Find research datasets worth reusing

opencc-zeroNov 2021View details →

A rodent anchored hybrid enrichment probe set for a range of phylogenetic utility – from order to species

Rodents are the largest order of mammals and contain several model organisms important to scientific research in a variety of fields, yet no large set of genomic markers have been designed for this group to date, hindering evolutionary studies into relationships of the group as a whole. Here we present a genomic probe set designed and optimized for rodents with a protocol easy to replicate with little laboratory investment. This design utilizes an anchored hybrid enrichment approach specifically targeting rodents to generate longer loci with a higher mutation rate than existing vertebrate probes to provide utility at various taxonomic levels. Using a test set of rodents from all five suborders we successfully obtained alignments for 416 of the 418 target loci with an average of 1,379 base pairs per locus and a total alignment of more than half a million base pairs. This genomic dataset performed well in all phylogenetic analyses, especially in recent phylogenetic splits, with ample parsimoniously-informative sites within genera and even within species, showing more than four times as many single nucleotide polymorphisms per locus than a recent vertebrate ultra-conserved elements study. Additional support is provided in resolving basal clades in Rodentia. By providing this probe design, we hope that more labs can easily generate data for answering questions in rodents from species delimitation to understanding relationships among families in rapid radiations.

Data set - Monitoring light pollution with an unmanned aerial vehicle

Dataset used in the study Monitoring light pollution with an unmanned aerial vehicle. A digital camera and a sky quality meter mounted on a UAV have been used to study the relationship between indices computed on night images and night ground brightness (NGB) measured by an optical device pointed downward towards the ground. Both measurements were taken contemporarily during flights at 70 meter and 100 meters altitude, and also varying exposure time.

opennotspecifiedJan 2011View details →

Figure 5. Branch length comparisons across models and taxa sets for 62 in Beyond the prolegomenon: a molecular phylogeny of the Australian camaenid land snail radiation

Figure 5. Branch length comparisons across models and taxa sets for 62 taxa. All common bipartition branch lengths from each of the three taxa set analyses (pruned to the 62 taxa) plotted against the 62t analysis 3p model bipartition branch lengths. Confidence intervals (CI) based on MCMC variation in the most data complete analysis, are approximated by dashed lines.

Data Set: The influence of roughness on experimental fault mechanical behaviour and associated microseismicity

This is the data set used to create the figures for the submitted manuscript, The influence of roughness on experimental fault mechanical behaviour and associated microseismicity. Submitted July 2022 to the Journal of Geophysical Research: Solid Earth. Article DOI: 10.1029/2022JB025113

Data set from: Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial.

Data set from the paper "Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial"  doi: <a href="https://dx.doi.org/10.3390%2Fmedicina57070713">10.3390/medicina57070713</a>

opencc-zeroDec 2021View details →

Oxylipin Data set

The soluble epoxide hydrolase (sEH) enzyme is a major regulator of bioactive lipids. The enzyme is highly expressed in liver and kidney and modulates levels of endogenous epoxy-fatty acids which have pleiotropic biological effects including limiting inflammation, neuroinflammation and hypertension. It has been hypothesized that inhibiting sEH has beneficial effects on limiting obesity and metabolic disease as well. There is a body of literature published on these effects, but typically only male subjects have been included. Here we investigate the role of sEH in both male and female mice and use a global sEH knockout mouse model to compare the effects of diet and diet induced obesity. The results demonstrate sEH activity in the liver is modulated by high fat diets in male compared to female mice. In addition, we characterized the sEH activity in high fat content tissues and demonstrated the influence of diet on levels of bioactive epoxy-fatty acids. The sEH KO animals had generally increased epoxy-fatty acids compared to wildtype mice but gained less body weight on higher fat diets. Proinflammatory prostaglandins and triglycerides were also lower in livers of sEH KO mice fed HFD. Thus, sEH activity, prostaglandins and triglycerides increase in male mice on high fat diet but are all limited by sEH ablation. Additionally, these changes also occur in female mice though at a different magnitude and are also improved by knockout of the sEH enzyme.

