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.
1,392
datasets available to search
ShareScore release 0.9.0
Dataset results
1,392 results for “Match”
Explaining human mobility predictions through a pattern matching algorithm
<p>The name of the file indicate information:<br> {type of sequence}_{type of measure}_{sequence properites}_{additional information}.csv</p> <p>{type of sequence} - 'synth' for synthetic or 'london' for real mobility data from London, UK.<br> {type of measure} - 'r2' for R-squared measure or 'corr' for Spearman's correlation<br> {sequence properties} - for synthetic data there are three types of sequences, described in the research article (random, markovian, nonstationary). For real mobility data this part includes information about data processing parameters: (...)_london_{type of mobility sequence}_{DBSCAN epsilon value}_{DBSCAN min_pts value}. {type of mobility sequence} is 'seq' for next-place sequences and '30min' or '1H' for the next time-bin sequences and indicate the size of the time-bin.<br> Files with 'predictability' at the end of the file contain R-squared and Spearman's correlation of measures calculated in relation to the predictability measure.</p> <p>R2 files include values of R-squared for all types of modelled regression functions.<br> 'line' indicates {y = ax + b} for single variable and {y = ax + by + c} for two variables.<br> 'expo' indicates {y = a*x^b + c} for single variable and {y = a*x^b + c*y^d + e} for two variables<br> 'log' indicates {y = a*log(x*b) + c} for single variable and {y = a * x + c * log(y) + e + d*x * log(y)} for two variables.<br> 'logf' indicates {y = a*log(x) + c * log(y) + e + b*log(x) * log(y)} for two variables</p>
Data from: Global plant-frugivore trait matching is shaped by climate and biogeographic history
<p>Species interactions are influenced by the trait structure of local multi-trophic communities. However, it remains unclear whether mutualistic interactions, in particular, can drive trait patterns at the global scale, where climatic constraints and biogeographic processes gain importance. Here we evaluate global relationships between traits of frugivorous birds and palms (<em>Arecaceae</em>), and how these relationships are affected, directly or indirectly, by assemblage richness, climate and biogeographic history. We leverage a new and expanded gape size dataset for nearly all avian frugivores, and find a positive relationship between gape size and fruit size, that is, trait matching, which is influenced indirectly by palm richness and climate. We also uncover a latitudinal gradient in trait matching strength, which increases towards the tropics and varies among zoogeographic realms. Taken together, our results suggest trophic interactions have consistent influences on trait structure, but that abiotic, biogeographic and richness effects also play important, though sometimes indirect, roles in shaping the functional biogeography of mutualisms.</p>
Matching Network of Ontologies: a Pattern Recognition Approach
<p>Networks of Ontologies research deals with the need to combine several ontologies at the same time. In a world of integrated systems (system of systems), isolated systems are increasingly rare in the near future, and their integration creates opportunities to change, validate information and add more value to an information system. This system of systems can contain ontologies to support the corresponding knowledge model. Consequently, new integration requirements may have to deal with network alignment rather than single ontologies. This work delves into the area of network alignment and proposes new ways to approach a particular case of alignment of large ontologies. The contribution of the work is the use of algebraic operations on networks to eliminate candidates before alignment and to use a stochastic search method to discover the relevant nodes. These nodes should be retained as they increase accuracy and final alignment retrieval even though they are identical and removed by the algebraic operation. To find out the particular relevance of each node, we propose a random walk combined with a frequent itemsets approach that overcomes the force brute approaches in processing time, as the size of networks grows, and have close precision. The approach was validated using networks of ontologies created from the OAEI ontologies. The approach selected the entities to send to the matcher without losing significant preexisting alignments. Finally, two different matchers were used to get metrics and compare the results with the pairwise force brute approach.</p>
Supplementary Data: Mapping of local lattice parameter ratios by projective Kikuchi pattern matching
