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”
data set related to article Suicidal ideation, suicidal attempts and non-suicidal self-injuries in referred adolescents
<p>This record contains raw data to article Suicidal ideation, suicidal attempts and non-suicidal self-injuries in referred adolescents</p>
data set related to article Suicidal ideation and suicidal attempts in referred adolescents with high functioning autism spectrum disorder and comorbid bipolar disorder: A pilot study
<p>This record contains raw data related to article Suicidal ideation and suicidal attempts in referred adolescents with high functioning autism spectrum disorder and comorbid bipolar disorder: A pilot study</p>
Data from: Soil dynamics in forest restoration: a data set for temperate and tropical regions
<p>Restoring forest ecosystems has become a global priority. Yet, soil dynamics is still poorly assessed among restoration studies and lacks knowledge on how soil is affected by forest restoration process. Here, we compile information on soil dynamics in forest restoration based on soil physical, chemical and biological attributes in temperate and tropical forest regions. It encompasses 50 scientific papers across 17 different countries and contains 1,469 quantitative information of soil attributes between reference (e.g., old-growth forest) and restored ecosystems (e.g. forests in their initial or secondary stage of succession) within the same study. To be selected, studies had to be conducted in forest ecosystems, to include multiple sampling sites (replicates) in both restored and reference ecosystems, and to encompass quantitative data of soil attributes for both reference and restored ecosystems.</p> <p>We recorded in each study the following information: (i) study year; (ii) country; (iii) forest region (tropical or temperate); (iv) latitude; (v) longitude; (vi) soil class; (vii) past disturbance; (viii) restoration strategy (active or passive); (ix) restoration age; (x) soil attribute type (physical, chemical or biological); (xi) soil attribute; (xii) soil attribute unit; (xiii) soil sampling (procedures); (xiv) date of sampling; (xv) soil depth sampled; (xvi) soil analysis; (xvii) quantitative values of soil attributes for both restored and reference ecosystems; (xviii) type of variation (standard error ou deviation) for both restored and reference ecosystems; and (xix) quantitative values of the variation for both restored and reference ecosystems. These were the most common data available in the selected studies.</p> <p>This extensive database on the extent soil physical, chemical and biological attributes differ between reference and restored ecosystems can fill part of the existing gap on both soil science and forest restoration in terms of: (i) which are the critical soil attributes to be monitored during forest restoration? and (ii) how do environmental factors affect soil attributes in forest restoration? The data will be made available to the scientific community for further analyses on both soil science and forest restoration. Soil information gap during the forest restoration process and its general patterns can be addressed using this data set.</p>
Figure 2 in Biogeography, land snails and incomplete data sets: the case of three island groups in the Aegean Sea
Figure 2. Map of the island group of Astypalaia, with islands and sites studied. 13: Diapori island.
Figure 4 in Biogeography, land snails and incomplete data sets: the case of three island groups in the Aegean Sea
Figure 4. UPGMA tree based on the similarity matrix obtained by applying Jaccard's index to the binary data matrix.
Figure 2 in Phylogenetic analysis and a time tree for a large drosophilid data set (Diptera: Drosophilidae)
Figure 2. Phylogenetic tree showing the reconstructed ancestral geographical distributions for extant and ancestral drosophilids estimated by the maximum-likelihood algorithm. Extant geographical distributions were retrieved from the Drosophila Stock Center or from the ZipcodeZoo database. See Table S2 for geographical distributions.
Figure 1 in Phylogenetic analysis and a time tree for a large drosophilid data set (Diptera: Drosophilidae)
Figure 1. Timescale for drosophilids based on a maximum-likelihood (ML) analysis using a concatenated alignment (9917 bp) of six protein-coding nuclear genes. Several monophyletic branches have been collapsed, indicating that all taxa within that taxonomic rank form a cluster. Support values above branches are bootstrap proportions performed on the ML tree; values less than 50 are not shown.
FIGURE 1 in First records of Asbestopluma hypogea Vacelet and Boury-Esnault, 1996 (Porifera, Demospongiae Cladorhizidae) on seamounts and in bathyal settings of the Mediterranean Sea
FIGURE 1. Mediterranean distribution of the carnivorous sponge Asbestopluma hypogea, with previously known shallow submarine cave occurrences ~20 m in Southern France (e.g. Vacelet and Boury-Esnault, 1996) and Croatia (Bakran-Petricioli et al., 2007), and with the new bathyal records (up to -707 m) in the western and central Mediterranean Sea (this study).
