Find research datasets worth reusing

SNDlib-MIPs: A new set of homogeneous MILP instances

We constructed 289 MILP instances based on the Survivable Network Design Library (SNDLib) <a href="http://sndlib.zib.de/home.action">http://sndlib.zib.de/home.action</a> and the models presented within. Our models differ from the formulations provided in that SOS constraints are used in place of big-M constraints, a set of edge disjoint paths are used in place of all possible paths, pre-installed capacity is only available if links are included, and in the bidirectional case capacities are the sum of both capacities instead of the maximum. The models were constructed using SCIP 8.0.3, with the code available at <a href="https://github.com/Opt-Mucca/branching-via-cut-selection">https://github.com/Opt-Mucca/branching-via-cut-selection</a>. Please see the attached PDF for a complete model description.

Data Set "Protein network centralities as descriptor for QM region construction in QM/MM simulations of enzymes"

This data set accompanies the publication "Efficient automatic construction of atom-economical QM regions with point-charge variation analysis" by Felix Brandt and Christoph R. Jacob (TU Braunschweig, Germany)  It contains: - PDB files of the starting structures - modified AMBER95 force field file - AMS fragment files for the substrates and ions - AMS input files for all geometry optimizations and single point calculations - Python script for WISP and centrality analysis

Individual and collective school-students' self-efficacy on climate change. Raw data set.

The dataset comprises 12 items on the topic of "self-efficacy on climate change" and additionally some items on person-related information. The items were developed with individual self-efficacy (8 items, iSE1 to iSE8) and collective self-efficacy (4 items, coSE1 to coSE4) in mind. The instruction was: "How do you think about yourself? Please tick to what extent the following statements apply to you" [translated from German]. A six-point Likert-type scale was used as response scale, headed with numbers from 1 to 6, only the ends of the response scale were verbally labelled (1 = "strongly disagree" to 6 = "strongly agree" [translated from German]). The items were used in German. The English translations were added to the dataset in square brackets. Data collection took place at German "Gymnasien" (equivalent to grammar schools) in 2022. The data set includes N = 163 school-students.

Individual and collective university students' self-efficacy on climate change. Raw data set.

The dataset comprises 12 items on the topic of "self-efficacy on climate change" and additionally some items on person-related information. The items were developed with individual self-efficacy (8 items, iSE1 to iSE8) and collective self-efficacy (4 items, coSE1 to coSE4) in mind. The instruction was: "How do you think about yourself? Please tick to what extent the following statements apply to you" [translated from German]. A six-point Likert-type scale was used as response scale, headed with numbers from 1 to 6, only the ends of the response scale were verbally labelled (1 = "strongly disagree" to 6 = "strongly agree" [translated from German]). The items were used in German. The English translations were added to the dataset in square brackets. Data collection took place at a German university in 2022. The data set includes N = 145 university students studying to become geography teachers.

Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems

Two power distribution systems are presented. The first system consists of 53 nodes and 61 branches, while the second system consists of 404 buses and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.

opencc-by-4.0Apr 2023View details →

Data sets and machine learning models for: Predicting critical properties and acentric factor of fluids using multi-task machine learning

