Find research datasets worth reusing

OMEN data set: Tokheim pancancer data set with genome-wide interaction network

OMEN is a Network-based Driver Gene Identification method that exploits Mutual Exclusivity. This repository stores the data used in a pancancer experiment showcasing this method. It consists of - [tokheim_pancancer_somatic_CADD.pl] A file containing CADD probabilities for gene-patient pairs (derived from the pancancer data set in Tokheim, Collin J., et al. "Evaluating the evaluation of cancer driver genes." Proceedings of the National Academy of Sciences 113.50 (2016): 14330-14335.) formatted to be used by OMEN. - [tokheim_pancancer_somatic_coverage_ranks.pl] A file containing gene coverage data (derived from the CADD data) formatted to be used by OMEN. - [network.pl] A file containing a genome-wide interaction network consisting of high quality metabolic interactions from Recon X and literature curated interactions from Intact. Recon X and Intact data was acquired from Pathway Commons version 8. The resulting network covers 7901 samples, and contains 15.694 nodes and 178.051 edges.

In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation

Background The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted).  It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset. General description / Dataset Structure Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function. The different stenosis degrees for each degree are: ScenarioI: 100 % Area fraction (no stenosis) ScenarioII: 37,5 % Area fraction ScenarioIII: 23,4 % Area fraction ScenarioIV: 6,56 % Area fraction Data fields for each file <table> <caption>headerStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>rate</td> <td>sampling rate in Hz</td> </tr> <tr> <td>description</td> <td>name of the scenario according to the paper, corresponds to filename</td> </tr> <tr> <td>configuration</td> <td>parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))</td> </tr> </tbody> </table>   <table> <caption>signalStruct</caption> <thead> <tr> <th scope="col">field</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>nodeId</td> <td>corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.</td> </tr> <tr> <td>type</td> <td>'p' ... pressure or 'q' ... flow</td> </tr> <tr> <td>data</td> <td>double array, time series of each sensor,  unit mmHg for type 'p' and ml/s for type 'q'  </td> </tr> <tr> <td>anatomicalPosition</td> <td> name of the corresponding anatomical position </td> </tr> </tbody> </table>

Full data set for "Real-Space Observation of Fluctuating Antiferromagnetic Domains" article in Science Advances

This is the full data set for the article, sufficient to reproduce any results shown. The data and data files description, and data retrieval instructions are in the Readme.txt file.

A synthetic fraud detection data set.

A synthetic fraud detection data set created using sklearn's make_blob for use in a blog. X, y = datasets.make_blobs(n_samples=[800000,200000], centers=None, cluster_std=[10.0, 2],random_state=42,n_features=4)

opencc-by-4.0Nov 2021View details →

Data sets for "Formation of an Al-rich niccolite-type silica in subducted oceanic crust: implications for water transport to the deep lower mantle"

This is the XRD and IR datasets for the article "Formation of an Al-rich niccolite-type silica in subducted oceanic crust: implications for water transport to the deep lower mantle" by Liu and Yuan et al.

opencc-by-4.0Dec 2021View details →

X-ray computed tomography aided engineering approach for non-crimp fabric reinforced composites [Data set]

The finite element models behind the publication Auenhammer, R.M., Jeppesen, N., Mikkelsen, L.P., Dahl, V.A., Blinzler, B.J., Asp, L.E.  Robust numerical analysis of fibrous composites from X-ray computed tomography image data enabling low resolutions, Composites Science and Technology, 224, 109458, <a href="https://doi.org/10.1016/j.compscitech.2022.109458">https://doi.org/10.1016/j.compscitech.2022.109458</a>, 2022.  The x-ray scan data which the model is based on can be found in the following publication: Jeppesen, N., V.A. Dahl, A.N. Christensen, A.B. Dahl, L.P. Mikkelsen, Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor. IOP Conf. Ser.: Mater. Sci. Eng. 942, 012037, <a href="https://doi.org/10.1088/1757-899X/942/1/012037">https://doi.org/10.1088/1757-899X/942/1/012037</a> 2020 and data-set Jeppesen N, Dahl V A, Christensen A N, Dahl A B and Mikkelsen L P 2020 Characterization of the fiber orientations in non-crimp glass fiber reinforced composites using structure tensor [data set] Zenodo. <a href="http://dx.doi.org/10.5281/zenodo.3877522">http://dx.doi.org/10.5281/zenodo.3877522.</a>