<p>This is the experimental dataset which was analyzed in:</p> <p>"Mapping of local lattice parameter ratios by projective Kikuchi pattern matching"<br> Aimo Winkelmann, Gert Nolze, Grzegorz Cios, and Tomasz Tokarski<br> Phys. Rev. Materials <strong>2</strong> (2018) 123803<br> https://doi.org/10.1103/PhysRevMaterials.2.123803</p> <p>We describe a lattice-based crystallographic approximation for the analysis of distorted crystal structures via electron backscatter diffraction (EBSD) in the scanning electron microscope. EBSD patterns are closely linked to local lattice parameter ratios via Kikuchi bands that indicate geometrical lattice plane projections. Based on the transformation properties of points and lines in the real projective plane, we can obtain continuous estimations of the local lattice distortion based on projectively transformed Kikuchi diffraction simulations for a reference structure. By quantitative image matching to a projective transformation model of the lattice distortion in the full solid angle of possible scattering directions, we enforce a crystallographically consistent approximation in the fitting procedure of distorted simulations to the experimentally observed diffraction patterns. As an application example, we map the locally varying tetragonality in martensite grains of steel.</p>
Evolution of a mosquito's hatching behavior to match its human-provided habitat
<p>A subspecies of the yellow fever mosquito, <em>Aedes aegypti</em>, has recently evolved to specialize in biting and living alongside humans. It prefers human odor and breeds in human-provided artificial containers rather than the forest tree holes of its ancestors. Here, we report one way this human specialist has adapted to the distinct ecology of human environments. While eggs of the ancestral subspecies rarely hatch in pure water, those of the derived human-specialist do so readily. We trace this novel behavior to a shift in how eggs respond to dissolved oxygen, low levels of which may signal food abundance. Moreover, we show that while tree holes are consistently low in dissolved oxygen, artificial containers often have much higher levels. There is thus a concordance between the hatching behavior of each subspecies and the aquatic habitat it uses in the wild. We find this behavioral variation is heritable, with both maternal and zygotic effects. The zygotic effect depends on dissolved oxygen concentration (i.e., GxE), pointing to potential changes in oxygen-sensitive circuits. Together, our results suggest that a shift in hatching response contributed to the pernicious success of this human-specialist mosquito and illustrate how animals may rapidly adapt to human-driven changes in the environment.</p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 2. New neuronal model, based on matching a wave-like representation of the output.
<p>For the new model, shown in Figure 2, the ‘differences’ between the outputs is measured and combined, to produce a kind of wave-like shape. If output point 1 has a value of 10, and output point 2 has a value of 5, for example, then this results in the shape moving down 5 points on the wave shape. This shape will, of course, change depending on the order that the dataset values are presented in. Also, if new data is presented, then that will produce a different shape. However, there is still an association between the input values and the output values and it is still this relation that is being learnt. The neural network needs to be able to learn a function that can generalise over the presented datasets, so that it can recognise the relation in previously unseen data as well. Generalising over trying to learn a wave-like shape, or trying to match the output values exactly looks quite similar, suggesting that the process is at least valid. In Figure 2, note that a transposition to move the learned shape up or down first is possible, before a weight value would try to scale it. If the initial match of the combined inputs can be as close as possible, then these adjustments will become less and will be more for fine tuning.</p>
Template matching catalog of the seismicity induced by hydraulic fracturing operations at Preston New Road (UK) in 2019
<p>The file PNR2_TMcatalog.csv contains the catalog of seismicity induced by the hydraulic fracturing operations carried out at Preston New Road (UK) in 2019. The catalog was created with template matching using a single downhole sensor located in a monitoring well.</p> <p>Here is the description of the columns found in the file:</p> <ul> <li>ARRTIME: P-wave arrival times at the reference station.</li> <li>X, Y, Z: hypocentral coordinates (easting, northing and depth). Northing and easting are expressed in the United Kingdom Ordnance Survey coordinate system (EPSG:27700). Depth is expressed in meters below sea level.</li> <li>MW: moment magnitudes.</li> <li>CLUSTER: cluster ID. Earthquakes with the same cluster ID were detected by the same template.</li> </ul>
Scripts and analysis files for categorization of PKZILLA matching proteomic peptides into protein-unique, protein-multimatch & exon-unique, exon-multimatch categories.
