-GTR+G4 model, the enigmatic <a><em>Martialis</em> </a><em>heureka</em> is sister to all ants except Leptanillinae, rejecting the more popular hypothesis supported under worse-fitting models, that place it as sister to Leptanillinae. These analyses resolve a lasting controversy in ant phylogeny and highlight the significance of model comparison and adequate modelling of among-site compositional heterogeneity in reconstructing the deep phylogeny of insects.</p>
ReaxFF Alumina Parametrization Data Set
<p>See README.md for an overview of the dataset and history of changes.</p>
Negative Complement of a Set of Vulnerability-Fixing Commits: Supplementary Material
<div> <div><span>EASE 2024 - Industry track</span></div> <br> <div><span>This archive contains accompanying materials for the paper: </span></div> <br> <div><span>Rocío Cabrera Lozoya, Antonino Sabetta, Tommaso Aiello. "Negative Complement of a Set of Vulnerability-Fixing Commits: Method and Dataset" </span></div> <br> <div><span>submitted to the Industry track at EASE 2024 - https://conf.researchr.org/track/ease-2024/ease-2024-industry</span></div> <br> <div><span>It contains the following folders and files:</span></div> <br> <div><span>-</span><span> data</span></div> <div><span> </span><span>-</span><span> commit_pairs_final.csv : Dataset of 534 commit pairs corresponding to a positiive (security-relevant) commit and a negative sample obtained by the approach described in the paper.</span></div> <div><span> </span><span>-</span><span> single_positive.csv : Contains a single positive instance (taken from the MSR2019 dataset) which can be used to test the generate_negative_complement.py script.</span></div> <div><span>-</span><span> scripts</span></div> <div><span> </span><span>-</span><span> generate_negative_complement.py : Takes in a single .java file and obfuscates its developer-defined identifiers.</span></div> <div><span> </span><span>-</span><span> obfuscate_java_file.py : Generates a negative complement for a security-relevant dataset. The ouput is written to couple_dataset.csv.</span></div> <div><span> </span><span>-</span><span> requirements.txt : Requirements needed in the virtual environment to successfully run the previous scripts.</span></div> </div>
Data sets for Polyplax serrata article "Highly-resolved genomes of two closely related lineages of the rodent louse Polyplax serrata with different host specificities"
<p><strong>Supplementary data for Polyplax serrata article 2023</strong></p> <p>Data included in this repository were generated and used in various genomic and phylogenetic analysis presented by the publication "<strong>Highly-resolved genomes of two closely related lineages of the louse </strong><em><strong>Polyplax serrata</strong></em><strong> with different host specificities</strong>"</p> <p><strong>Description of the data and file structure</strong></p> <p>Data provided for each analyzed taxa include:</p> <p>- Annotation table.</p> <p>- fasta format files for transcripts (CDS and mRNA).</p> <p>- fasta format file for genome.</p> <p>- protein fasta file.</p> <p>- gbk format file that includes the genome with its corresponding annotations.</p> <p>Additionally,</p> <p>- repeat families in fasta format were included for <em>Polyplax serrata</em> S and N lineages.</p> <p>- rRNA in fasta format were included for <em>Polyplax serrata</em> S and N lineages, <em>Pediculus humanus, Columbicola columbae </em>and <em>Brueelia nebulsa</em>. </p> <p><strong>Sharing/Access information</strong></p> <p>GenBank accession number of analyzed taxa:</p> <p>· <em>Aedes Aegypti</em> (GenBank accession no. GCF_002204515.2).</p> <p>· <em>Brueelia nebulsa</em> ( GenBank accession no. GCA_028293925.1).</p> <p>· <em>Columbicola columbae</em> (GenBank accession no. GCA_016920875.1).</p> <p>· <em>Cimex lectularis</em> (GenBank accession no. GCF_000648675.2).</p> <p>· <em>Glossina morsitans</em> (GenBank accession no. GCA_001077435.1).</p> <p>· <em>Pediculus humanus</em> (GenBank accession no. GCA_000006295.1).</p> <p>· <em>Rhodnius prolixus</em> (GenBank accession no. GCA_000181055.3).</p> <p>· <em>Polyplax serrata S lineage</em> (GenBank accession no. JAWJWF000000000).</p> <p>· <em>Polyplax serrata N lineage</em> (GenBank accession no. JAWJWE000000000).</p> <p> </p> <p>Note: All the latter genomes except for the two genomes of <em>Polyplax serrata</em> S and N lineages, were acquired from GenBank database and were subjected to the same gene prediction and annotation workflow as <em>P. serrata</em> genomes to maintain methodological consistence in downstream analysis of the annotation results.</p> <p><strong>Software</strong></p> <p>- Gene prediction and annotation was performed using Funannotate v1.18.14 (<a href="https://github.com/nextgenusfs/funannotate">https://github.com/nextgenusfs/funannotate)</a>).</p> <p>- Repeat were identified in the genomes of P. serrata S and N lineages using RepeatModeler v2.0.3.</p>
A target enrichment probe set for resolving phylogenetic relationships in the coffee family, Rubiaceae