opencc-zeroDec 2020View details →

Combining molecular data sets with strongly heterogeneous taxon coverage enlightens the peculiar biogeographic history of stoneflies (Insecta: Plecoptera)

Extant members of the ancient insect order of stoneflies exhibit a disjunct, antitropical distribution, with one major lineage exclusively occurring in the Southern Hemisphere and the other, with few exceptions, on the Northern continents. Here, we address the biogeographic distribution and phylogenetic relationships of stoneflies using a phylogenetic workflow that combines both transcriptomic and Sanger sequence datasets with heterogeneous taxon coverage. We used a dataset comprising 2997 genes derived from the transcriptomes of 30 species and Sanger sequences of seven genes for 498 species. The backbone phylogeny was mainly inferred from the transcriptomic data, whereas the Sanger nucleotide sequence data provided high species density for divergence time estimation and diversification analyses. Our results show that the biogeographic pattern we observe today is primarily more likely shaped by long-distance over-land dispersal than by vicariance. We inferred that the ancestors of extant stoneflies originated in the Northern Hemisphere approximately 265 Ma and were presumably restricted to this area due to climatic and geographic boundaries. Our analyses suggest that with the break-up of Pangaea around 200 Ma and the associated climatic and geographical changes, two groups of stoneflies, the Anarctoperlaria and the Notonemouridae, dispersed to Gondwana and subsequently went extinct on the northern continents. Both groups likely dispersed across Gondwana before its break-up into the modern continents. At least one member of another group of 'northern' stoneflies, the Acroneuriinae, seems to have migrated from North America to South America around 67 Ma. We found four major net diversification rate shifts, indicating rapid radiation patterns that hampered a robust phylogenetic placement of these stonefly groups. Our study provides the first conclusive evolutionary explanation for the unique distribution pattern of stoneflies.

Rushing and Strolling among Answer Sets - Navigation Made Easy (Experiments)

The experiments, we conducted.

PASTIS-R PixelSet - Radar and Optical Satellite Image Time Series in Pixel-Set format

Extension of the <a href="https://zenodo.org/record/5012942#.YaUaQ7so-V6">PASTIS benchmark</a> with radar and optical image time series. In this version the satellite image time series are prepared in pixel-set format. See associated <a href="https://arxiv.org/abs/2112.07558v1">article</a> for more details.

Medical Equipment Image Data set

This data set aims to realise the medical equipment recognition to aid visual search through three deep learning models. The data set contains ten medical equipment classes: commodes, wheelchairs, walking frames, blood pressure monitors, breast pumps, thermometers, rippled mattresses, oximeters, crutches, and therapeutic ultrasound machines. We collected from online resources around 220 images for each medical equipment class. Each image class in the test set has around 40 images. Refer to the paper <a href="http://doi.org/10.3233/JIFS-212786">here</a> or in research gate (preprint).

Data sets used in "Neural network emulation of the formation of organic aerosols based on the explicit GECKO-A chemistry model"

The training, validation, and testing data sets for toluene, dodecane, and alpha-pinene models described in the manuscript. A link to the manuscript will be added here when it becomes available. All trajectories in the data sets were generated using GECKO-A. The source code for using the data sets can be found at https://github.com/NCAR/gecko-ml

opencc-by-4.0Aug 2021View details →

Data set for the paper "Temperatures and cooling rates recorded by the New Caledonia ophiolite: implications for cooling mechanisms in young forearc sequences"

Data set for the paper "Temperatures and cooling rates recorded by the New Caledonia ophiolite: implications for cooling mechanisms in young forearc sequences" by Secchiari et al.