<p>A .zip file containing the source data files & Jupyter notebook for analysis of the <em>Prymnesium parvum</em> 12B1 PKZILLA-detecting proteomic results (<a href="https://doi.org/10.5281/zenodo.10023441">https://doi.org/10.5281/zenodo.10023441</a>), and the resulting files from the workflow. See "Analysis of proteomic results" section of the manuscript Materials and Methods for further detail. </p> <p><strong>Key files:</strong></p> <ul> <li>'PKZILLA-1_classify_peptides.txt' - A plaintext report of the # of classified peptides for PKZILLA-1</li> <li>'PKZILLA-2_classify_peptides.txt' - A plaintext report of the # of classified peptides for PKZILLA-2</li> <li>'./hierarchical_classified_xlsx/' - Excel spreadsheets with the classified peptides for PKZILLA-1 and PKZILLA-2</li> <li>'./Process_into_polypeptide_coordinates/' - Workflow, results, and plots for back-alignment of peptides back to PKZILLA-1 and PKZILLA-2 genomic loci</li> </ul>
MIX 'N' MATCH Di egent ogganizations gecognize di egent numbegs of bigd species, mostcy because they use di egent de nitions fog what constitutes a species. in Taxonomy anarchy hampers conservation
MIX 'N' MATCH Di egent ogganizations gecognize di egent numbegs of bigd species, mostcy because they use di egent de nitions fog what constitutes a species.
Dataset: Match Group, Inc. (MTCH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 4 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 4. Scatter diagram load ratio versus ant size. The load ratio was calculated as follows: ant size (head width) + load size/ant size. Data are from random samples of returning foragers of a single M. wasmanni colony. One dot represents one observation (N = 776). Linear regression analysis revealed a low negative correlation (R² = 0.14, p = 0.0001) between ant size and load ratio. The larger the worker size class, the smaller the range in the load ratio. Residuals from regressions were approximately normally distributed around zero in all cases.
Figure 5 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 5. Mean foraging efficiency (in %) per day. Calculations were performed separately per size class and per season. Foraging efficiency varied considerably over the seasons and between size classes. Sample size represented by numbers in bars. Minor = minor-sized workers, Media = media-sized workers, Major = major-sized workers.
Figure 3 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 3. Scatter diagram load size versus ant size. Data are from random samples of returning foragers of a single M. wasmanni colony. One dot represents one observation (N = 776). Linear regression analysis revealed a very low positive correlation (R² = 0.02, p = 0.0001) between ant size and load size, indicating only a small tendency for majorsized workers to carry larger loads than minor-sized workers. Residuals from regressions were approximately normally distributed around zero in all cases.
Figure. 1 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure. 1. Overview of the topics discussed in the paper. Surface activity was analysed at both colony level and individual level.
Figure 2 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 2. Frequency distribution of harvested material in percent (May, July-August and October 2009). During periods of aboveground activity, returning foragers carrying food items and other materials were collected at random from foraging trails 10 cm far from the nest entrances.