Bulk components and diacids and related compounds in size-segregated aerosols at Tianjin, North China – Data set

To better understand the sources of atmospheric aerosols in winter, we collected size-resolved (9-stage) aerosols at Tianjin, North China, and studied for inorganic ions, carbonaceous components, diacids and related compounds. Inorganic ions were measured using ion chromatography (ICS-5000 System, China, Dai An). Organic carbon (OC) and elemental carbon (EC) were measured using OC/EC analyzer (USA, Sunset Laboratory Inc.), based on thermal light reflectance following the IMPROVE protocol of the protective visual environment. Water-soluble organic carbon (WSOC) was measured using the total organic carbon (TOC) analyzer (model: OI, 1030W + 1088). Concentrations of diacids and related compounds were measured using a capillary gas chromatography (GC; Agilent 7890B). Peak identification was carried out with reference to authentic standards retention time measured by GC-FID and mass spectra performed using a GC-mass spectrometry (GC-MS) system. Based on the results obtained, we found that the atmospheric aerosols over the Tianjin region, North China were derived from mixed: both anthropogenic and biogenic, sources and the subsequent secondary formation/transformations were also important in winter.

opencc-by-3.0-atApr 2022View details →

Planar Graph Classifier - Set Split

Set with class information (Planar Graph Classifier - Class Information - 10.5281/zenodo.6510113) is split into training and test set for model fitting and evaluation.

FlowNet-PET Test Sets

The test sets utilized in our work on FlowNet-PET found in https://github.com/teaghan/FlowNet_PET

FlowNet-PET Training Set

The training set used for FlowNet-PET found in https://github.com/teaghan/FlowNet_PET A dataset of 300 phantoms generated using the 4D extended cardiac-torso (XCAT) anthropomorphic digital phantom (2x4x4) mm3 voxels. The dataset is split into training (270) and validation (30) sets. For each phantom, ten frames spaced across a single breath cycle were created, each frame having (108x152x152) pixels resembling the activity distribution throughout the phantom. To emulate a simple representation of PET data acquisition, these distributions were used to sample a random number (between 1x106 and 9x106) of counts per frame.   The parameters for each XCAT phantom were varied randomly. This included the gender, axial section included, transaxial shifts, scaling factors in each direction, size and location of the lung lesion, extent of the diaphragm motion, extent of the chest expansion, and the activity of each organ.

Data set for manuscript "Optimal utilization of PMTCT of HIV services among adolescents under group versus focused antenatal care"

The derived variables in the data set are labeled "DER what variables they are derived from" except the variable "optimal utilization which is a composite outcome of other variables depending on HIV status as described in the manuscript. The variables "...cat" are categorized or recoded from an original numeric/ categorical variable The variables bin are used when a "cat" variable exists but further recoding is done to form a binary variable

opencc-zeroMay 2022View details →

dryad36/100

Data from: Implementation of a pediatric telemedicine and medication delivery service in a resource-limited setting: A pilot study for clinical safety and feasibility

Objective: Determine the clinical safety and feasibility of implementing a telemedicine and medication delivery service (TMDS) to address gaps in nighttime healthcare access for children in low-resource settings. Results: A total of 391 cases were enrolled from September 9th, 2019 to January 19th, 2021; 89% (347) received a household visit. Most cases were triaged as mild or moderate (92%; 361). Among the severe cases, 83% (20) sought subsequent referred care. The most common complaint was a respiratory problem (63%; 246). At 10-days, 95% (329) of parents reported their child's condition as "improved" or "recovered". Ninety-nine percent (344) rated the TMDS as "good" or "great". The median phone consultation was 20 minutes, time to arrival at the household was 73 minutes and total workflow per case was 114 minutes. Conclusion: The TMDS was a feasible healthcare delivery model with high rates of improved clinical status at 10-days.

dryad36/100

Data set - Island area and historical geomorphological dynamics shape multifaceted diversity of barrier island floras