The experimental data sets, data splits, additional features, QM calculations, model predictions, and final machine learning models for the manuscript "Predicting Critical Properties and Acentric Factor of Fluids Using Multi-Task Machine Learning". Citation should refer directly to the manuscript: <ul> <li> Biswas, S.; Chung, Y.; Ramirez, J.; Wu, H.; Green, W. H. Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning. Journal of Chemical Information and Modeling. 2023 63 (15), 4574-4588. DOI: <a href="https://doi.org/10.1021/acs.jcim.3c00546">10.1021/acs.jcim.3c00546</a> </li> </ul> To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a>.  Detailed information can be found in README.md file.   Details on the properties considered The data set includes the following 8 properties: <ul> <li>Tc: critical temperature, in K</li> <li>Pc: critical pressure, in bar</li> <li>rhoc: critical density, in mol/L</li> <li>omega: acentric factor, unitless</li> <li>Tb: boiling point, in K</li> <li>Tm: melting point, in K</li> <li>dHvap: enthalpy of vaporization at boiling point, in kJ/mol</li> <li>dHfus: enthalpy of fusion at melting point, in kJ/mol</li> </ul> Details on the files 1. Data sets under CritProp_v1.1.0: <ul> <li>all_data: includes the data sets used in this work. All data points are listed for each chemical compound as well as its corresponding data source. The details of the data sources can be found in the README.md file. The distribution of the data set is included in each folder. <ul> <li>estimated_data_for_pretraining: contains the estimated data from Yaws' handbook that are used to pre-train our machine learning (ML) model.</li> <li>experimental_data: contains the experimental data (references 1 - 15) used to fine-tune our final ML model.</li> </ul> </li> <li>additional_features: includes the additional features tested for the ML model. The Abraham features are generated for all data (references 1 - 15) while the acsf, qm, and rdkit features are only generated for the data from references 1 - 9. <ul> <li>abraham: Abraham solute parameters (E, S, A, B, L). Molecular features.</li> <li>acsf: ACSF (atom-centered symmetry functions). Atomic features that are coverted from the 3D coordinates of the compound</li> <li>qm_atom: QM (quantum chemical) atomic feature. </li> <li>qm_mol: QM molecular feature.</li> <li>rdkit: Selected RDKit 2D molecular features.</li> </ul> </li> <li>data_splits_and_model_predictions: contains the training and test sets used to evaluate the model. It also contains the predicted values from our final ML model for each test set. <ul> <li>random and scaffold splits: training and test sets that include the data from references 1 - 9.</li> <li>external test set: a test set that includes the data from only references 10 - 15.</li> </ul> </li> </ul> 2. Machine learning (ML) model files: <ul> <li>CritProp_ML_model_files_with_abraham_feat.zip: contains the Chemprop ML model files that are trained using Abraham features as additional molecular features. This gives the best results.</li> <li>CritProp_ML_model_files_without_additional_feat.zip: contains the Chemprop ML model files that are trained without any additional features. This gives the second best results.</li> </ul> To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a> 3. QM (quantum chemical) calculations: <ul> <li>QM_calculations.zip: contains the results of the QM calculations that are performed to compute QM features.</li> </ul>

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Phylogenomic analyses using a new 1013-gene Vitaceae bait-set support major groups of North American Vitis

A set of newly designed Vitaceae baits targeting 1013 genes was employed to explore phylogenetic relationships among North American Vitis. Eurasian Vitis taxa including Vitis vinifera were found to be nested within North American Vitis subgenus Vitis. North American Vitis subgenus Vitis can be placed into nine main groups: the Monticola group, the Occidentales group, the Californica group, the Vinifera group (introduced from Eurasia), the Mustangensis group, the Palmata group, the Aestivali group, the Labrusca group, and the Cinerea group. Strong cytonuclear discordances were detected in North American Vitis, with many species non-monophyletic in the plastid phylogeny, while monophyletic in the nuclear phylogeny. The phylogenomic analyses support recognizing four distinct species in the Vitis cinerea complex in North America: V. cinerea, V. baileyana, V. berlandieri, and V. simpsonii. Such treatment will better serve the conservation of wild Vitis diversity in North America.

opencc-zeroJun 2023View details →

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Data from: Axial conduit widening, tree height and height growth rate set the hydraulic transition of sapwood into heartwood