<p><em>Rubiaceae </em>is among the most species-rich, morphologically and geographically diverse plant families. Phylogenies have been inferred for many different groups across the family, however these have mostly relied on few genomic and plastid loci, as opposed to large-scale genomic data. Target enrichment provides the ability to generate sequence data for hundreds to thousands of phylogenetically informative, single-copy loci, which often leads to improved phylogenetic resolution at both shallow and deep taxonomic scales; however, a publicly accessible <em>Rubiaceae</em>-specific probe set that allows for comparable phylogenetic inference across clades is lacking. Here, we use publicly accessible genomic resources to identify putatively single copy nuclear loci for target enrichment in two <em>Rubiaceae </em>tribes: Hillieae (<em>Cinchonoideae</em>) and Palicoureeae+Psychotrieae (<em>Rubioideae</em>). We sequenced 2270 exons corresponding to 1059 supercontigs in our target clades, and generated in silico target enrichment sequences for other <em>Rubiaceae </em>taxa using our designed probe set. Our probe set, which we call <em>Rubiaceae </em>2270, was effective for targeting loci in species across and even outside of <em>Rubiaceae</em>. This probe set will facilitate phylogenomic studies in <em>Rubiaceae </em>and advance systematics and macroevolutionary studies in the family.</p>
A data set on_Electrical and colloidal properties of hydrogenated nanodiamonds: effects of structure, composition and size
<p>The data set to paper: </p> <p>Electrical and colloidal properties of hydrogenated nanodiamonds: effects of structure, composition and size</p> <p>Stepan Stehlik1,2*, Ondrej Szabo2, Ekaterina Shagieva2, Daria Miliaieva2, Alexander Kromka2, Zuzana Nemeckova3, Jiri Henych3, 4, Jan Kozempel5, Evgeny Ekimov6, Bohuslav Rezek7</p> <p>1 New Technologies – Research Centre, University of West Bohemia, Univerzitní 8, 306 14, Pilsen, Czechia<br>2 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10, 162 00 Prague 6, Czechia<br>3 Institute of Inorganic Chemistry of the Czech Academy of Sciences, 250 68 Husinec-Řež, Czechia<br>4 Faculty of Environment, Jan Evangelista Purkyně University in Ústí nad Labem, Pasteurova 3632/15, 400 96 Ústí nad Labem, Czechia<br>5 Department of Nuclear Chemistry, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, Prague 1, 115 19, Czechia<br>6 Vereshchagin Institute for High Pressure Physics, Russian Academy of Sciences, Moscow 108840, Russia<br>7 Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czechia</p> <p>*principal investigator: stehlik@fzu.cz</p> <p>Data manager: Jan Jaroš: Jan.Jaros2@vut.cz</p> <p>Date of data collection: 1. 6. 2020 - 18. 1. 2024</p> <p>All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective Figure to which the data belong is provided in high resolution. <br>The data are in the following formats: <br>Figure 1: tiff, csv<br>Figure 2: tiff, csv<br>Figure 3: tiff, csv<br>Figure 4: tiff, csv<br>Figure 5: tiff, csv<br>Figure 6: tiff</p> <p>Data acquistion methods and conditions and data processing is provided in the Experimental part in the publication: DOI:10.1016/j.cartre.2024.100327</p> <p> </p> <p><span> </span></p>
Data set related to the manuscript "Dynamics and energetics of ion adsorption at the interface between a pure ionic liquid and carbon electrodes"
<p>Text files with data for plotting figures in the quoted manuscript. Example input files for simulations.</p>
A Stimuli Set of Forty Popular Music Drum Patterns with Perceived Complexity Estimates (Data set)
<p>Stimuli and datasets for the study "A Stimuli Set of Forty Popular Music Drum Patterns with Perceived Complexity Estimates"</p>
Curated catalogue for IRFundusSet (Integrated Retinal Fundus Set)
<p>Integrated Retinal Fundus Set (IRFundusSet) is a dataset that consolidates and curates several public datasets to ease their use as a harmonized unit and to <br>have a common definition of what is normal or healthy. </p> <p><strong>Motivation:</strong> Existing retinal fundus public datasets do not follow a standard format or structure for organizing and archiving the datasets. Moreover, eyes/images marked as `not pathological` might represent healthy eyes or eyes with another pathology that is not relevant to the task or disease the dataset was created for. </p> <p>This dataset entails </p> <ul> <li>A Python module that that consolidates and harmonizes the public datasets and that can be used in a data pipeline following the PyTorch style for Dataset objects. </li> <li>A curated catalogue in CSV format where images have been manually reviewed to determine if the image is of a healthy or pathological eye; a common definition for what is a healthy observation </li> </ul> <p> </p> <p> </p>
JCURA 2024 PyLC Image Testing Set and Landcover Masks
