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.
2,001
datasets available to search
ShareScore release 0.9.0
Dataset results
2,001 results for “Orders”
Linked collectors and determiners for: Collection of various insect orders, Natural History Museum, University of Oslo.
Natural history specimen data linked to collectors and determiners held within, "Collection of various insect orders, Natural History Museum, University of Oslo". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/4cfd80a7-d630-4d7f-89fb-d2d1e749a418">https://bionomia.net/dataset/4cfd80a7-d630-4d7f-89fb-d2d1e749a418</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4cfd80a7-d630-4d7f-89fb-d2d1e749a418">https://gbif.org/dataset/4cfd80a7-d630-4d7f-89fb-d2d1e749a418</a>. Formatted as a Frictionless Data package.
Data from: Topological nano-switches in higher-order topological insulators
<p>This repository provides data underlying the figures in the manuscript titled 'Topological nano-switches in higher-order topological insulators'.</p> <p>The data is given in CSV-format and the data files are named after their respective figures. The data corresponding to figure 2 is provided separately for the four field orientations in each panel of the figure, and contains the probability density on each site of the discretized system as a 2D array. Similarly, the data for figure C1 is provided separately. The other files contain the scattering matrix elements in columns labeled 'ij', with each row corresponding to an orientation of the magnetic field in the range [0, 2π] listed in the column labeled 'theta'. </p> <p>The data was generated using Kwant, a free (open source), powerful, and easy to use Python package for numerical calculations on tight-binding models. An example of how the studied systems were constructed is included in 'higher_order_ti_nano_switches.py', and contains the code to calculate the corresponding scattering matricies for a chosen set of parameters. The probability density included in the data related to figure 2 can similarly be extracted from the discretized system. See '<a href="https://kwant-project.org/">kwant-project.org</a>' for additional details.</p>
Data and code repository for «Magnetic Order in Nanoscale Gyroid Netwoks»
<p>Data and Code repository for </p> <p>«Magnetic Order in Nanoscale Gyroid Netwoks»</p> <p>Ami S. Koshikawa, Justin Llandro, Masayuki Ohzeki, Shunsuke Fukami, Hideo Ohno, and Naëmi Leo</p> <p>Physical Review B 108, 024414 (2023)</p> <p>https://doi.org/10.1103/PhysRevB.108.024414</p>
Experimental X-ray Diffraction Data for "Cooling-Induced Order-Disorder Phase Transition in CsPbBr3 Nanocrystal Superlattices"
<p>Experimental X-ray diffraction data: </p> <p>-- temperature-dependent diffraction patterns (theta:2theta, rocking curves) for C18 and C8 CsPbBr3 nanocrystal superlattice samples;</p> <p>-- room temperature diffraction patterns (theta:2theta, rocking curves) for C6, C8, C10, C12, and C18 CsPbBr3 nanocrystal superlattices;</p> <p>in all files, first column is angle in degrees and the second column is intensity.</p>
Measured and analyzed raw data for publication "Nanoscale spin ordering and spin screening effects in tunnel ferromagnetic Josephson junctions" (doi: https://doi.org/10.1038/s43246-024-00497-1)
<p>The data provided by this dataset are the raw data published in the paper "Nanoscale spin ordering and spin screening effects in tunnel ferromagnetic Josephson junctions" (doi: https://www.nature.com/articles/s43246-024-00497-1). </p> <p>It can be found:</p> <p>-In the folder figure2_IV, the current-voltage characteristics (IV) of standard Superconductor-Insulator-Superconductor Josephson Junctions (SIS JJ) and of Superconductor-Insulator-Ferromagnet-thin superconductor- Superconductor Josephson Junctions (SIsFS JJ) at 10 mK </p> <p>-In the folder figure2_IVH, the magnetic dependence of the critical current of the SIsS and SIsFS at 10 mK</p> <p>-In the folder figure3_IVHT, the magnetic dependence of the critical current of the SIsFS as a function of the temperature T</p> <p>-In the figure4_gamma, the experimental and theoretical dependence of \gamma, i.e., the magnetic moment of the S-layers normalized to the F-layer in absolute value, as a function of the characteristic energy of the inverse proximity effect</p>