The size-related xylem adjustments required to maintain a constant leaf-specific sapwood conductance (KLEAF) with increasing height (H) are still under discussion. Alternative hypotheses are that: (i) the conduit hydraulic diameter (Dh) at any position in the stem and/or (ii) the number of sapwood rings at stem base (NSWr) increase with H. In addition, (iii) lower stem elongation (ΔH) increases the tip-to-base conductance through inner xylem rings, thus possibly the NSWr contributing to KLEAF. A detailed stem analysis showed that Dh increased with the distance from the apex (DCA) in all rings of a P. abies and a F. sylvatica tree. Net of DCA effect, Dh did not increase with H. Using sapwood traits from a global dataset, NSWr increased with H and decreased with ΔH, and the mean sapwood ring width (SWrw) increased with ΔH. A numerical model based on anatomical patterns predicted the effects of H and ΔH on the conductance of inner xylem rings. Results suggested the sapwood/heartwood transition depends on both H and ΔH, and is set when the C allocation to maintenance respiration of living cells in inner sapwood rings produces a lower gain in total conductance than investing the same C in new vascular conduits.

opencc-zeroJul 2023View details →

Data set supplementing "Characteristics of Users and Nonusers of Symptom Checkers in Germany: Cross-Sectional Survey Study"

This is the data set used to conduct the analyses in the article published by the Journal of Medical Internet Research under the title "Characteristics of Users and Nonusers of Symptom Checkers in Germany: Cross-Sectional Survey Study" (<a href="https://doi.org/10.2196/46231">https://doi.org/10.2196/46231</a>) This data set contains information collected of 1,084 respondents about their awareness, use, and usefulness of symptom checkers and several individual characteristics.

opencc-by-4.0Apr 2023View details →

Data set for "Electrical control of hybrid exciton transport in a van der Waals heterostructure"

Data set for "Electrical control of hybrid exciton transport  in a van der Waals heterostructure"

Car Data Set

Car data set

Velocity data from GETM GBA set up

Sample surface velocity data from a GETM model of the Greater Bay Area region encompassing Hong Kong. Data for use with https://github.com/julianmak/ParcelsGETM_demo for a summer school demonstrating the use of the Parcels package (https://oceanparcels.org/)

Data Set for Copper-Catalyzed Benzylic Functionalization of Lignin-Derived Monomers

Raw NMR and HRMS data for the article entitled Copper-Catalyzed Benzylic Functionalization of Lignin-Derived Monomers. The folder names corrspond to the compound names.

Paradata datasets for: Identifying a Set of Key Paradata Indicators in Web Surveys

Paradata datasets for Identifying a Set of Key Paradata Indicators in Web Surveys Based on the Relationship with Response Quality, Respondent Characteristics and Substantive Variables

Supporting Data Set for Paper "Can Videos as a By-Product of GUI Testing Help Developers Understand GUI Tests?"

This data set is a supporting material for an accepted paper "Can Videos as a By-Product of GUI Testing Help Developers Understand GUI Tests?" on 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW 2023). This dataset consists of <ul> <li>a consent form of the study in English;</li> <li>a tutorial video for TakeNote App (see TakeNote Tutorial-v02);</li> <li>a questionnaire in HTML format;</li> <li>used videos (in HTML Video Player with Videos and VTT files) and screenshots;</li> <li>the source code of the HTML Video Player (in HTML Video Player with Videos and VTT files);</li> <li>obtained and coded results from the questionnaire (see Study-Data-4EmpiRE-v22);</li> <li>calculation steps of the Mann-Whitney U Test;</li> <li>the source code of the TakeNote App.</li> </ul>

TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN automatic weather station data set from Nafingalm

