Find research datasets worth reusing

Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis

We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes. Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks. Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions. An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available. Please cite:  https://pubmed.ncbi.nlm.nih.gov/24521231/  https://pubs.acs.org/doi/10.1021/ci400709d

Data for publication of "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning"

The data are made available as part of the paper "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning", submitted to Geoscientific Model Development. This data set incorporates selected post-processed files needed to reproduce the results presented in the paper. The data contains six zip files, that are: <ul> <li>Namelist.input files for the WRF model simulations of ten tropical cyclones</li> <li>WRF model simulation outputs using the default parameter values</li> <li>WRF model simulation outputs using the optimal parameter values (which give minimum RMSE value)</li> <li>IMDAA surface observations and IMERG precipitation data</li> <li>IMD observed tracks of ten tropical cyclones</li> <li>Ipython notebooks of sensitivity analysis and machine learning codes</li> </ul> The remaining files are the ncl scripts that were used to obtain the figures. The ncl scripts used the data in the zip files.

Opinions and data on short food supply chains related policy analysis in 9 EU countries generated in the H2020 SMARTCHAIN Project grant number: 773785

Questionnaires: Opinions and data on short food supply chains related policy analysis in 9 EU countries generated in the H2020 SMARTCHAIN Project grant number: 773785

Hurricane Michael - SAMURAI analysis files created from NOAA P-3 tail Doppler radar data

This repository contains analyses of Hurricane Michael created from quality controlled data from the NOAA P3 tail Doppler radar. The TDR data can be found in its raw format at the following link under the folders 20181008H1, 20181009H1, 20181009H2, and 20181010H1: <a href="http://seb.noaa.gov/pub/acdata/2018/RADAR/">https://seb.noaa.gov/pub/acdata/2018/RADAR/</a> The analyses are the topic of the manuscript 'Vertical Vortex Development in Hurricane Michael (2018) during Rapid Intensification' which is in review as of dataset publication. Please cite the manuscript when using this data as it contains methodological information on how the analyses were created. More information about the center fix times each analysis file is related to are available in the manuscript. Code to run the SAMURAI analysis tool which created these files, information on how SAMURAI works, and directions can be found at the following 2 links: <a href="http://github.com/mmbell/samurai">https://github.com/mmbell/samurai</a> <a href="http://wiki.lrose.net/index.php/SAMURAI">http://wiki.lrose.net/index.php/SAMURAI</a>

Codebook for the analysis of focus group interview data collected as part of the DETECT project.

This is the codebook created for the analysis of focus group interview data collected as part of the <a href="http://detectproject.eu/">DETECT project</a>. In particular, nine interviews were conducted with primary and secondary school teachers in Finland, Italy, Spain and the UK in order to explore their perceptions of critical digital literacies and how these are manifested in their practices. The interviews were organised and conducted by the researchers in the respective local HEIs between February-June 2020. A total of 7 focus-groups interviews took place with a total number of 39 participating teachers (7 from Finland, 6 from the UK, 9 from Spain and 17 from Italy). The interviews were conducted both face to face (schools in Spain) and online (schools in Finland, Italy and the UK) due to pandemic-related restrictions imposed shortly after the start of the data collection period. The collected data were read and segmented and all interview excerpts relating to the Critical Digital Literacies framework sub-dimensions were marked and chosen for the analysis. The total number of marked segments was 666 in all nine focus group interviews. To ensure the reliability of the analyses, the researchers translated and collected a representative sample of codings (n=117 out of 666) for each sub-dimensions from each school and these were examined by all researchers in several consensus meetings and the final criteria for each sub-category were constructed together based on those discussions. This dataset accompanies the intellectual outputs of the DETECT project, including amongst others this report: Gouseti, A., Bruni, I., Ilomäki, L., Lakkala, M., Mundy, D., Raffaghelli, J., Ranieri, M., Roffi, A., Romero, M. and Romeu, T. (2021) Schools’ perceptions and experiences of critical digital literacies across four European countries - DETECT Report 2. Accessed at: <a href="http://doi.org/10.5281/zenodo.5070394">http://doi.org/10.5281/zenodo.5070394</a> Acknowledgement This project was funded by Erasmus+, KA2. Project Reference: 2019-1-UK01-KA201-061508.

opencc-by-4.0Aug 2021View details →

Simulation data for "Phylogenetic analysis of migration, differentiation, and class switching in B cells"

Simulation data for https://doi.org/10.1101/2020.05.30.124446. Scripts available at: https://bitbucket.org/kleinstein/projects/src/master/Hoehn2020/ data_twostate.tsv: AIRR TSV file of data used for simulations trees_twostate.RData: Tree topologies used to simulate migration simulations.tar.gz: Files used in simulation analyses. File names show: <rate (r)>_<rate A to B (r_ab)>_<pi_A>_<repetition> laddersims.tar.gz: Files used in ladder tree simulation analyses. File names show: <rate (r)>_<rate A to B (r_ab)>_<pi_A>_<number of tips>_<number of trees>_<repetition>

opencc-by-4.0Aug 2021View details →

Phenotype, genotype and fitness data related to genetic analysis of praziquantel response in schistosome parasites.

