Find research datasets worth reusing

1440° (4 turn) data set from cubic insulin, with slight radiation damage

X-ray diffraction data set recorded on beamline i03 at Diamond Light Source, as part of a training workshop. Data were collected from a cubic insulin crystal prepared following standard methods with an Eiger 2XE 16M detector at 250Hz.   Data Collection Parameters <table> <tbody> <tr> <td>Wavelength</td> <td>1.2399Å</td> </tr> <tr> <td>Oscillation angle</td> <td>0.1°</td> </tr> <tr> <td>Nominal resolution (inscribed circle)</td> <td>1.5</td> </tr> <tr> <td>Total rotation</td> <td>1440°</td> </tr> <tr> <td>Nominal flux</td> <td>5e11 ph/s</td> </tr> <tr> <td>Beam size</td> <td>80x20µm</td> </tr> </tbody> </table>   Merging stats from xia2 / DIALS automated online processing <pre>For mx30951v8/xins162/SAD Overall Low High High resolution limit 1.32 3.58 1.32 Low resolution limit 54.95 55.00 1.34 Completeness 98.7 100.0 83.3 Multiplicity 132.9 152.8 33.7 I/sigma 41.7 200.6 0.4 Rmerge(I) 0.072 0.044 4.027 Rmerge(I+/-) 0.071 0.043 3.977 Rmeas(I) 0.072 0.044 4.088 Rmeas(I+/-) 0.071 0.044 4.094 Rpim(I) 0.006 0.004 0.675 Rpim(I+/-) 0.008 0.005 0.940 CC half 1.000 1.000 0.360 Wilson B factor 19.890 Anomalous completeness 98.4 100.0 79.3 Anomalous multiplicity 69.1 84.3 17.7 Anomalous correlation 0.760 0.646 -0.050 Anomalous slope 0.613 dF/F 0.026 dI/s(dI) 1.013 Total observations 2426172 151841 26018 Total unique 18257 994 772 Assuming spacegroup: I 2 3 Unit cell (with estimated std devs): 77.71580(7) 77.71580(7) 77.71580(7) 90.0 90.0 90.0 </pre>   Data are made available for any purpose including methods developers trying to optimize their software for higher multiplicity data sets.

Settings and data files from Silva et al (Scientific Reports 2022)

Neolithic Greece mtDNA sequences The 47 mitochondrial sequences generated for this work are available in the .arp format for the program ARLEQUIN (http://cmpg.unibe.ch/software/arlequin35/). Simulated Data and Simulation Program This dataset permits to simulate the scenarios investigated in the article submitted by Silva et al, using the modified version of the program SPLATCHE2 provided here (http://www.splatche.com). There is a zipped folder Silva_et_al_SimulationSettings" that contains: i) SPLATCHE2 executable called "SPLATCHE2-VariableAdmixture". ii) a folder "DanubeRouteExpansion" including the settings used for the simulation of the four scenarios of the Neolithic expansion along the Danubian route. iii) a folder "GreeceContinuity" including the settings used to perform the structured population continuity test. A "ReadMe.txt" file is available in both settings folders with the instructions to launch the simulations.

Impact of water on the solubility of Si in Fe alloys_data sets

Original data for the manuscript "Possible formation of hydrogen-rich layer in the topmost outer core by deeply subducted water". All relevant data in the manuscript and supplementary information will be uploaded. Codes in the manuscript are available at https://zenodo.org/record/6383354#.Yj3lZedBwuX.