Bio-ML: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
<p> </p> <blockquote> <p><strong>This version is used in the Bio-ML track of the OAEI 2024; the only change compared to the OAEI 2023 is the deletion of certain training subsumption mappings.</strong></p> </blockquote> <p> </p> <h3><strong>Overview</strong></h3> <p>The purpose of these datasets is to support <em>equivalence</em> and <em>subsumption</em> ontology matching.</p> <p>There are five ontology pairs extracted from MONDO and UMLS:</p> <table> <tbody> <tr> <td>Source</td> <td>Task</td> <td>Category</td> <td>#SrcCls</td> <td>#TgtCls</td> <td>#Ref (equiv)</td> <td>#Ref (subs)</td> </tr> <tr> <td>Mondo</td> <td>OMIM-ORDO</td> <td>Disease</td> <td>9,648</td> <td>9,275</td> <td>3,721</td> <td>103</td> </tr> <tr> <td>Mondo</td> <td>NCIT-DOID</td> <td>Disease</td> <td>15,762</td> <td>8,465</td> <td>4,686</td> <td>3,338 (-1)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-FMA</td> <td>Body</td> <td>34,418</td> <td>88,955</td> <td>7,256</td> <td>5,453 (-53)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-NCIT</td> <td>Pharm</td> <td>29,500</td> <td>22,136</td> <td>5,803</td> <td>4,224 (-1)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-NCIT</td> <td>Neoplas</td> <td>22,971</td> <td>20,247</td> <td>3,804</td> <td>213</td> </tr> </tbody> </table> <p>The "-" numbers reflect the changes due to lthe deletion of certain training subsumption mappings.</p> <p>The main track is available at "bio-ml", where each pair is associated with a task folder, containing the source and target ontologies, reference equivalence mappings (in "refs_equiv"), reference subsumption mappings ("refs_subs"). </p> <p>The special sub-track is available at "bio-llm", where each pair is associated with a task folder, containing the source and target ontologies, and the test candidate mappings. </p> <p> </p> <h3><strong>Citation</strong></h3> <p><strong>Bio-ML (Main Track)</strong></p> <pre>```<br>@inproceedings{he2022machine, title={Machine learning-friendly biomedical datasets for equivalence and subsumption ontology matching}, author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Jim{\'e}nez-Ruiz, Ernesto and Hadian, Ali and Horrocks, Ian}, booktitle={International Semantic Web Conference}, pages={575--591}, year={2022}, organization={Springer} }<br>```</pre> <p><strong>Bio-LLM (Sub-track)</strong></p> <pre>```<br>@article{he2023exploring, title={Exploring large language models for ontology alignment}, author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Horrocks, Ian}, journal={arXiv preprint arXiv:2309.07172}, year={2023} }<br>```</pre> <p> </p> <h3><strong>Important Links</strong></h3> <ul> <li>See detailed documentation at: <a href="https://krr-oxford.github.io/DeepOnto/bio-ml">https://krr-oxford.github.io/DeepOnto/bio-ml</a>.</li> <li>See the OAEI Bio-ML track at: <a href="https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/">https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/</a></li> <li>See our resource paper for the original Bio-ML at <a href="https://arxiv.org/abs/2205.03447">arxiv</a> or <a href="https://link.springer.com/chapter/10.1007/978-3-031-19433-7_33">springer</a> (accepted at <em>ISWC-2022</em> and nominated as the <em>best resource paper candidate</em>). See our poster paper for the Bio-LLM sub-track at <a href="https://arxiv.org/abs/2309.07172">arxiv </a>(accepted at <em>ISWC-2023 Posters & Demos</em>).</li> </ul> <p> </p> <h3><strong>Changelog</strong></h3> <p>The only change in this version compared to the OAEI 2023 is the deletion of certain training subsumption mappings that can be directly exploited through deductive reasoning.</p>
Fig. 14 Erythraeus regalis, larva. a in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 14 Erythraeus regalis, larva. a Gnathosoma and idiosoma, dorsal view. b Dorsal opisthosomal seta. c Gnathosoma and idiosoma, ventral view
Fig. 11 Erythraeus cinereus, larva. a Leg I. b Leg II. c Leg III. d Tarsus I. e Tarsus II in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 11 Erythraeus cinereus, larva. a Leg I. b Leg II. c Leg III. d Tarsus I. e Tarsus II (d, e, only specialized setae shown)
Fig. 9 Erythraeus cinereus, larva. a Chelicera. b in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 9 Erythraeus cinereus, larva. a Chelicera. b Gnathosoma (and scutum), dorsal view. c Gnathosoma, ventral view. d Palp tibia. e Palp tarsus
Fig. 8 Erythraeus cinereus, adult. a Palp, medial view. b Crista metopica and eyes. c Dorsal opisthosomal setae. d in Towards resolving the double classification in Erythraeus (Actinotrichida: Erythraeidae): matching larvae with adults using 28S sequence data and experimental rearing
Fig. 8 Erythraeus cinereus, adult. a Palp, medial view. b Crista metopica and eyes. c Dorsal opisthosomal setae. d Serratala on genu I. e Serratala on genu IV. f Diversity of serratalae and setae of non-serratalae type on telofemora, genua, and tibiae of legs I–IV
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.