These data are related to the study of the Genetic analysis of praziquantel response in schistosome parasites implicates a Transient Receptor Potential channel. Mass treatment with praziquantel (PZQ) monotherapy is the mainstay for schistosomiasis treatment. This drug shows imperfect cure rates in the field and parasites showing reduced PZQ response can be selected in the laboratory, but the extent of resistance in Schistosoma mansoni populations is unknown. We examined the genetic basis of variation in PZQ response in a S. mansoni population (SmLE-PZQ-R) selected with PZQ in the laboratory: 35% of these worms survive high dose (73 µg/mL) PZQ treatment. We used genome wide association to map loci underlying PZQ response. The major chr. 3 peak contains a transient receptor potential (Sm.TRPM_PZQ) channel (Smp_246790), activated by nanomolar concentrations of PZQ. PZQ response shows recessive inheritance and marker-assisted selection of parasites at a single Sm.TRPM_PZQ SNP enriched populations of PZQ-resistant (PZQ-ER) and sensitive (PZQ-ES) parasites showing >377 fold difference in PZQ response. The PZQ-ER parasites survived treatment in rodents better than PZQ-ES. Resistant parasites show 2.25-fold lower expression of Sm.TRPM_PZQ than sensitive parasites. Specific chemical blockers of Sm.TRPM_PZQ enhanced PZQ resistance, while Sm.TRPM_PZQ activators increased sensitivity. A single SNP in Sm.TRPM_PZQ differentiated PZQ-ER and PZQ-ES lines, but mutagenesis showed this was not involved in PZQ response, suggesting linked regulatory changes. We surveyed Sm.TRPM_PZQ sequence variation in 259 parasites from the New and Old World revealing one nonsense mutation that results in a truncated protein with no PZQ-binding site. Our results demonstrate that Sm.TRPM_PZQ underlies variation in PZQ response in S. mansoni and provides an approach for monitoring emerging PZQ-resistance alleles in schistosome elimination programs. This dataset is divided in 3 folders. Each folder has a readme detailing its content. 1-Phenotyping_data This folder includes the data tables related to the phenotyping of the worms performed during this study. The phenotype measured was the viability of worms following PZQ treatment (i.e., PZQ response). This viability was assessed microscopically or using worm lactate production released in culture media. The data correspond to the following experiments: <ul> <li>PZQ response of single adult male worms from SmLE and SmLE-PZQ-R populations to different doses of PZQ. This data was used to determine the PZQ IC50 of each population.</li> <li>Lactate production from single SmLE-PZQ-R adult male worms and correlation with visual observation. This was a proof-of-principle that lactate production can be used to efficiently and unbiasedly phenotype schistosome adult male worms in response to PZQ drug.</li> <li>PZQ response of single SmLE-PZQ-R adult male worms. These worms were then divided in low and high producer in response to PZQ and used to perform a genome-wide association study.</li> <li>PZQ response of single adult male worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations to different doses of PZQ. This data was used to determine the PZQ IC50 of each population.</li> <li>PZQ response of single adult male worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations in presence of Sm.TRPM_PZQ blocker (MB2) and activator (MV1) with and without PZQ drug.</li> <li>In vivo PZQ response of schistosome worms from SmLE-PZQ-ER and SmLE-PZQ-ES populations.</li> </ul> 2-Genotyping_data This folder includes the data tables related to the genotyping of the worms performed during this study. Worms were genotyping using PCR-RFLP (genotyping of single nucleotide polymorphisms (SNPs) on chr2 and chr3 QTLs) or using qPCR (genotyping of a copy number variation (CNV) on chr3 QTL). The data correspond to the following experiment: <ul> <li>Association between PZQ response of single adult male worms from SmLE-PZQ-R population and their respective genotype on chromosome 2 (SNP) and chromosome 3 (SNP and CNV) loci.</li> </ul> 3-Fitness_data This folder includes the data tables related to the fitness of the parasite populations. We collected data regarding: <ul> <li>The number of surviving and infected snails after exposure to SmLE-PZQ-ER or SmLE-PZQ-ES miracidia.</li> <li>The number of adult worms recovered from golden Syrian female hamsters exposed to SmLE-PZQ-ER or SmLE-PZQ-ES cercariae.</li> </ul> All the data were collected during 12 generations of parasites and are used to evaluate a potential impact of PZQ resistance on the parasite fitness.