DepthTrack Training Set 01 (ICCV2021)

DepthTrack training set 01, including 100 RGBD sequences and their groundtruth bounding boxes. If you use it, please cite: <blockquote> <pre><code>@InProceedings{yan2021det, author = {Yan, Song and Yang, Jinyu and Kapyla, Jani and Zheng, Feng and Leonardis, Ales and Kamarainen, Joni-Kristian}, title = {DepthTrack: Unveiling the Power of RGBD Tracking}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {10725-10733} }</code></pre> </blockquote>

opencc-zeroDec 2021View details →

A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. In this study, we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when they were involved. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields. This dataset provides the data used in this study and supplementary tables cited in the manuscript.

opencc-by-nc-nd-4.0Nov 2021View details →

Rotterdam EyePACS AIROGS train set

The Rotterdam EyePACS AIROGS train set contains 101,442 color fundus images from 54,274 subjects and approximately 500 different sites with a heterogeneous ethnicity. All images were assigned by human experts with the labels referable glaucoma, no referable glaucoma, or ungradable. This dataset contains the data for training data of the AIROGS challenge, which is held as part of ISBI 2022. See <a href="https://airogs.grand-challenge.org/">https://airogs.grand-challenge.org/</a> for more information on the still ongoing challenge. The paper that summarizes the AIROGS challenge is available on ArXiv here: <a href="https://arxiv.org/abs/2302.01738">https://arxiv.org/abs/2302.01738</a>. Please cite as follows: <pre>@article{devente23airogs, title={AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge}, author={de Vente, Coen and Vermeer, Koenraad A. and Jaccard, Nicolas and Wang, He and Sun, Hongyi and Khader, Firas and Truhn, Daniel and Aimyshev, Temirgali and Zhanibekuly, Yerkebulan and Le, Tien-Dung and Galdran, Adrian and González Ballester, Miguel Ángel and Carneiro, Gustavo and G, Devika R and S, Hrishikesh P and Puthussery, Densen and Liu, Hong and Yang, Zekang and Kondo, Satoshi and Kasai, Satoshi and Wang, Edward and Durvasula, Ashritha and Heras, Jónathan and Zapata, Miguel Ángel and Araújo, Teresa and Aresta, Guilherme and Bogunović, Hrvoje and Arikan, Mustafa and Lee, Yeong Chan and Cho, Hyun Bin and Choi, Yoon Ho and Qayyum, Abdul and Razzak, Imran and van Ginneken, Bram and Lemij, Hans G. and Sánchez, Clara I.}, journal={arXiv preprint arXiv:2302.01738}, year={2023} }</pre>

opencc-zeroJan 2022View details →

VCF for neutral data set in Harpagifer bispinis along the Magellan Province

Quaternary glacial cycles shaped the current distribution of polar and cold-temperate biotas. In the Magellan province of South America, ice covering during the last glacial maximum radically altered the landscape/seascape, speciation rate, and the distribution of species. Here we studied nototheniid fishes Harpagifer spp. along the Magellan province reported as two nominal species: H. bispinis in Patagonia and H. palliolatus, endemic to the Falkland/Malvinas Islands. Previous molecular analyses in Harpagifer showed that the genus may have recently colonized southern South America ~ 1 million years ago. The extensive use in systematics of molecular markers to determine evolutionary units has been improved due to the advances in NGS. Combining traditional DNA sequences and non-targeted GBS-SNPs we evaluated both, the presence of effective evolutionary units and contemporary patterns of genetic structure across the Magellan province. DNA sequences consistently showed an absence of phylogeographic structure, with shared dominant haplotypes between nominal species, pointing towards the presence of a single evolutionary unit. In contrast, SNPs identified three groups in Patagonia, two located north and south of the Strait of Magellan, and a third well-differentiated one in the Falkland/Malvinas Islands. Connectivity analyses using SNPs suggest limited and asymmetric gene flow from Patagonia to the Falkland/Malvinas. Contrasting rough- and fine-scale genetic evolutionary patterns recorded in Harpagifer enhance the relevance in the use of combined methodologies for species delimitation analyses. Depending on the question to be addressed, we could discriminate among phylogeographic structure discarding incipient speciation, and contemporary spatial differentiation processes linked to drift-migration equilibrium models.