Data from 'Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience'

Aquatic deoxygenation has been flagged as an overlooked but key factor driving mass bleaching-induced coral mortality as oxygen supplies lower to concentrations that can elicit an aerobic metabolic crisis i.e., hypoxia. Surprisingly little is known of the fundamental hypoxia responsive gene set inventory corals possess to respond to deoxygenation. It is unclear whether variation in gene copy number across species exist that potentially affect gene expression with subsequent differences in the effectiveness of a given stress response. Here, we used an ortholog-based meta-analysis to investigate how hypoxia gene inventories differed amongst coral species to assess putative copy number variation (CNV) across 24 coral protein sets from species with a sequenced genome that span corals from the robust and complex clade. We found approximately a third of the investigated genes exhibited copy number differences, and these differences were species-specific rather than the robust-complex split. Zipped folders of OrthoFinder results: 'Results_Feb16' contains results including all 24 coral species from 7 genera (Acropora, Pocillopora, Stylophora, Montastrea, Montipora, Obricella, Porites). 'gene_sets_acropora_acuminata_only' contains results including just one species per genera with Acropora acuminata. 'gene_sets_acropora_cytherea_only' contains results including just one species per genera with Acropora cytherea. 'gene_sets_acropora_digitifera_only' contains results including just one species per genera with Acropora digitifera.   Results and Interpretations from these analyses are published open access here: <a href="https://doi.org/10.3389/fmars.2022.834332">https://doi.org/10.3389/fmars.2022.834332</a> Full citation: Alderdice R, Hume BCC, Kühl M, Pernice M, Suggett DJ, Voolstra CR. Disparate inventories of hypoxia gene sets across corals align with inferred environmental resilience. Front Mar Sci. 2022;9. doi:10.3389/fmars.2022.834332 Scripts are available here: <a href="https://zenodo.org/record/6396671#.YoYpoS8RoZg">https://github.com/didillysquat/alderdice_2021</a>

MTG Price Set

Dataset containing core and expansion sets for MTG pulled from Scryfall on 2022-04-11. It includes a summary of each card and the price in EUR, USD and TIX.

opencc-by-4.0Jun 2019View details →

ISC19 IO500 Data Sets

Full, 10 node, and standard IO500 lists

openmit-licenseApr 2022View details →

Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?

Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?"

opencc-by-4.0Feb 2022View details →

Data set from long-term wind and acceleration monitoring of the Gjemnessund Bridge

The Gjemnessund Bridge has been monitored by accelerometers and anemometers for almost ten years. The data collected between 2013 and 2018 are now available in this open-access research entry, for free access and download. The data is collected in two h5-files (hierachical data format), with sampling rates 2 Hz and 10 Hz, downsampled from the raw sampling rate of 200 Hz. Some minimal signal processing is applied to the data in line with that applied to the Hardanger Bridge data described in Fenerci et al. (2021) and the Bergsøysund Bridge data described in Kvåle et al. (2022). The structure of the data is identical to that of the latter reference, which is described in a preprint appended to that research entry. The Python package opyndata available on GitHub contains useful tools compatible with the format of the dataset, for data import, processing and visualization.

DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Development Set

General Description: The development set for task 5 of DCASE 2022 "Few-shot Bioacoustic Event Detection" consists of 192 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets.  Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unkwown (UNK) values for each class. UNK indicates uncertainty about a class.  Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest.  this version (3): * fixes issues with annotations from HB set   Folder Structure: Development_Set.zip |_Development_Set/     |__Training_Set/         |___JD/             |____*.wav             |____*.csv         |___HT/             |____*.wav             |____*.csv         |___BV/             |____*.wav             |____*.csv         |___MT/             |____*.wav             |____*.csv         |___WMW/             |____*.wav             |____*.csv       |__Validation_Set/         |___HB/             |____*.wav             |____*.csv         |___PB/             |____*.wav             |____*.csv         |___ME/             |____*.wav             |____*.csv   Development_Set_Annotations.zip has the same structure but contains only the *.csv files   ## Dataset statistics Some statistics on this dataset are as follows, split between training and validation set and their sub-folders: ----------------------------------------------------- TRAINING SET ----------------------------------------------------- Number of audio recordings        |    174 Total duration                    |    21 hours Total classes                    |    47 Total events                    |    14229 ----------------------------------------------------- TRAINING SET/BV ----------------------------------------------------- Number of audio recordings        |    5 Total duration                    |    10 hours Total classes                     |    11 Total events                     |    9026 Ratio event/duration            |    0.04 Sampling rate                    |    24000 Hz ----------------------------------------------------- TRAINING SET/HT ----------------------------------------------------- Number of audio recordings        |    5 Total duration                    |    5 hours Total classes                     |    5 Total events                     |    611 Ratio event/duration            |    0.05 Sampling rate                    |    6000 Hz ----------------------------------------------------- TRAINING SET/JD ----------------------------------------------------- Number of audio recordings        |    1 Total duration                    |    10 mins Total classes                    |    1 Total events                    |    357 Ratio event/duration            |    0.06 Sampling rate                    |    22050 Hz ----------------------------------------------------- TRAINING SET/MT ----------------------------------------------------- Number of audio recordings        |    2 Total duration                    |    1 hour and 10 mins Total classes                    |    4 Total events                    |    1294 Ratio event/duration            |    0.04 Sampling rate                    |    8000 Hz ----------------------------------------------------- TRAINING SET/WMW ----------------------------------------------------- Number of audio recordings        |    161 Total duration                    |    4 hours and 40 mins Total classes                    |    26 Total events                    |    2941 Ratio event/duration            |    0.24 Sampling rate                    |    various sampling rates ----------------------------------------------------- ----------------------------------------------------- VALIDATION SET ----------------------------------------------------- Number of audio recordings        |    18 Total duration                    |    5 hours and 57 minutes Total classes                    |    5 Total events                     |    1077 ----------------------------------------------------- VALIDATION SET/HB ----------------------------------------------------- Number of audio recordings        |    10 Total duration                    |    2 hours and 38 minutes Total classes                     |    1 Total events                     |    712 Ratio event/duration            |    0.7 Sampling rate                    |    44100 Hz ----------------------------------------------------- VALIDATION SET/PB ----------------------------------------------------- Number of audio recordings        |    6 Total duration                    |    3 hours Total classes                    |    2 Total events                     |    292 Ratio event/duration            |    0.003 Sampling rate                    |    44100 Hz ----------------------------------------------------- VALIDATION SET/ME ----------------------------------------------------- Number of audio recordings        |    2 Total duration                    |    20 minutes Total classes                    |    2 Total events                     |    73 Ratio event/duration            |    0.01 Sampling rate                    |    44100 Hz -----------------------------------------------------   Annotation structure Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows: TRAINING SET --------------------- Audiofilename, Starttime, Endtime, CLASS_1, CLASS_2, ...CLASS_N VALIDATION SET --------------------- Audiofilename, Starttime, Endtime, Q   Classes DCASE2022_task5_training_set_classes.csv and DCASE2022_task5_validation_set_classes.csv provide a table with class code correspondence to class name for all classes in the Development set. DCASE2022_task5_training_set_classes.csv --------------------- dataset, class_code, class_name DCASE2022_task5_validation_set_classes.csv --------------------- dataset, recording, class_code, class_name   Evaluation Set The Evaluation set for this task will be released on the 1st of June 2022 Open Access: This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.   Contact info: Please send any feedback or questions to: Ines Nolasco -  i.dealmeidanolasco@qmul.ac.uk

openogl-uk-3.0Apr 2022View details →

PRIMAVERA European winter windstorm event set

PRIMAVERA was a European Union Horizon 2020 project whose primary aim was to generate advanced and well-evaluated high-resolution global climate model datasets, for the benefit of governments, business and society in general.  Following consultation with members of the insurance industry, we have used a PRIMAVERA multi-model ensemble to generate a European winter windstorm event set for use in insurance risk analysis, containing approximately 1300 years of windstorm data. The uploaded dataset contains the model and re-analysis windstorm footprints in netcdf format and documentation of the data.  Further information is given in Lockwood et al., Using high-resolution global climate models from the PRIMAVERA project to create a European winter windstorm event set, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-12, in review, 2022. Any products or applications which use this dataset must state the following attribution “Acknowledgment to the PRIMAVERA partners. The information/material contained has been produced with funding from the European Union’s Horizon 2020 Research & Innovation Programme under grant agreement no. 641727.”

