Find research datasets worth reusing

MetaPhyler 1.25 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63

Software: MetaPhyler SoftwareVersion: 1.25 DataURL: https://data.cami-challenge.org/participate SoftwareURL: http://metaphyler.cbcb.umd.edu/ DockerImage: stefanjanssen/docker_profiling_tools:metaphyler IsBiobox: True BioboxYAMLFile: https://zenodo.org/record/3629567/files/biobox.yaml?download=1 ReferenceDatabase: 2012 ShortReadsUsed: True LongReadsUsed: False CommandsUsed: docker run \ --volume="/path/to/19122017_mousegut_scaffolds_yaml:/bbx/mnt/yaml:ro" \ --volume="/path/to/19122017_mousegut_scaffolds:/bbx/mnt/input:ro" \ --volume="/path/to/output:/bbx/mnt/output:rw" \ --volume="/path/to/output/metadata:/bbx/metadata:rw" \ --volume="/path/to/output/cache:/cache:rw" \ stefanjanssen/docker_profiling_tools:metaphyler

DIAMOND 0.9.24 taxonomic binning of the CAMI 2 Mouse Gut Toy data set, gold standard pooled assembly

Taxonomic binning of the gold standard pooled assembly Software: DIAMOND SoftwareVersion: 0.9.24 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://github.com/bbuchfink/diamond ReferenceDatabase: NCBI nr 2019-04-17 Taxonomy: NCBI 2019-04-17 ShortReadsUsed: False LongReadsUsed: False CommandUsed: diamond makedb --in nr.faa.gz --db nr --taxonmap prot.accession2taxid.gz --taxonnodes nodes.dmp diamond blastx --query anonymous_gsa_pooled.fasta --db nr --outfmt 102 --out 19122017_mousegut_scaffolds.out --block-size 40 --index-chunks 1 --tmpdir tmp

mOTUs 2.5.1 taxonomic profiling of the CAMI 2 Mouse Gut Toy data set, samples 0-63

Software: mOTUs SoftwareVersion: 2.5.1 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://motu-tool.org/ DockerImage: cami/motus:2.5.1 IsBiobox: False ReferenceDatabase: mOTUs database version 2.5.0 ShortReadsUsed: True LongReadsUsed: False CommandsUsed: for i in {0..63}; do motus profile -f sample_$((i))/reads/anonymous_reads_r1.fq -r sample_$((i))/reads/anonymous_reads_r2.fq -n $((i)) -C precision > sample$((i)).profile ; done cat sample*.profile > cami2_mouse_gut_motus2.5.1.profile

MaxBin 2.2.7 genome binning of the CAMI 2 Mouse Gut Toy data set, samples 0-63, gold standard pooled assembly