opencc-by-4.0Jan 2022View details →

A Data Set for State and Parameter Estimation in Power Systems

This data set consists of data from three power system models of different scales (IEEE 14, IEEE 118 and <a href="https://doi.org/10.5281/zenodo.2642175">PanTaGruEl</a>). For each of these systems, 5 different cases are provided, they are sorted from the least to the most "advanced" system operations. Data are stored in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5</a> format (as H5T_NATIVE_FLOAT) which can be read by (mostly) any language (e.g. Python, Matlab or Julia). Description of the different cases: <ul> <li>Case 1# consists of 2000 samples. Each sample is obtained by: firstly, defining total active and reactive loads in the system which are then distributing to the buses and, secondly, dispatching generation (this is performed by running an OPF (Optimal Power Flow) with <a href="https://matpower.org/">Matpower</a>). The same distribution factors were used for every samples.</li> <li>Case 2# is similar to case 1# with the addition of independent white noises to each bus load.</li> <li>Case 3# differs from case 1# in that independent active and reactive bus loads are randomly drawn.</li> <li>Case 4# is similar to case 3#, but some generators are randomly drawn to be in maintenance. This set of generators is independently generated for each sample.</li> <li>Case 5# is similar to case 4#, plus the generation cost of each generator is randomly drawn from a predefined range. Costs are independently generated for each sample.</li> </ul> General Description: Each data set case file contains the following elements: <ul> <li>V ($N_{\rm bus} \times N_{\rm sample}$ matrix): Voltage magnitudes,</li> <li>theta ($N_{\rm bus} \times N_{\rm sample}$ matrix): Voltage phases,</li> <li>P ($N_{\rm bus} \times N_{\rm sample}$ matrix): <a href="https://en.wikipedia.org/wiki/AC_power">Active</a> power injections (i.e. = generation - load),</li> <li>Q ($N_{\rm bus} \times N_{\rm sample}$ matrix): <a href="https://en.wikipedia.org/wiki/AC_power">Reactive</a> power injections,</li> <li>idgen ($N_{\rm gen}$ vector): index of generator buses,</li> <li>id_slack: index of the bus used as <a href="https://en.wikipedia.org/wiki/Slack_bus">slack bus</a>,</li> <li>epsilon ($N_{\rm line} \times 2$ matrix): list of the lines in the system (Each row corresponds to a line. Entries are buses’ indices.),</li> <li>b ($N_{\rm line}$ vector): line <a href="https://en.wikipedia.org/wiki/Admittance">susceptances</a>,</li> <li>g ($N_{\rm line}$ vector): line <a href="https://en.wikipedia.org/wiki/Admittance">conductances</a>,</li> <li>bsh ($N_{\rm bus}$ vector): shunt susceptances,</li> <li>gsh ($N_{\rm bus}$ vector): shunt conductances.</li> </ul> Visualization: The data set also includes bus coordinates. Some theory: The <a href="https://en.wikipedia.org/wiki/Incidence_matrix">incidence matrix</a> B is defined as $B_{ij} = \left\{\begin{array}{l}-1,\; \text{if line $j$ starts at bus $i$,}\\1,\; \text{if line $j$ ends at bus $i$,}\\ 0,\; \text{otherwise.} \end{array}\right.$ (“Ends” and “starts” are purely conventional, but they have to be assigned to account for the direction power flows in the system. We use the first column of epsilon as "starts" and the second one as "ends".) The <a href="https://en.wikipedia.org/wiki/Nodal_admittance_matrix">admittance matrix</a> Y is obtained by $y = g + ib,\\ y_{\rm sh} = g_{\rm sh} + ib_{\rm sh},\\ Y = B\,{\rm diag}(y)\,B^\top + {\rm diag}(y_{\rm sh}) .$ Defining the <a href="https://en.wikipedia.org/wiki/AC_power">complex</a> power injections and voltages, respectively, as $S = P + iQ,\\ \underline{V} = V \cdot e^{i \theta}, $ where $\cdot$ denotes the element-wise product. One has the following relation $S = \underline{V} \cdot {\rm conj}(Y\, \underline{V}).$ This relation is equivalent to the <a href="https://en.wikipedia.org/wiki/Power-flow_study">power flow equations</a>. <ul> </ul>