opencc-by-4.0Aug 2021View details →

Diversity in citations to a single study: Supplementary data set for citation context network analysis

Introduction This document describes the data set used for all analyses in 'Diversity in citations to a single study: A citation context network analysis of how evidence from a prospective cohort study was cited' accepted for publication in Quantitative Science Studies [1]. Data Collection The data collection procedure has been fully described [1]. Concisely, the data set contains bibliometric data collected from Web of Science Core Collection via the University of Edinburgh’s Library subscription concerning all papers that cited a cohort study, Paul et al. [2], in the period <1985. This includes a full list of citing papers, and the citations between these papers. Additionally, it includes textual passages (citation contexts) from 343 citing papers, which were manually recovered from the full-text documents accessible via the University of Edinburgh’s Library subscription. These data have been cleaned, converted into network readable datasets, and are coded into particular classifications reflecting content, which are described fully in the supplied code book and within the manuscript [1].  Data description All relevant data can be found in the attached file 'Supplementary_material_Leng_QSS_2021.xlsx', which contains the following five workbooks: <ul> <li>“Overview” includes a list of the content of the workbooks.</li> <li>“Code Book” contains the coding rules and definitions used for the classification of findings and paper titles.</li> <li>“Node attribute list” includes a workbook containing all node attributes for the citation network, which includes Paul et al. [2] and its citing papers as of 1984. Highlighted in yellow at the bottom of this workbook is two papers that were discarded due to duplication - remove these if analysing this dataset in a network analysis. The columns refer to:</li> </ul> <ol> <li>Id, the node identifier</li> <li>Label, the formal citation of the paper to which data within this row corresponds. Citation is in the following format: last name of first author, year of publication, journal of publication, volume number, start page, and DOI (if available).  </li> <li>Title, the paper title for the paper in question.</li> <li>Publication_year, the year of publication.</li> <li>Document_type, the document type (e.g. review, article)</li> <li>WoS_ID, the paper’s unique Web of Science accession number.</li> <li>Citation_context, a column specifying whether citation context data is available from that paper</li> <li>Explanans, the title explanans terms for that paper;</li> <li>Explanandum, the explanandum terms for that paper.</li> <li>Combined_Title_Classification, the combined terms used for fig 2 of the published manuscript.</li> <li>Serum_cholesterol_(SC), a column identifying papers that cited the serum cholesterol findings.</li> <li>Blood_Pressure_(BP), a column identifying papers that cited the blood pressure findings.</li> <li>Coffee_(C), a column identifying papers that cited the coffee findings.</li> <li>Diet_(D), a column identifying papers that cited the dietary findings.</li> <li>Smoking_(S), a column identifying papers that cited the smoking findings.</li> <li>Alcohol_(A), a column identifying papers that cited the alcohol findings.</li> <li>Physical_Activity_(PA), a column identifying papers that cited the physical activity findings.</li> <li>Body_Fatness (BF), a column identifying papers that cited the body fatness findings.</li> <li>Indegree, the number of within network citations to that paper, calculated for the network shown in Fig 4 of the manuscript.</li> <li>Outdegree, the number of within network references of that paper as calculated for the network in Fig 4.</li> <li>Main_component, a column specifying whether a node is contained in the largest weakly connect component as shown in Fig 4 of the manuscript.</li> <li>Cluster, provides the cluster membership number as discussed within the manuscript (Fig 5).</li> </ol> <ul> <li>“Edge list” includes a workbook including the edges for the network. The columns refer to:</li> </ul> <ol> <li>Source, contains the node identifier of the citing paper.</li> <li>Target, contains the node identifier of the cited paper.</li> </ol> <ul> <li>“Citation context classification” includes a workbook containing the WoS accession number for the paper analysed, and any finding category discussed in that paper established via context analysis (see the code book for definitions). The columns refer to:</li> </ul> <ol> <li>Id, the node identifier</li> <li>Finding_Class, the findings discussed from Paul et al. within the body of the citing paper.  </li> </ol> <ul> <li> “Citation context data” includes a workbook containing the WoS accession number for papers in which citation context data was available, the citation context passages, the reference number or format of Paul et al. within the citing paper, and the finding categories discussed in those contexts (see code book for definitions). The columns refer to:</li> </ul> <ol> <li>Id, the node identifier</li> <li>Citation_context, the passage copied from the full text of the citing paper containing discussion of the findings of Paul et al.</li> <li>Reference_in_citing_article, the reference number or format of Paul et al. within the citing paper.</li> <li>Finding_class, the findings discussed from Paul et al. within the body of the citing paper. </li> </ol> Software recommended for analysis For the analyses performed within the manuscript, Gephi version 0.9.2 was used [3], and both the edge and node lists are in a format that is easily read into this software. The Sci2 tool was used to parse data initially [4]. Notes <ol> <li>Leng, R. I. (Forthcoming). Diversity in citations to a single study: A citation context network analysis of how evidence from a prospective cohort study was cited. Quantitative Science Studies.</li> <li>Paul, O., Lepper, M. H., Phelan, W. H., Dupertuis, G. W., Macmillan, A., McKean, H., et al. (1963). A longitudinal study of coronary heart disease. Circulation, 28, 20-31. <a href="https://doi.org/10.1161/01.cir.28.1.20">https://doi.org/10.1161/01.cir.28.1.20</a>.</li> <li>Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.</li> <li>Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies. Stable URL: <a href="https://sci2.cns.iu.edu">https://sci2.cns.iu.edu</a></li> </ol>