Genome binning of the gold standard pooled assembly Software: MaxBin SoftwareVersion: 2.2.7 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://sourceforge.net/projects/maxbin/ DockerImage: cami/maxbin:2.2.7 IsBiobox: No ShortReadsUsed: True LongReadsUsed: False CommandUsed: run_MaxBin.pl -thread 16 -contig anonymous_gsa_pooled.fasta -out output -reads sample_0/reads/anonymous_reads.fq -reads2 sample_1/reads/anonymous_reads.fq -reads3 sample_2/reads/anonymous_reads.fq -reads4 sample_3/reads/anonymous_reads.fq -reads5 sample_4/reads/anonymous_reads.fq -reads6 sample_5/reads/anonymous_reads.fq -reads7 sample_6/reads/anonymous_reads.fq -reads8 sample_7/reads/anonymous_reads.fq -reads9 sample_8/reads/anonymous_reads.fq -reads10 sample_9/reads/anonymous_reads.fq -reads11 sample_10/reads/anonymous_reads.fq -reads12 sample_11/reads/anonymous_reads.fq -reads13 sample_12/reads/anonymous_reads.fq -reads14 sample_13/reads/anonymous_reads.fq -reads15 sample_14/reads/anonymous_reads.fq -reads16 sample_15/reads/anonymous_reads.fq -reads17 sample_16/reads/anonymous_reads.fq -reads18 sample_17/reads/anonymous_reads.fq -reads19 sample_18/reads/anonymous_reads.fq -reads20 sample_19/reads/anonymous_reads.fq -reads21 sample_20/reads/anonymous_reads.fq -reads22 sample_21/reads/anonymous_reads.fq -reads23 sample_22/reads/anonymous_reads.fq -reads24 sample_23/reads/anonymous_reads.fq -reads25 sample_24/reads/anonymous_reads.fq -reads26 sample_25/reads/anonymous_reads.fq -reads27 sample_26/reads/anonymous_reads.fq -reads28 sample_27/reads/anonymous_reads.fq -reads29 sample_28/reads/anonymous_reads.fq -reads30 sample_29/reads/anonymous_reads.fq -reads31 sample_30/reads/anonymous_reads.fq -reads32 sample_31/reads/anonymous_reads.fq -reads33 sample_32/reads/anonymous_reads.fq -reads34 sample_33/reads/anonymous_reads.fq -reads35 sample_34/reads/anonymous_reads.fq -reads36 sample_35/reads/anonymous_reads.fq -reads37 sample_36/reads/anonymous_reads.fq -reads38 sample_37/reads/anonymous_reads.fq -reads39 sample_38/reads/anonymous_reads.fq -reads40 sample_39/reads/anonymous_reads.fq -reads41 sample_40/reads/anonymous_reads.fq -reads42 sample_41/reads/anonymous_reads.fq -reads43 sample_42/reads/anonymous_reads.fq -reads44 sample_43/reads/anonymous_reads.fq -reads45 sample_44/reads/anonymous_reads.fq -reads46 sample_45/reads/anonymous_reads.fq -reads47 sample_46/reads/anonymous_reads.fq -reads48 sample_47/reads/anonymous_reads.fq -reads49 sample_48/reads/anonymous_reads.fq -reads50 sample_49/reads/anonymous_reads.fq -reads51 sample_50/reads/anonymous_reads.fq -reads52 sample_51/reads/anonymous_reads.fq -reads53 sample_52/reads/anonymous_reads.fq -reads54 sample_53/reads/anonymous_reads.fq -reads55 sample_54/reads/anonymous_reads.fq -reads56 sample_55/reads/anonymous_reads.fq -reads57 sample_56/reads/anonymous_reads.fq -reads58 sample_57/reads/anonymous_reads.fq -reads59 sample_58/reads/anonymous_reads.fq -reads60 sample_59/reads/anonymous_reads.fq -reads61 sample_60/reads/anonymous_reads.fq -reads62 sample_61/reads/anonymous_reads.fq -reads63 sample_62/reads/anonymous_reads.fq -reads64 sample_63/reads/anonymous_reads.fq

InterFlex WP3 data set for scalability and replicability analysis of InterFlex demonstrators

This set of data includes the data for the scalability and replicability task of InterFlex project under GA 731289 for the functional (grid operation) and ICT conducted analyses of the demonstrated use cases. The results and the detailed report can be found in deliverable D3.8

LogChunks: A Data Set for Build Log Analysis

We collected 797 Travis CI logs from a wide range of 80 GitHub repositories from 29 different main development languages. You can find our collection tool in `log-collection` and the logs sorted by language and repository in `logs`. We manually labeled the part (chunk) of the log describing why the build failed.In addition, the chunks are annotated with keywords that we would use to search for them and categorized according to their structural representation within the log. You can find this data in an xml-file for each repository in `build-failure-reason`.

opencc-by-4.0Feb 2020View details →

MEGAHIT 1.0.3, 1.1.3, 1.2.9 assembly of the CAMI 2 Mouse Gut Toy data set, samples 0-9, short reads