Dataset of the publication: Chemical design and magnetic ordering in thin layers of 2D MOFs
<p>Dataset of the publication: Chemical design and magnetic ordering in thin layers of 2D MOFs</p> <p>DOI: 10.1021/jacs.1c07802</p> <p>López-Cabrelles, J; Mañas-Valero, S; Vitórica-Yrezábal, IJ; Siskins, M; Lee, M; Steeneken, PG; van der Zant, HSJ; Espallargas, GM; Coronado, E<br>J. Am. Chem. Soc. 2021, 143, 44, 18502–18510</p>
Datasets for ``Leading-order nonlinear gravitational waves from reheating magnetogeneses''
<pre>This directory contains an index.html file with links to the run directories with secondary data for Table II of the paper "Leading-order nonlinear gravitational waves from reheating magnetogeneses" by Yutong He, Axel Brandenburg, and Alberto Roper Pol. If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Reproducibility Package: Automata-Driven Partial Order Reduction and Guided Search for LTL
<p>Reproducibility package for the paper <strong>Automata-Driven Partial Order Reduction and Guided Search for LTL Model Checking</strong> accepted for VMCAI'22.</p> <p>Contains scripts, 64-bit Linux binaries, and the dataset used to run the experiments detailed in the paper, a source code snapshot used to compile those binaries, as well as scripts for processing the results into the tables seen in the paper. The file README.md contains details on running the various parts of the artifacts, assuming a Linux environment.</p>
1847 in Atlas of European millipedes 3: Order Chordeumatida (Class Diplopoda)
1847, according to Enghoff et al. (2015) with recent modifications: 1. Family Anthogonidae Ribaut, 1913 has been reinstated and added to superfamily Anthroleucosomatoidea; it includes Acherosomatidae Verhoeff, 1929 and Biokoviellidae Mršić, 1992 as junior synonyms (Antić et al. 2015a, 2016). 2. Family Dalmatosomatidae Antić & Makarov, 2018, was described as new and placed in Branneroidea by Antić et al. (2018c). 3. Family Hungarosomatidae Ceuca,1974, was reinstated by Mock et al. (2016) but not assigned to a superfamily. Based on the discussion by Mock et al. (2016), it is here placed as Craspedosomatidea incertae sedis. Families covered by the present volume are shown in bold and are followed by the number of known European species
FIGURE. Annual and cumulative totals of Thysanoptera species descriptions in Brazil (image provided by Elison Lima). in All genera of the world: Order Thysanoptera (Animalia: Arthropoda: Insecta)
FIGURE. Annual and cumulative totals of Thysanoptera species descriptions in Brazil (image provided by Elison Lima).
Dataset: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Dataset used in the experiments of the publication: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data" by Bach et al.</p> <p><strong>File description:</strong></p> <ul> <li> <p>cfmid4.tar: MS² spectra simulated using <a href="https://bitbucket.org/wishartlab/cfm-id-code/src/CFM-ID_4.0.7/">CFM-ID (v4.0.7)</a> for all molecular candidate structures</p> </li> <li> <p>db_layout.png: Visualization of the SQLite database (DB) layout</p> </li> <li> <p>massbank.sqlite.gz: DB containing all needed data to (re-)run the experiments shown in the paper. Please read "DB_README.md" for further details. The database file can be unpacked using gzip.</p> </li> <li> <p>metfrag.tar: MetFrag input files and MS² scores for all candidate sets computed using the <a href="https://ipb-halle.github.io/MetFrag/projects/metfragcl/">MetFrag software</a>.