ABSTRACT This data set was collected with an automatic weather station (AWS) of <a href="http://acinn.uibk.ac.at/">ACINN</a> at the Nafingalm, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022). DATA SET DESCRIPTION 1. Location The AWS was located close to a small lake at the Nafinglam in the Weer Valley, Tyrol, Austria. The exact location is: 47.2151419°N / 11.712628°E / 1928 m MSL 2. Period The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the AWS data set provided here contains the period from 15 June to 12 September 2022. The time series has a measurement interval of 5 minutes and, depending on the parameter, contains both mean values and instantaneous values. 3. Instrument details A detailed description of the AWS, its sensors, parameters, calibration and correction procedures is provided as part of the netCDF file metadata, as well as in a PDF file containing the netCDF header extracted with the Linux command ncdump -h. 4. Data file The data are provided in a single netCDF file teamx_pc22_aws_nafingalm.nc together with a description of its content in the PDF file teamx_pc22_aws_nafingalm_ncdump_output.pdf. 5. Analysis A first analysis of the data was performed in a Bachor thesis (Viebahn, 2023), which is available upon request from the first author of this data set. 6. Contact Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set. 7. References Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubi&scaron;ic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J.  Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, 2022: A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. Bulletin of the American Meteorological Society, 103, E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a> Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubi&scaron;ić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, 2020: Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a> Viebahn, T., 2023: Windregime und Stabilität in einem alpinen Seitental im Sommer: Eine Standortcharakterisierung im Rahmen der TEAMx Vorkampagne 2022. Bachlor thesis, University of Innsbruck, 75 pp.

Blommersia wittei complex revision - data sets

Data set containing original sequence alignments, call recordings and a table with sequences and sequence metadata for the paper: Vences et al., "Integrative revision of the Blommersia wittei complex, with description of a new species of frog from western and north-western Madagascar". Zootaxa 5319 (2): 178–198. The paper can be found at DOI: https://doi.org/10.11646/zootaxa.5319.2.2

Called peaks for 8 D. melanogaster functional genomics data sets

Analyses were performed on chromatin accessibility (ATAC-seq, DNase-seq, FAIRE-seq), histone modification ChIP-seq (H3K4me1, H3K4me3, and H3K27ac), and direct enhancer activity reporter via an ectopic plasmid based assay (STARR-seq) data sets generated from experiments in D. melanogaster. The ATAC-seq and FAIRE-seq data sets were generated using wandering third instar larvae eye antennal imaginal disc tissue extracted from the FRT82 stock (Davie et al., 2015), while all other data sets were generated from Drosophila melanogaster S2 cells (Arnold et al., 2013; Henriques et al., 2018; de Almeida et al., 2022). Sequencing reads were downloaded from the NIH SRA, cleaned and trimmed using Trimmomatic v0.39 (Bolger et al., 2014), and aligned to the D. melanogaster r6.45 genome (Hoskins et al., 2015) using BWA (bwa aln, default settings) v0.7.17-r1188 (Li and Durbin, 2009). The D. melanogaster genome was downloaded from Fly Base (Gramates et al., 2022). Aligned reads were filtered for mapping quality (-q 10 -F 0x0200 -F 0x0100 -F 0x004) using SAMTools v1.11 (using htslib v1.11-4) (Li et al., 2009). Peaks were called using MACS2 v 2.2.7.1 (Zhang et al., 2008) with FDR correction (-q 0.01) and the preset D. melanogaster genome size (-g dm). The data sets varied in terms of read lengths, single-end or paired-end, and the availability of control data, so parameters were adjusted appropriately. MACS peak calling parameters for ATAC-seq and FAIRE-seq data were taken from Davie et al. (2015).

opencc-zeroJul 2023View details →

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Data for: Russian honey bee genotype identification through enhanced marker panel set

Russian honey bees (RHB) are a breeding population developed by USDA-ARS as an effort to provide Varroa-resistant honey bees to beekeepers. The selection strategy for this breeding population was the first in honey bees to incorporate genetic stock identification (GSI). The original GSI approach has been in use for over a decade, and though effective, novel technologies and analytical approaches recently developed provide an opportunity for improvement. Here we outline a novel genotyping assay that capitalizes on the markers used in the GSI as well as novel loci recently identified in a whole genome pooled study of commercial honey bee stocks. Our approach utilizes a microfluidic platform and machine learning analyses to arrive at an accurate, high throughput assay. This novel approach provides an improved tool that can be readily incorporated into breeding decisions towards healthier more productive bees.

Muon000 and Muon002 raw and pressure temperature corrected data set

Data from Two detectors Muon000 and Muon002. Raw coincidence counts and pressure temperature corrected data set.