dryad40/100

Data from: Let's stick together: infection enhances preferences for social settings in a songbird species

Acute infections can alter foraging and movement behaviours relevant to sociality and pathogen spread. However, few studies have examined how infection with directly-transmitted pathogens influences host social preferences. Juvenile house finches are gregarious and particularly susceptible to infection by the bacterial pathogen Mycoplasma gallisepticum (MG). Changes in sociality during infection are likely to have important consequences for MG transmission throughout majority-juvenile flocks, but it remains unknown how infection influences sociality in house finches. To test this, we inoculated 33 wild-caught juvenile house finches with MG or media (sham control). At peak infection, birds were given a choice assay to assess preference for associating near a flock versus an empty cage. Infected birds were significantly more likely than controls to spend time near the flock while eating, and marginally so while perching. These results indicate augmented social preferences during infection, potentially as a form of behavioural tolerance. Notably, infected birds showed strong social preferences regardless of individual variation in disease severity or pathogen loads, with 14/19 harbouring high loads (log10 5-6) at time of assay. Overall, our results show that infection with a directly-transmitted pathogen can augment social preferences, with potential implications for MG spread in natural populations.

opencc-zeroApr 2022View details →

opencc-by-nc-sa-4.0Apr 2022View details →

DEBS 2022 Grand Challenge Data Set: Trading Data

The data provided here as part of the DEBS 2022 Grand Challenge is based on real tick data captured by Infront Financial Technology GmbH for the complete week of November 8th to 14th, 2021 (i.e., five trading days Monday to Friday + Saturday and Sunday). The data set contains 289 million tick data events covering 5504 equities and indices that are traded on three European exchanges: Paris (FR), Amsterdam (NL), and Frankfurt (ETR).  Some event notifications appear to come with no payload. This is due to the fact that the 2022 GC requires only a small subset of attributes to be evaluated; other attributes have been eliminated from the data set to minimize its overall size while keeping the amount of events to process unchanged. Further details on the data set, its syntax and its semantics can be found in the official DEBS 2022 Grand Challenge paper as part of the DEBS 2022 conference proceedings (please use this for citation):  Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer, and Hans-Arno Jacobsen. 2022. The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data. In The 16th ACM International Conference on Distributed and Event-based Systems (DEBS ’22), June 27-June 30, 2022, Copenhagen. ACM, New York, NY, USA. All files of the DEBS 2022 Grand Challenge Data Set “Trading Data” are provided as-is. By downloading and using this data you agree to the terms and conditions of the licensing agreement (CC by-nc-sa).

Thetis Baltic Sea simulation: model and observation data sets

Model and observation data sets used in article "Adjoint-based optimization of a regional water elevation model".

Data set for: Toggle-like current-induced Bloch point dynamics of 3D skyrmion strings in a room-temperature nanowire

This data set contains both the experimental data and the simulation scripts to reproduce the results of [1]: Toggle-like current-induced Bloch point dynamics of 3D skyrmion strings in a room-temperature nanowire by M. T. Birch, D. Cortés-Ortuño, K. Litzius, S. Wintz, F. Schulz, M. Weigand, A. &Scaron;tefančič, D. Mayoh, G. Balakrishnan, P.D. Hatton, G. Schütz. A preprint of this publication is available at <a href="https://www.researchsquare.com/article/rs-1235546/v1">https://www.researchsquare.com/article/rs-1235546/v1</a>. This data set is also hosted in Github: <a href="https://github.com/davidcortesortuno/paper-2022_toggle-like_current_induced_bp_dynamics_3d_skyrmion_strings">https://github.com/davidcortesortuno/paper-2022_toggle-like_current_induced_bp_dynamics_3d_skyrmion_strings</a> If you find this material useful please cite us <pre><code>@Misc{Birch2022, author = {M. T. Birch and D. Cort\'es-Ortu\~no}, title = {{Data set for: Toggle-like current-induced Bloch point dynamics of 3D skyrmion strings in a room-temperature nanowire}}, howpublished = {Zenodo \url{doi:10.5281/zenodo.6393340}. Github: \url{https://github.com/davidcortesortuno/paper-2022_toggle-like_current_induced_bp_dynamics_3d_skyrmion_strings}}, year = {2022}, doi = {10.5281/zenodo.6393340}, url = {https://doi.org/10.5281/zenodo.6393340}, }</code></pre>