<div> The influence of island dynamics and characteristics on taxonomic diversity, particularly species richness, are well studied. Yet, our knowledge on the influence of island dynamics and characteristics on other facets of diversity, namely functional and phylogenetic diversity, is limited, constraining our understanding of assembly processes on islands (e.g., biogeographic history, dispersal and environmental filtering, and species interactions). Using barrier islands, a highly dynamic and so far, understudied island type, we investigate how multiple facets of vascular plant diversity (functional, phylogenetic and taxonomic diversity) are shaped by island geomorphology, modern and historic area, and habitat heterogeneity. In line with our expectation, historical dynamics in island geomorphology affected phylogenetic and taxonomic diversity via habitat heterogeneity. However, island area was the best predictor across all facets of diversity. Specifically, larger islands had higher functional and phylogenetic diversity than expected by chance while most of the smaller islands had lower diversity. The influence of area on functional diversity acted via habitat heterogeneity, with habitat heterogeneity influencing negatively functional diversity. Our results suggest that larger islands accumulate functionally and phylogenetically unique species. Further, results for functional diversity pointed towards potential area-heterogeneity trade-offs, with these trade-offs likely resulting from increased interspecific competition favoring a specific set of trait values (of stronger competitors), particularly on smaller islands. Together, these results demonstrate that going beyond taxonomic diversity contributes to identifying underlying processes shaping diversity-area relationships. </div>

opencc-zeroMay 2022View details →

Synth-Salience Choral Set

The Synth-salience Choral Set (SSCS) is a publicly available dataset for voice assignment based on pitch salience.  The dataset was created to support research on voice assignment based on pitch salience. By definition, an “ideal” pitch salience representation of a music recording is zero everywhere where there is no perceptible pitch, and has a positive value that reflects the pitches’ perceived energy at the frequency bins of the corresponding F0 values. In practice, for a normalized synthetic pitch salience function we assume a value equal to the maximum energy (salience), i. e., 1, in the time-frequency bins that correspond to the notes present in a song, and 0 elsewhere. We obtain such a synthetic pitch salience representation directly by processing the digital (MusicXML, MIDI) score of a music piece, using the desired time and frequency quantization, i. e., a time-frequency grid.  To build the SSCS, we collect scores of four-part (SATB) a cappella choral music from the <a href="https://www.cpdl.org/wiki/index.php/Main_Page">Choral Public Domain Library (CPDL)</a> using their API. We assemble a collection of 5381 scores in MusicXML format, which we subsequently convert into MIDI files for an easier parsing. Each song in the dataset comprises five CSV files: one with the polyphonic pitch salience representation of the four voices (*_mix.csv) and four additional files with the monophonic pitch salience representation of each voice separately (*_S/A/T/B.csv). In both cases, the asterisk refers to the name of the song, which is shared between all representations from the same song. Besides the pitch salience files, we provide a metadata CSV file (sscs_metadata.csv) which indicates the associated CPDL URL for each song in the dataset. Note that this dataset contains the input/output features used in the cited study, i.e., salience functions, and not audio files nor scores. However, the accompanying metadata file allows researchers to access the associated open access scores for each example in the dataset. When using this dataset for your research, please cite: Helena Cuesta and Emilia Gómez (2022). Voice Assignment in Vocal Quartets using Deep Learning Models based on Pitch Salience. Transactions of the International Society for Music Information Retrieval (TISMIR). To appear. Helena Cuesta (2022). Data-driven Pitch Content Description of Choral Singing Recordings. PhD thesis. Universitat Pompeu Fabra, Barcelona.

opencc-by-4.0Apr 2021View details →

Active optical switching evaluation in lab settings

Transfer function of a MZI with PZT actuators on top by tuning the voltage in both arms

Data Set used in "Full backward and forward dependencies through regional hypothetical extraction method"