</p> </li> <li> <p>sirius_scores.tar: MS² scores for all candidates and measured spectra using the <a href="https://bio.informatik.uni-jena.de/software/sirius/">SIRIUS software</a>.</p> </li> <li> <p>sirius_inputs.tar: Input (ms-files) for the SIRIUS software.</p> </li> <li> <p>DB_README.md: Description of each table in the "massbank.sqlite" SQLite DB.</p> </li> <li> <p>db_processing_scripts.tar: Scripts to re-produce the "massbank.sqlite" and a README.md providing further information on the process.</p> </li> <li> <p>massbank__2020.11__v0.6.1.sqlite: Base SQLite DB from which the "massbank.sqlite" was build up. It was created using the "<a href="https://github.com/bachi55/massbank2db">massbank2db</a>" (v0.6.1) Python package using the <a href="https://github.com/bachi55/MassBank-data/tree/2020.11-branch">MassBank release 2020.11</a>.</p> </li> <li> <p>substructure_fingerprints.tar: Pre-computed substructure counting fingerprints for all candidates related to our experiments.</p> </li> </ul> <p><strong>Instructions:</strong></p> <p>The "massbank.sqlite" can be directly used with the Structure Support Vector Machine Model (SSVM) described in the manuscript and implemented in the "<a href="https://github.com/aalto-ics-kepaco/msms_rt_ssvm">ssvm</a>" Python package.</p> <p>If desired, the database can be re-produced using the scripts provided in "db_processing_scripts.tar":</p> <ol> <li>Create a directory for all data</li> <li>Download and extract the ... <ol> <li>Processing scripts</li> <li>MS² scorer outputs (e.g. metfrag.tar)</li> <li>Pre-computed substructure fingerprints</li> </ol> </li> <li>Follow the instructions given in the "README.md" of the "db_processing_scripts.tar"</li> </ol>
Entropy driven order in an array of nanomagnets
<p>Long-range ordering, while typically understood as a decrease in entropy, can also be driven by increasing system entropy in certain special cases. We demonstrate that artificial spin ice arrays of single-domain nanomagnets can be designed to produce entropy-driven order. We probe thermally active tetris artificial spin ice, known to have a zero point Pauli entropy, both experimentally and through simulations. We find two-dimensional magnetic ordering in one subset of the nanomagnet moments, which we demonstrate to be induced by disorder (i.e., increased entropy) in another subset of moments. Contrasting with other entropy-driven systems, the degrees of freedom in artificial spin ice are both designable and directly observable at the microscale, and the entropy of the system is precisely calculable in simulations. This robust example, in which the system's interactions and ground state entropy are well-defined, significantly expands the experimental landscape for the study of entropy-driven ordering.</p>
Fig. 1 in Chewing Lice (Order Mallophaga, Suborders Amblycera And Ichnocera) Fauna Of Domestic Chicken (Gallus Gallus Domesticus) In Ukraine
Fig. 1. Menopon gallinae: ♀: 1 — forehead; 2 — temporal lobe; 3 — antenna; 4 — eyes; 5 — abdomen (×400); ♂: 1 — forehead; 2 — temporal lobe; 3 — antenna; 4 — foot; 5 — bristles; 6 — abdomen posterior (×300).
Fig. 4 in Chewing Lice (Order Mallophaga, Suborders Amblycera And Ichnocera) Fauna Of Domestic Chicken (Gallus Gallus Domesticus) In Ukraine
Fig. 4. Morphology of Goniocotes hologaster: ♀: 1 — forehead; 2 — eyes; 3 — bristles on head; 4 — the rear of the abdomen (×300); ♂: 1 — head; 2 — temporal edges; 3 — overall oval body; 4 — the last segment of the abdomen blade-shaped (×250).
Fig. 3 in Chewing Lice (Order Mallophaga, Suborders Amblycera And Ichnocera) Fauna Of Domestic Chicken (Gallus Gallus Domesticus) In Ukraine
Fig. 3. Morphology of Menacanthus cornutus: ♀: 1 — forehead; 2 — temporal lobe; 3 — sternal plate; 4 — crop; 5 — posterior part of the abdomen with bristles; ♂: 2 — eye; 3 — prothorax with foots; 4 — mesothorax; 5 — metathorax; 6 — abdomenal bristles; 7 — oval shape of the rear of the abdomen; 8 — ejaculatory ducts (×400).