FIGURE 3 in First records of Asbestopluma hypogea Vacelet and Boury-Esnault, 1996 (Porifera, Demospongiae Cladorhizidae) on seamounts and in bathyal settings of the Mediterranean Sea
FIGURE 3. Spicules of Asbestopluma hypogea (SEM of MS-32). A) Style. B) Magnification of a style base showing the slight swelling. C–E) Anisochelae: C) profile view, mounted on front side, D) back view with characteristic basal hole, E) front view.
FIGURE 2. A phylogenetic network for a hypothetical data set. This network represents the relationships between four taxa, A-D in Exploring character conflict in molecular data*
FIGURE 2. A phylogenetic network for a hypothetical data set. This network represents the relationships between four taxa, A-D. The length of branch (a) is proportional to the strength of support for the relationship (A,C)(B,D). The length of branch (b) is proportional to the strength of support for the relationship (A,B)(C,D). In this example there is conflicting support for both of these arrangements, but more weight is given to (A,C)(B,D) than to (A,B)(C,D).
Fig. 2 in Dung Removal by Dichotomius (Luederwaldtinia) Sericeus (Harold, 1867) (Coleoptera: Scarabaeidae: Scarabaeinae) in a Laboratory Setting, with Behavioral Notes
Fig. 2. Records of gallery formation in the soil by Dichotomius sericeus in a laboratory setting. a) Mean number of galleries, b) Mean diameter of galleries. Dispersion measure is standard error. Different letters above error bars identify significant differences for pairwise comparisons. EU = experimental unit.
Fig. 1 in Dung Removal by Dichotomius (Luederwaldtinia) Sericeus (Harold, 1867) (Coleoptera: Scarabaeidae: Scarabaeinae) in a Laboratory Setting, with Behavioral Notes
Fig. 1. Mean bovine manure removal by Dichotomius sericeus under laboratory conditions. Dispersion measure is standard error. Different letters above the error bars identify significant differences for pairwise comparisons. EU = experimental unit.
ChemBioSim Recalibration: Twelve Preprocessed ChEMBL Data Sets
<p><strong>Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data</strong></p> <p><strong>Project description</strong></p> <p>Machine learning models are powerful tools for the prediction of molecular properties or the biological activity of chemical compounds. However, to make these models useful and applicable, the confidence in the predictions should also be specified. For that purpose, models may be integrated in a conformal prediction (CP) framework that adds a calibration step to estimate the confidence of the predictions. CP models offer the advantage of ensuring a predefined error rate, as long as the test and training sets are exchangeable.</p> <p>In cases where the test data presents a drift from the descriptor space of the training data, or where assay setups change, this assumption may not be fulfilled and the models are not guaranteed to be valid. </p> <p>In this study, the performance of internally valid CP models was evaluated upon application to either newer time-split data or to external data. More specifically, temporal data drifts were analysed based on time-splits of twelve toxicity-related datasets from the ChEMBL database. Moreover, models trained on publicly available data for liver toxicity and MNT in vivo were applied on proprietary data to evaluate the discrepancies. In general it was observed that the training and (holdout) test sets were not exchangeable in the studied set-ups, and the models were therefore not applicable (i.e. non-valid CP models).</p> <p>To recover the validity of the models on the holdout test set, a strategy for updating the calibration set with data more similar to the holdout set was investigated. Restored validity is the main requisite for applying the CP models with confidence. However, this comes at the cost of decreased model efficiency, as more predictions are identified as inconclusive.</p> <p> </p> <p><strong>Dataset</strong></p> <p>The uploaded file contains the ChEMBL data used in the work for the manuscript “Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data”.