This set of data was obtained from EUREGIO database, developed by the Tinbergen Institute, which is a set of global IO tables with regional and sectoral disaggregation. The EUREGIO database collects the productive structure and commercial relations of the WIOD in the period 2000-2010. The table is broken down into 249 administrative regions at the NUTS2 level, from 24 EU countries, 16 non-EU countries, and a block that brings together countries from the rest of the world, making a total of 266 regions. The statistical information is organised in 11 IO tables, one for each year. The data base that we provid in this repository is used in our study with the aim to determine the key regions of the Spanish economy. In order to address this objective, IO tables of smaller dimensions are built, through an aggregation and disaggregation procedure. First, the 14 industries are grouped, then the 4 sectors of final demand and, lastly, the 4 components of value added. Below, the 266 EUREGIO regions are grouped into 21 regions. Of these, 19 regions correspond to Spain <a href="#_ftn1">[1]</a>, one region includes the rest of the NUTS2 in the EU and another region covers the rest of the world. <a href="#_ftnref1">[1]</a> The 17 Spanish regions and the two autonomous cities of Ceuta and Melilla.

opencc-zeroMay 2022View details →

dryad36/100

Data set: The origin of sex differences in song in a tropical duetting wren

The study of song development has focused on temperate zone birds in which typically only males sing. In the bay wren, Cantorchilus nigricapillus, both sexes sing, performing precisely timed, female-initiated duets in which birds alternate sex-specific song phrases. We investigated the origin of these sex differences by collecting bay wren eggs and nestlings and hand-raising them in individual acoustic isolation chambers. Each bird was tutored with either monophonic or stereophonic recordings of bay wren duets, or heard no song. As adults, each tutored bird individually sang complete duets, singing both male and female song phrases. On occasion, birds learned only the male or female part of a duet to which they were exposed. However, mono-tutored birds showed no sex-specificity in these solo songs, whereas stereo-tutored birds only sang solos consistent with their sex. In addition, stereo-tutored birds acquired songs over a longer period than did mono-tutored birds. In both groups, females showed more sex-specificity during the song learning process. Finally, we observed that tutored and acoustically isolated birds invented male-like songs, whereas only males invent songs in the wild. These results reveal the relative roles played by social versus innate influences in the development of sex-specific song in this species.

Data sets compiled for review article on Gender Equity in Oceanography

Data sets compiled for a review paper on Gender Equity in Oceanography, to appear in Annual Review of Marine Science, 2023. Includes: Annual Reviews of Marine Science invited author gender statistics; China and USA oceanography career progression gender statistics; Current employment fractions of 2010-2019 physical oceanography PhDs; Gordon Research Conference Oceanography gender statistics; JGR oceans author and reviewer gender statistics; Oceanography faculty gender statistics for China; Oceanography graduate degree gender statistics for China; Oceanography magazine author gender data.

Data set for the article "Self-oscillation and Synchronisation Transitions in Elasto-Active Structures"

This is the data set for the article "Self-oscillation and Synchronisation Transitions in Elasto-Active Structures",  <table summary="Additional metadata"> <tbody> <tr> <td><a href="https://doi.org/10.48550/arXiv.2106.05721">https://doi.org/10.48550/arXiv.2106.05721</a>   </td> </tr> </tbody> </table> Is contains raw images and processed data from images used to describe the self-oscillations under study in this article. All zip files corresponds to the single chain experiment except "Double_chain_Experiment.zip".

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

General Description The evaluation set for task 5 of DCASE 2022 "Few-shot Bioacoustic Event Detection" consists of 46 audio files acquired from different bioacoustic sources.  The first 5 annotations are provided for each file, with events marked as positive (POS) for the class of interest.  This dataset is to be used for evaluation purposes during the task Folder Structure Evaluation_Set.zip     |___DC/         |____*.wav         |____*.csv     |___CT/         |____*.wav         |____*.csv     |___CHE/         |____*.wav         |____*.csv     |___MGE/         |____*.wav         |____*.csv     |___MS/         |____*.wav         |____*.csv     |___QU/         |____*.wav         |____*.csv   Evaluation_Set_5shots.zip has the same structure but contains only the *.wav files. Evaluation_Set_5shots_annotations_only.zip has the same structure but contains only the *.csv files The subfolders denote different recording sources and there may or may not be overlap between classes of interest from different wav files. Annotation structure Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows: [ Audiofilename, Starttime, Endtime, Q ] Development Set The development set for the same task can be found at: <a href="http://doi.org/10.5281/zenodo.4543504">https://doi.org/10.5281/zenodo.6012309</a> 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