<p>This is the image and landcover mask testing set created for the 2024 JCURA project "Mountains of Confusion: Evaluating Image Enhancement to Improve AI Landscape Classification" by Larissa Bron. </p> <p>The Python Landscape Classification tool (PyLC) [https://github.com/scrose/pylc] was trained using 95 images from two researcher's work, [Fortin (2018)](https://dspace.library.uvic.ca/items/0a911eb0-53bf-4a82-a75a-8b6949c28edd) and [Jean et al. (2015)](https://ieeexplore.ieee.org/document/7045940), and tested with 19 images that were a combination of 11 images from Fortin and Jean withheld from training and 8 images from the Landscapes in Motion project [(Higgs et al., (2020))](https://friresearch.ca/publications/advances-visual-applications-visualizing-quantifying-landscape-change-sw-alberta-using). This core set of training and testing images was used to train the colour models of PyLC with repeat images, then these images were grayscaled and added to training the grayscale models of PyLC with historic images. </p> <p>For this JCURA project, the testing image set was updated to: 1) Incorporate images from more geographic areas, and 2) Remove images with reference land cover masks with large errors. </p> <p>The files included are 24 test images (.jpg, .tiff, .tif) and 24 manually annotated land cover masks (.png). File names indicate: Researcher_ImageIdentifier_Time where time is whether the image is a historic capture or a repeat. </p>
bee test set of MOT
<p>Using Darklabel to annotate video data in MOT16 format, it is necessary to combine the target tracking model to output a txt file for MOT metric verification.</p>
Distance-dependence seed set of Vasconcellea chilensis
<p><span>Plant reproductive failure is a critical concern for conserving rare and endangered species that typically have low-density and sparse populations. One important factor contributing to reproductive failure is the spatial arrangement of plants within a population, which can lead to isolation and negatively affect seed output, particularly in obligate outcrosses. Additionally, this effect can be compounded by plant size. Here, we investigate how plants' spatial distribution and size influence the reproductive success of <em>Vasconcellea chilensis</em>, a threatened papaya species from northern Chile. </span><span>Using flower exclusion experiments, we first examined whether <em>V. chilensis</em> can produce seeds via apomixis. We then used Spatial Point Pattern Analysis (SPPA) in three populations to examine the spatial arrangement of plants in three populations, and, finally, we assessed whether plant size and mate distance influence the reproductive success of this plant species. </span><span><em>V. chilensis</em> is a dioecious shrub unable to produce fruits through apomixis. The SPPA revealed significant clustering of female and male plants at different spatial scales, indicating a non-random distribution. Moreover, a significant attraction between the sexes suggested a preference for proximity. In two populations, closer proximity to male plants was linked to higher seed production. </span><span>Our study revealed that the absence of apomixis in <em>V. chilensis</em> makes it prone to experiencing distance-dependent reproductive failure. In particular, the seed set was compromised in female plants isolated from male neighbors. This link between isolation and seed production was especially significant in the driest site, and we discussed how environmental factors can exacerbate this effect.</span></p>
Example data set for Replay-triggered Brain-wide Activation in Humans
<p>Example data set for <em>Replay-triggered Brain-wide Activation in Humans </em></p> <p><em>Unzip the file and place it in the directory named "example_data_code".</em></p>
Eye tracking data set of academics making an omelette: An egg-breaking work
<p>Just as there are numerous ways to cook an egg, there are numerous ways to recreate a YouTube video of cooking an omelette. We created a dataset of 10 academics replicating a viral video of making an omelette. We evaluated the saccade behavior during the varying subtasks and found differences related to the actions (whisking, sprinkling, etc.) and the objects (eggs, butter, plate, etc.). This dataset can further offer insight into eye movements in complex tasks and is potentially even applicable for task planning and </p>
Set of instances used in article "Medical staff planning for field hospital deployments: the START hospital"
<p>The dataset presented is used in the article "Medical staff planning for field hospital deployments: the START hospital" by F. Javier Martín-Campo, M. Teresa Ortuño and Berta Ruiz-González, submitted for publication (2024).</p> <p>This paper proposes a mathematical optimisation model to deal with the management of emergency staff to a field hospital from a roster of volunteers with different characteristics.</p> <p>A set of Excel files is available being:</p> <ul> <li>Readme: It contains the general information of the files.</li> <li>Data: It contains the data (general parameters, demand, prices, charter flights, health profiles, availability and grades).</li> <li>8 files with the solution for: Infeasibility test, Cost, Availability, Grades, Monoobjective, Goal Programming 1, Goal Programming 2 and Compromise Programming.</li> </ul>