Assembly of the first 10 short read samples with different MEGAHIT versions and parameters Software: MEGAHIT SoftwareVersion: 1.0.3, 1.1.3, 1.2.9 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://github.com/voutcn/megahit ShortReadsUsed: True LongReadsUsed: False CommandUsed: # install bioconda and run: conda create -n megahit103 megahit=1.0.3 conda create -n megahit113 megahit=1.1.3 conda create -n megahit129 megahit=1.2.9 Sample0=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_0/reads/anonymous_reads.fq.gz Sample1=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_1/reads/anonymous_reads.fq.gz Sample2=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_2/reads/anonymous_reads.fq.gz Sample3=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_3/reads/anonymous_reads.fq.gz Sample4=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_4/reads/anonymous_reads.fq.gz Sample5=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_5/reads/anonymous_reads.fq.gz Sample6=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_6/reads/anonymous_reads.fq.gz Sample7=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_7/reads/anonymous_reads.fq.gz Sample8=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_8/reads/anonymous_reads.fq.gz Sample9=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_9/reads/anonymous_reads.fq.gz conda activate megahit103 /usr/bin/time -v megahit -t 48 \ --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \ -o megahit103-Sample0-9-sLibs-default conda activate megahit113 /usr/bin/time -v megahit -t 48 \ --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \ -o megahit113-Sample0-9-sLibs-default /usr/bin/time -v megahit -t 48 --presets meta-sensitive \ --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \ -o megahit113-Sample0-9-sLibs-meta-sensitive /usr/bin/time -v megahit -t 48 --presets meta-large \ --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \ -o megahit113-Sample0-9-sLibs-meta-large conda activate megahit129 /usr/bin/time -v megahit -t 48 \ --12 $Sample0 --12 $Sample1 --12 $Sample2 --12 $Sample3 --12 $Sample4 --12 $Sample5 --12 $Sample6 --12 $Sample7 --12 $Sample8 --12 $Sample9 \ -o megahit129-Sample0-9-sLibs-default

opencc-by-4.0Feb 2020View details →

metaSPAdes 3.13.0 assembly of the CAMI 2 Mouse Gut Toy data set, samples 0-9, short reads

Assembly of the first 10 short read samples Software: metaSPAdes SoftwareVersion: 3.13.0 DataURL: https://data.cami-challenge.org/participate SoftwareURL: https://github.com/ablab/spades ShortReadsUsed: True LongReadsUsed: False CommandUsed: conda create -n spades3130 spades=3.13.0-0 Sample0=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_0/reads/anonymous_reads.fq.gz Sample1=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_1/reads/anonymous_reads.fq.gz Sample2=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_2/reads/anonymous_reads.fq.gz Sample3=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_3/reads/anonymous_reads.fq.gz Sample4=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_4/reads/anonymous_reads.fq.gz Sample5=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_5/reads/anonymous_reads.fq.gz Sample6=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_6/reads/anonymous_reads.fq.gz Sample7=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_7/reads/anonymous_reads.fq.gz Sample8=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_8/reads/anonymous_reads.fq.gz Sample9=/path/to/19122017_mousegut_scaffolds/2017.12.29_11.37.26_sample_9/reads/anonymous_reads.fq.gz cat $Sample0 $Sample1 $Sample2 $Sample3 $Sample4 $Sample5 $Sample6 $Sample7 $Sample8 $Sample9 > Samples0-9_anonymous_reads.fq.gz conda activate spades3130 /usr/bin/time -v metaspades.py --12 Samples0-9_anonymous_reads.fq.gz -o metaSPAdes3130-Sample0-9

opencc-by-4.0Feb 2020View details →

MLCQ: Industry-relevant code smell data set

The MLCQ data set with nearly 15000 code samples was created by software developers with professional experience who reviewed industry-relevant, contemporary Java open source projects.  We expect that this data set should stay relevant for a longer time than data sets that base on code released years ago and, additionally, will enable researchers to investigate the relationship between developers' background and code smells' perception. If you use this data set please cite the following paper: Lech Madeyski and Tomasz Lewowski. MLCQ: Industry-relevant code smell data set. In Evaluation and Assessment in Software Engineering (EASE2020), April 15–17, 2020, Trondheim, Norway.ACM, New York, NY, USA, 6 pages, DOI: <a href="https://doi.org/10.1145/3383219.3383264">3383219.3383264</a> URL: https://doi.org/10.1145/3383219.3383264 Note: Pre-print should be available soon from <a href="http://madeyski.e-informatyka.pl">http://madeyski.e-informatyka.pl</a>