</p> <p>Twelve preprocessed datasets containing molecule chembl ID, SMILES, binary activity (i.e. 1 if active, 0 if inactive), publication year, and CHEMBIO descriptors are available for the following ChEMBL endpoints, extracted from ChEMBL Version 26:</p> <ul> <li> <p>CHEMBL220: Acetylcholinesterase (human), 2673 compounds</p> </li> <li> <p>CHEMBL4078: Acetylcholinesterase (fish), 3811 compounds</p> </li> <li> <p>CHEMBL5763: Cholinesterase, 2755 compounds</p> </li> <li> <p>CHEMBL203: EGFR erbB1, 4059 compounds</p> </li> <li> <p>CHEMBL206: Estrogen receptor alpha, 1416 compounds</p> </li> <li> <p>CHEMBL279: VEGFR 2, 5174 compounds</p> </li> <li> <p>CHEMBL230: Cyclooxygenase-2, 2020 compounds</p> </li> <li> <p>CHEMBL340: Cytochrome P450 3A4, 3316 compounds</p> </li> <li> <p>CHEMBL240: HERG, 4976 compounds</p> </li> <li> <p>CHEMBL2039: Monoamine oxidase B, 2534 compounds</p> </li> <li> <p>CHEMBL222: Norepinephrine transporter, 1566 compounds</p> </li> <li> <p>CHEMBL228: Serotonin transporter, 2111 compounds</p> </li> </ul> <p> </p> <p><strong>Usage</strong></p> <p>This dataset can be used as input to run the notebooks available at </p> <p><a href="https://github.com/volkamerlab/CPRecalibration_manuscript_SI">https://github.com/volkamerlab/CPRecalibration_manuscript_SI</a></p> <ol> <li> <p>Clone the GitHub repository.</p> </li> <li> <p>Download the dataset provided here.</p> </li> <li> <p>Copy the dataset (don’t extract) into the data folder of the cloned GitHub repository.</p> </li> <li> <p>Follow the instructions on GitHub.</p> </li> </ol>
Figure 3. Maximum likelihood topologies. A, cytochrome oxidase 1 fragments. B, internal transcribed spacer fragment. C, combined data set. Bootstrap supports over 75 in Integrative taxonomy of Parasabella and Sabellomma (Sabellidae: Annelida) from Australia: description of new species, indication of cryptic diversity, and translocation of some species out of their natural distribution range
Figure 3. Maximum likelihood topologies. A, cytochrome oxidase 1 fragments. B, internal transcribed spacer fragment. C, combined data set. Bootstrap supports over 75% shown on nodes. Scale bar, average of nucleotide substitutions per site.
Data set for the paper "Temperatures and cooling rates recorded by the New Caledonia ophiolite: implications for cooling mechanisms in young forearc sequences"
<p>Mineral data set for the paper "Temperatures and cooling rates recorded by the New Caledonia ophiolite: implications for cooling mechanisms in young forearc sequences"</p>
Data set for the paper "Temperatures and cooling rates recorded by the New Caledonia ophiolite: implications for cooling mechanisms in young forearc sequences"
<p>Mineral data set for the paper "Temperatures and cooling rates recorded by the New Caledonia ophiolite: implications for cooling mechanisms in young forearc sequences"</p>
Data set for publication.
<p>Sequencing data.</p>
Figure 6 in A scolopocryptopid centipede (Chilopoda: Scolopendromorpha) from Mexican amber: synchrotron microtomography and phylogenetic placement using a combined morphological and molecular data set
Figure 6. Single shortest cladogram for six genes and morphology in combination (14 570 steps) under parameter set 3221. Numbers above branches are jackknife frequencies> 50%. Navajo rugs (as explained in Fig. 5) for the six parameter sets shown below branches.
Figure 5 in A scolopocryptopid centipede (Chilopoda: Scolopendromorpha) from Mexican amber: synchrotron microtomography and phylogenetic placement using a combined morphological and molecular data set
Figure 5. Single shortest cladogram for six genes in combination (14 465 steps) under parameter set 3221. Numbers above branches are jackknife frequencies> 50%. Navajo rugs below branches depict monophyly (black) or nonmonophyly (white) of clades under the six parameter sets shown at left; grey box indicates monophyly in some but not all shortest cladograms.
Figure 4 in A scolopocryptopid centipede (Chilopoda: Scolopendromorpha) from Mexican amber: synchrotron microtomography and phylogenetic placement using a combined morphological and molecular data set
Figure 4. Strict consensus of nine best-fit cladograms based on morphological data in Table 2 under implied weights (k = 2, 3, 4, 5, and 6). GC values> 50% shown above branches for concavity constant k = 3. Position of Scolopocryptops simojovelensis highlighted.
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.