DCASE 2024 Task 9: Language-Queried Audio Source Separation | Validation Set
<p>This is the <strong>validation set for Task 9, Language-Queried Audio Source Separation (LASS), in DCASE 2024 Challenge</strong>. </p> <p>This validation split is meant to be used for Task 9 at the scientific challenge DCASE 2024. This split is not meant to be used for training LASS methods. This split is meant to be used for evaluating LASS methods during the model development stage.</p> <p>This validation set consists of 1000 audio files sourced from Freesound [1], uploaded between April and October 2023. Each audio file has been manually annotated with three captions. In the annotation guidance, we instructed annotators to describe the content of audio clips using 5-20 words (similar to the caption style in Clotho [3] and AudioCaps [4] datasets). The tags of each audio file were verified and revised according to the FSD50K [2] sound event categories. Each audio file has been chunked into a 10-second clip and downsampled to 16kHz.</p> <p><strong>== Details ==</strong></p> <p>The audio files in the archives:</p> <ul> <li>lass_validation.zip</li> </ul> <p>and the associated metadata (including tags and captions) in the JSON file:</p> <ul> <li>lass_validation.json</li> </ul> <p>Participants will evaluate their LASS models using synthetic mixture data in the development stage. Specifically, given an audio clip A1 and its corresponding caption C, we select an additional audio clip, A2, to serve as background noise, thereby creating a mixed audio, A3. We anticipate that the LASS system, given A3 and C as inputs, will be able to separate the A1 source. We use the revised tags information to ensure that the two audio clips used in each mix do not share overlapping sound source classes. Three thousand synthetic audio mixtures with signal-to-noise ratios (SNR) ranging from -15dB to 15dB will be generated for the validation of LASS model development. These synthetic mixtures can be generated based on the provided CSV file:</p> <ul> <li>lass_synthetic_validation.csv</li> </ul> <p>The evaluation tool can be found at: https://github.com/Audio-AGI/dcase2024_task9_baseline/blob/main/dcase_evaluator.py</p> <p><strong>== References ==</strong></p> <p>[1] Fonseca E, Pons Puig J, Favory X, et al. Freesound datasets: a platform for the creation of open audio datasets. International Society for Music Information Retrieval (ISMIR), 2017.</p> <p>[2] Fonseca E, Favory X, Pons J, et al. FSD50k: an open dataset of human-labeled sound events. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 30: 829-852.</p> <p>[3] Drossos K, Lipping S, Virtanen T. Clotho: An audio captioning dataset. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2020: 736-740.</p> <p>[4] Kim C D, Kim B, Lee H, et al. AudioCaps: Generating captions for audios in the wild. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2019: 119-132.</p>
DCASE 2024 Task 9: Language-Queried Audio Source Separation | Development Set
<p><strong>== Description == </strong></p> <p>The development set is composed of audio samples from FSD50K [1] and Clotho v2 [2] datasets. FSD50K contains over 51k audio clips (~100 hours) manually labeled using 200 classes drawn from the AudioSet Ontology. For each audio clip in the FSD50K dataset, we generated one automatic caption for each audio clip by prompting ChatGPT (GPT-4) with its sound event tags. All audio files should be converted to mono 16 kHz audio for training LASS models. </p> <p>Clotho v2: <a href="../records/4783391">https://zenodo.org/records/4783391</a></p> <p>FSD50K: <a href="../records/4060432">https://zenodo.org/records/4060432</a></p> <p>Automatic captions generated for FSD50K:</p> <ul> <li>fsd50k_dev_auto_caption.json</li> <li>fsd50k_eval_auto_caption.json</li> </ul> <p>Prompt for generating captions:</p> <blockquote> <p>I will give you a number of lists containing sound events. Please write an one-sentence audio caption to describe these sounds.</p> <p>Make sure you are using grammatical subject-verb-object sentences. Directly describe the sounds and avoid using the word “heard”. Please don't describe the temporal order of these sound events. The caption should be less than 20 words.</p> </blockquote> <p>In addition to the development set, participants are free to use any external data (including private data) but are not allowed to use audio in Freesound uploaded between April and October 2023. Participants must specify all external resources utilized in their submission in the technical report.</p> <p><strong>== References ==</strong></p> <p>[1] Fonseca E, Favory X, Pons J, et al. FSD50k: an open dataset of human-labeled sound events. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 30: 829-852.</p> <p>[2] Drossos K, Lipping S, Virtanen T. Clotho: An audio captioning dataset. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2020: 736-740.</p> <p><strong>== Contact ==</strong></p> <p>Xubo Liu, xubo.liu@surrey.ac.uk</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.