Data set from Eriksson-Helmert, ICAPS 2020

This data set contains the raw experiment data used in the ICAPS 2020 Paper "Certified Unsolvability for SAT Planning with Property Directed Reachability". It contains 3 directories, one for each of the certified version of planning algorithms "DFS-CL" "fast-downward" and "PDR". In each directory, there is an "*-eval" directory containing a json file called "properties" which summarizes all parsed results, as well as an html file presenting an overview of these results. Finally, the main directory contains a file "make_plots.py", which generates a tex file for each plot presented in the paper.

Videos of computer vision based recognition/segmentation/classification of materials inside vessels in chemistry lab and other setting

These videos contain materials in vessels in various settings related to the chemistry lab, medical samples and handling liquids in everyday life settings. The region and type of each vessel and material phase found by the computer vision (neural net) are marked in purple, the class of each phase appears above each panel in green (each panel corresponds to a different class). The work is part of the computer vision for the chemistry lab project. For details on the project see: <a href="https://chemrxiv.org/articles/Computer_Vision_for_Recognition_of_Materials_and_Vessels_in_Chemistry_Lab_Settings_and_the_Vector-LabPics_Dataset/11930004">https://chemrxiv.org/articles/Computer_Vision_for_Recognition_of_Materials_and_Vessels_in_Chemistry_Lab_Settings_and_the_Vector-LabPics_Dataset/11930004</a> For Details on the videos See: <a href="https://www.youtube.com/watch?v=K7I2QJcIyBQ&list=PLRiTwBVzSM3B6MirlFl6fW0YQR4TtQmtJ">https://www.youtube.com/watch?v=K7I2QJcIyBQ&list=PLRiTwBVzSM3B6MirlFl6fW0YQR4TtQmtJ</a> Basically, the videos contain process such as pouring, mixing, foam formation precipitation, phase separation,  melting freezing, dissolving, etc.., where the region of each vessel and material phase and their type (liquid, solid, powder, suspension, foam) is found by the neural net and marked purple. For code see: <a href="https://github.com/aspuru-guzik-group/Computer-vision-for-the-chemistry-lab">https://github.com/aspuru-guzik-group/Computer-vision-for-the-chemistry-lab</a>

Data Set from the Systematization of Vulnerability Discovery Metrics

The data set contains vulnerability discovery metric data extracted from 26 primary studies identified as part of the systematic literature review conducted. The review was conducted as part achieving our overall research vision to assist software engineers in building secure software by providing a technique that generates scientific, interpretable, and actionable feedback on security as the software evolves.

opencc-by-4.0Sep 2019View details →

Data set for sub-millimetre MRI tissue class segmentation

Sub-millimetre 7Tesla MRI image data set of the human brain for supervised training of algorithms to perform tissue class segmentation. The dataset contains preprocessed MRI images (co-registered + bias corrected) and corresponding ground truth labels. The dataset contains two different acquisitions: - MPRAGE dataset, based on 5 subjects, with T1w, PDw and T2w images - MP2RAGE dataset, based on 4 subjects, with inv1, inv2 and me gre images   The following ground truth labels are provided: [1] white matter [2] grey matter [3] cerebrospinal fluid [4] ventricles [5] subcortical [6] vessels [7] sagittal sinus Images are saved as nifti files and organized in BIDS format. This dataset is an extension of the following, initial dataset publication: * Dataset: A scalable method to improve gray matter segmentation at ultra high field MRI. The initial dataset is also available as a zenodo repository and can be downloaded from: https://zenodo.org/record/1206163