opencc-by-4.0Jan 2022View details →

iMAC data set

================================================================================ Title: Comparison between core-collapse supernova nucleosynthesis and meteoric         stardust grains: investigating magnesium, aluminium, and chromium  Authors: den Hartogh J., Peto M.K., Lawson T., Sieverding A., Brinkman H.,      Pignatari M., Lugaro M.  ================================================================================ Description of contents: the data files in this data deposit are either stellar profiles or stellar yields as used in the paper to create figures. All files are machine readable .txt files. Any text editor is able to open these files.       A complete list of the files in this repository are provided here:         iMAC_profiles/m15exp_LAW_D_subm.txt         iMAC_profiles/m15exp_LAW_R_subm.txt         iMAC_profiles/m15exp_RIT_D_subm.txt         iMAC_profiles/m15exp_RIT_R_subm.txt         iMAC_profiles/m15exp_SIE_D_subm.txt         iMAC_profiles/m15exp_SIE_R_subm.txt         iMAC_profiles/m20exp_LAW_D_subm.txt         iMAC_profiles/m20exp_LAW_R_subm.txt         iMAC_profiles/m20exp_RIT_D_subm.txt         iMAC_profiles/m20exp_RIT_R_subm.txt         iMAC_profiles/m20exp_SIE_D_subm.txt         iMAC_profiles/m20exp_SIE_R_subm.txt         iMAC_profiles/m25exp_LAW_D_subm.txt         iMAC_profiles/m25exp_LAW_R_subm.txt         iMAC_profiles/m25exp_RIT_D_subm.txt         iMAC_profiles/m25exp_RIT_R_subm.txt         iMAC_profiles/m25exp_SIE_D_subm.txt         iMAC_profiles/m25exp_SIE_R_subm.txt         iMAC_yields/m15_yields_D.txt         iMAC_yields/m20_yields_D.txt         iMAC_yields/m25_yields_D.txt     For the profiles the *R_subm.txt files have the following columns:     mass,he4,c12,o16,ne20,si28,ni56,fe56,al26,al27,mg24,mg25,mg26,cr50,cr52,cr53,cr54     All isotopes are radioactive or non-decayed.      For the profiles the *D_subm.txt files have the following columns:     mass,al27,mg24,mg25,mg26,cr50,cr52,cr53,cr54     All isotopes include all radioactive contributions and are thus fully decayed.      The yield files have the following columns:     ref Mg24, Mg25, Mg26, Al26, Al27, Cr50, Cr52, Cr53, Cr54     All yields are fully decayed. The refs are equal to the labels in Figure 2.                 System requirements: - Additional comments: the Cr53 data for the middle row of Figure 3 is not included in these files. ================================================================================

opencc-by-4.0Jan 2022View details →

Data set for this article tiled " the relationship between product innovation and customer satisfaction

my data set for this article tiled " the relationship between product innovation and customer satisfaction

DepthTrack Training Set 02 (ICCV2021)

DepthTrack training set 02, including 52 RGBD sequences and their groundtruth bounding boxes. If you use it, please cite: <blockquote> <pre><code>@InProceedings{yan2021det, author = {Yan, Song and Yang, Jinyu and Kapyla, Jani and Zheng, Feng and Leonardis, Ales and Kamarainen, Joni-Kristian}, title = {DepthTrack: Unveiling the Power of RGBD Tracking}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {10725-10733} }</code></pre> </blockquote>