opencc-by-4.0Oct 2019View details →

Fishyscapes Lost & Found validation set

Validation Set of 100 images for the Fishyscapes Benchmark Lost & Found dataset. The download contains novel annotations for the existing Lost & Found dataset.

Data set: On the porosity-dependent permeability and conductivity of triply periodic minimal surface based porous media

This file contains all processed data from the simulations and calculations.

opencc-by-4.0May 2022View details →

Data set of Reversing anterior insular cortex neuronal hypoexcitability attenuates compulsive behavior in juvenile rats

Development of self-regulatory competencies during adolescence is partially dependent on normative brain maturation. Here we report that adolescent rats as compared to adults exhibit impulsive and compulsive-like behavioral traits, the latter being associated with lower expression of mRNA levels of the immediate early gene zif268 in the anterior insula cortex (AIC). This suggests that underdeveloped AIC function in adolescent rats could contribute to an immature pattern of interoceptive cue integration in decision-making and a compulsive phenotype.  In support of this, we report that layer 5 pyramidal neurons in the adolescent rat AIC are hypoexcitable and receive fewer glutamatergic synaptic inputs compared to adults. Chemogenetic activation of the AIC attenuated compulsive traits in adolescent rats supporting the idea that in early stages of AIC maturity there exists a suboptimal integration of sensory and cognitive information that contributes to inflexible behaviors in specific conditions of reward availability.