Data set: Resource footprints, quality of life and economic development

This data set accompanies the publication "Towards a comprehensive framework of the relationships between resource footprints, quality of life and economic development" by Stefan Cibulka and Stefan Giljum from the Institute for Ecological Economics at the Vienna University of Economics and Business (WU). The file provides data on resource footprints (carbon footprint, material footprint), Human Development Index, Happiness Index as well as GDP for countries world-wide in the time span from 1990 to 2015. In addition, it provides all specifications of the regression analyses undertaken in the course of this study and the detailed regression results.

opennotspecifiedMar 2020View details →

FIGURE 30. Partial cladogram modified from Data Set 3 in Review of crayfish color patterns in the Family Cambaridae (Astacoidea), with discussion of their possible importance

FIGURE 30. Partial cladogram modified from Data Set 3 from Stern et al. 2017 showing Clades 4 & 5, with photos for comparison of color patterns between Cambarus aff. dubius and Cambarus gentryi.

opennotspecifiedMar 2020View details →

FIGURE 26. Partial cladogram modified from Data Set 3 in Review of crayfish color patterns in the Family Cambaridae (Astacoidea), with discussion of their possible importance

FIGURE 26. Partial cladogram modified from Data Set 3 from Stern et al. (2017); showing two subclades A & B within the clade of the former subgenus Pennides in the genus Procambarus.

openother-ncMar 2020View details →

The "Last.fm" data set used in the article "Cumulative effects of triadic closure and homophily in social networks"

This is the "Last.fm" network used in the article: A. Asikainen, G. Iñiguez,  J. Ureña-Carrión,  K. Kaski, M. Kivelä. Cumulative effects of triadic closure and homophily in social networks. Science Advances (in press) https://doi.org/10.1126/sciadv.aax7310 The data set is described in the article. Please cite the original article when using this data set. The original data in which this network is based on was donwloaded from audioscrobbler.net where it was licensed under the "Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England & Wales" licese, and accordinly this data set uses the same license. The data contains two files: lastfm.edg This is the network formatted as an edge list, where each row in the file is an edge connecting the two nodes indicated by the two numbers separated by a whitespace. Each node number corresponds to a single account in the website. lastfm_genders.txt This is the list of genders of the nodes. Each row corresponds to one node. The first number is the node id (matching the one in the edge list) and the second number indicates the gender such that 0=male and 1=female.

Data set for comparison between two biosignals acquisition systems – BioNomadix and BITalino

The data was collected in order to compare quality of the signal acquired by two devices – BITalino (Da Silva, Guerreiro, Lourenço, Fred, & Martins, 2014) and BioNomadix (BIOPAC Systems Inc., Goleta, CA, USA).

Participatory Design of Usability Requirements for Access Control in an Evolutionary-Teal Organization Workshop Data Set

In order to enable members of a socio-technical evolutionary-teal organization to design the technical component for access control, we conducted a workshop that structures the requirement engineering with members. The workshop aims to illustrate user needs, challenges and potential solutions that can be used to infer usability requirements for the development of a prototype of the user interface for access control in the form of "problem statements" and clustered mind-maps. The workshop is setup as a standalone, five-and-a-half-hour group discussion. It uses the methods of Design Thinking and Participatory Design. The workshop has been recorded in video and this data set contains a textual German transcript and transcripts of the moderation cards that have been created during the workshop for various guiding topics. We hope that the material can be used to (a) comprehend the interpretation used in our qualitative research, (b) to adapt the workshop model by other volunteers of our case study Viva con Agua de St. Pauli e.V. (<a href="https://www.vivaconagua.org/">https://www.vivaconagua.org/</a>), and (c) investigate other interesting research questions. Some material, such as timetables and storyboard, has been omitted in the publication due to privacy restrictions.