Fig. 2 in Chewing Lice (Order Mallophaga, Suborders Amblycera And Ichnocera) Fauna Of Domestic Chicken (Gallus Gallus Domesticus) In Ukraine
Fig. 2. Morphology Menacanthus stramineus: ♀: 1 — forehead; 2 — temporal lobe; 3 — prothorax; 4 — mesothorax; 5 — metathorax; 6 — tarse; 7 — crop; 8 — the egg chamber; ♂: 1 — prothorax foot; 2 — foot mesothorax; 3 — foot metathorax; 4 — testes; 5 — crop (×400).
Research Data supporting "Pachyrhynchus weevils use 3D photonic crystals with varying degrees of order to create diverse and brilliant displays"
<p>The research data is arranged into different folders containing the following files (.txt, .tif, .xlsx files; <em>italics</em>). This data and the descriptions below should be read in conjunction with the manuscript and “Supporting Info”, both of which may be found at the following DOI: 10.1002/smll.202200592.</p>
Extending a High-Performance Prover to Higher-Order Logic
<p>This is the package containing raw evaluation data and other related data for the submission<br> Extending a High-Performance Prover to Higher-Order Logic.</p> <p>The problems used for the evaluation are stored in the problems/ directory. Higher-order TPTP benchmarks are in TPTP_HO subdirectory, while first-order TPTP benchmarks are in TPTP_FO subdirectory. Sledgehammer benchmarks are stored in SH subdirectory.</p> <p>Figures 2 and 3 in the submission are automatically created using Python script get_table.py from scripts/ directory. This script processes raw data obtained from StarExec, which is stored in the results/ directory.</p> <p>To create Figure 2 use the following command:</p> <p> python3 scripts/get_table.py results/ scripts/tptp_sh.json</p> <p>Figure 3 is created using:</p> <p> python3 scripts/get_table.py results/ scripts/fo.json</p> <p>Directory e-26-4 contains both first-order (eprover) and higher-order (eprover-ho)<br> binaries compiled under Ubuntu 18-04. The directory e-26-4 can be packaged in<br> an archive and submitted to StarExec for evaluation. starexec_run_as4_serialize.sh<br> was used on SH benchmarks, starexec_run_as8.sh was used on higher-order TPTP<br> benchmarks. starexec_run_fo-boa and starexec_run_ho-boa were used on first-order<br> benchmarks. To run the scripts locally, environment variables STAREXEC_CPU_LIMIT<br> and STAREXEC_WALLCLOCK_LIMIT must be set. Note these scripts do not limit any resources<br> of lambdaE and rely on StarExec facilities for this purpose.</p> <p> </p> <p>lambdaE was compiled from the master_bce_merge, branch of eprover github (https://github.com/eprover/eprover),<br> with the git commit hash 693e48236d18e6b9a8f60db9ff2c56503ed16945.</p>
Data from: Out of the tropics: Macroevolutionary size trends in an old insect order are shaped by temperature and predators
<p>Global body size distributions are shaped by selection pressures arising from biotic and abiotic factors such as temperature, predation and parasitism. Here, we investigated the ecological and evolutionary drivers of global latitudinal size gradients in an old insect order (Odonata; dragonflies and damselflies). Phylogenetic comparative analyses revealed that global size variation of extant taxa is negatively influenced by both regional avian diversity and temperature. Interestingly, fossil data show that the relationship between wing size and latitude has shifted: latitudinal size trends had initially negative slopes but became shallower or positive following the emergence of birds 150 MYA. These changing size-latitude trends over geological time were likely driven by bird predation and high dispersal ability of large dragonflies. Our results therefore suggest that latitudinal size gradients were shaped by temperature but also by predators driving the dispersal of large-sized clades out of the tropics and in to the temperate zone.</p>
Fig. 1 in New records of millipedes of the order Julida (Diplopoda) from Asian Russia and adjacent regions
Fig. 1. Distribution of Julus azarovae Mikhaljova, 2009 (circle) and Leptoiulus tigirek Mikhaljova, Nefediev, Nefedieva et Dyachkov, 2015 (diamond). Previously known localities
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.