opencc-by-4.0Oct 2015View details →

Uruguayan candombe drumming - beat and downbeat tracking data set

Uruguayan Candombe drumming - beat and downbeat data set =========== Description =========== This dataset includes more than 2 hours of Candombe recordings, with annotated beats and downbeats. It features 35 complete performances by renowned players, playing in groups of three to five drums. A total of 26 tambor players from various generations took part, representing the three key traditional Candombe styles. These recordings were created in a studio setting over a period of more than two decades as part of musicological research. The sessions from 1992 and 1995 were produced by Luis Jure, and the session from 2014 was produced by Luis Jure and Martín Rocamora. The audio files are in stereo with a sampling rate of 44.1 kHz and 16-bit precision. An expert annotated the location of beats and downbeats, totaling more than 4700 downbeats. The audio is provided in flac format and the annotations are in .csv files. The values in the first column of the CSV file represent the time instants of the beats. The numbers in the second column indicate both the bar number and the beat number within the bar. For example, 1.1, 1.2, 1.3, and 1.4 represent the four beats of the first bar. Therefore, each label ending in .1 indicates a downbeat. Another set of annotations is provided as .beats files in which the bar numbers are removed. ======== Citation ======== This dataset was released with the publication of the following paper. So if you find the dataset useful and want to reference it in your publications, please cite it. "Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming”. Leonardo Nunes, Martín Rocamora, Luis Jure, Luiz W. P. Biscainho. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), pages 264-270, Málaga, Spain, 26-30 October, 2015. @inproceedings{Nunes2015,     author = {Leonardo Nunes and Martín Rocamora and Luis Jure and Luiz W. P. Biscainho},     title = {{Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming}},     booktitle = {Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015)},     month = {Oct.},     address = {Málaga, Spain},     pages = {264--270},     year = {2015} } ======== Candombe ======== Candombe is a vital part of Uruguayan popular culture, with thousands of practitioners and its rhythm influencing various genres of popular music. In 2009, UNESCO recognized it as part of the Intangible Cultural Heritage of Humanity. While it originated in Uruguay, Candombe has its roots in the culture brought by African slaves in the 18th century. Over time, it has evolved to incorporate the descendants of European immigrants and has become a part of the entire society. Candombe drumming, with its unique rhythm, is the essential element of this tradition, which also includes dancing, symbolic characters, and costumes. The drum used in Candombe is called tambor, which is Spanish for "drum." There are three different sizes: chico (small), repique (medium), and piano (big). Each size has its own unique sound, ranging from high to low frequency, and its own specific rhythmic patterns. All three drums are played with a stick in the dominant hand and the other one hitting the skin directly. The stick is also used to hit the shell when playing the clave or madera pattern. A minimal ensemble of drums (cuerda de tambores) must have at least one of each of the three drums. During a llamada de tambores, the ensemble usually consists of around 20 to 60 drums. When marching, the players walk forward with short steps synchronized with the beat, which is important for embodying the rhythm, even though it is not audible. =============== Acknowledgments =============== This work was partially supported by the funding agency Comisión Sectorial de Investigación Científica, Universidad de la República, Uruguay, and by the Department of Culture of the Municipality of Montevideo. This is the complete list of performers, in alphabetical order: Mariano Barroso, Eduardo 'Cacho' Giménez, Eduardo 'Malumba' Giménez, Francisco Giménez, José Luis Giménez, Jorge 'Foqué' Gómez, José Pedro 'Perico' Gularte, Luis 'Pocholo' Maciel, Julio Magariños, Raúl 'Neno' Magariños, Marcelo Magariños, Javier 'Cerdo' Martirena, Wilson Martirena, Eduardo 'Tierra' Nilo, Sergio Ortuño, Fernando 'Lobo' Núñez, Edinson 'Palo' Oviedo, Gustavo Oviedo, Egdardo Pintos, Luis 'Mocambo' Quiroz, Rodolfo 'Pelado' Rodríguez, Fernando 'Hurón' Silva, Juan Silva, Raúl Silva, Waldemar 'Cachila' Silva, and Héctor Manuel Suárez.

MItosis DOmain Generalization Challenge 2022 (MICCAI MIDOG 2022), Training data set (PNG version)

This is the training dataset of the MItosis DOmain Generalization (MIDOG) challenge 2022, held in conjunction with MICCAI 2022. Please find the structured challenge description at 10.5281/zenodo.6362337. The training set consists of 405 tumor cases in total across six tumor types: <ul> <li>Canine Lung Cancer (44 cases, scanned with 3DHistech Pannoramic Scan II)</li> <li>Human Breast Cancer (150 cases, scanned using three scanners, part of MIDOG2021 dataset)</li> <li>Canine Lymphoma (55 cases, scanned with 3DHistech Pannoramic Scan II)</li> <li>Human neuroendocrine tumor (55 cases, scanned with Hamamatsu NanoZoomer XR)</li> <li>Canine Cutaneous Mast Cell Tumor (50 cases, scanned with Aperio ScanScope CS2)</li> <li>Human melanoma (51 cases, scanned with Hamamatsu NanoZoomer XR) (no labels provided)</li> </ul> From each WSI, a trained pathologist selected an area of 2mm² corresponding to approximately 10 high power fields, according to the grading scheme of Elston and Ellis. We cropped this area and provide it as PNG files in this data set due to restrictions in data set size on zenodo. Each file includes the resolution (in dots per inch, DPI) of the original scanned images. The training set contains 9501 mitotic figures (MF) and 11051 hard examples (non-mitotic figures). All annotations are provided in MS COCO JSON format and as SQLITE database (SlideRunner format).