FIGURES 14–22. 14–15 in Are Monobia and Montezumia Monophyletic? A Cladistic Analysis of Their Species Groups Based on Morphological Data (Hymenoptera, Vespidae, Eumeninae)

FIGURES 14–22. 14–15. Female pronotum in dorsal-oblique view: 14. Mb. apicalipennis; 15. Mt. analis. 16–18. Female mesosoma in lateral view: 16. Mb. apicalipennis; 17. Mt. dimidiata; 18. Mt. nigriceps. 19–22. Female mesosoma in dorsal view: 19. Mb. apicalipennis; 20. Mt. analis; 21. Mt. dimidiata; 22. Mt. nigriceps. Scale bars for 14–15 = 1 mm; 16–18 and 22 = 1 mm; 19–21 = 2 mm.

FIGURES 42–47. Monobia goiana. 42–43 in Are Monobia and Montezumia Monophyletic? A Cladistic Analysis of Their Species Groups Based on Morphological Data (Hymenoptera, Vespidae, Eumeninae)

FIGURES 42–47. Monobia goiana. 42–43. Habitus: 42. Female; 43. Male. 44. Female mesosoma in dorsal view. 45–46. Head in frontal view: 45. Female; 46. Male. 47. Female metasoma in dorsal view. Scale bars for 42–43 = 3 mm; 44, 47 = 1 mm; 45–46 = 2 mm.

FIGURES 33–41. 33–36 in Are Monobia and Montezumia Monophyletic? A Cladistic Analysis of Their Species Groups Based on Morphological Data (Hymenoptera, Vespidae, Eumeninae)

FIGURES 33–41. 33–36. Male paramere and volsella (digitus and cuspis) in lateral inner view: 33. Mb. quadridens; 34. Mt. analis; 35. Mt. azurescens; 36. Mt. brethesi. 37–40. Male aedeagus in lateral view: 37. Mb. quadridens; 38. Mt. analis; 39. Mt. azurescens; 40. Mt. brethesi. 41. Sketchy representation of the digitus of Mb. quadridens and Mt. analis: da = basal dorsal angle of digitus; va = basal ventral angle of digitus. Scale bars for 33–35 = 1 mm; figs. 36–39 = 1 mm; 40 = 0.5 mm.

FIGURES 48–53. 48–50 in Are Monobia and Montezumia Monophyletic? A Cladistic Analysis of Their Species Groups Based on Morphological Data (Hymenoptera, Vespidae, Eumeninae)

FIGURES 48–53. 48–50. Mb. insueta, female: 48. Pronotum and mesoscutum in dorsal view; 49. Terga I–II in dorsal view; 50. Sterna I–VI in ventral view. 51–52. Mb. trifasciata, female: 51. Head and pronotum in dorsal view; 52. Head and mesosoma in lateral view. 53. Mb. goiana, female head and pronotum in dorsal view. Scale bars for 48–49 = 1 mm; 50, 52 = 2 mm; 51, 53 = 1 mm.

FIGURES 5–13. 5–9 in Are Monobia and Montezumia Monophyletic? A Cladistic Analysis of Their Species Groups Based on Morphological Data (Hymenoptera, Vespidae, Eumeninae)

FIGURES 5–13. 5–9. Female head in frontal view: 5. Mb. apicalipennis; 6. Mb. schrottkyi; 7. Mt. analis; 8. Mt. dimidiata; 9. Mt. infernalis. 10–13. Female head in dorsal view: 10. Mb. apicalipennis; 11. Mt. dimidiata; 12. Mb. funebris; 13. Mt. analis. Scale bars for 5–9 = 2 mm; 10–11 = 1 mm; 12–13 = 0.5 mm.

FIGURES 23–32. 23–24 in Are Monobia and Montezumia Monophyletic? A Cladistic Analysis of Their Species Groups Based on Morphological Data (Hymenoptera, Vespidae, Eumeninae)

FIGURES 23–32. 23–24. Female metanotum in posterior view: 23. Mt. azurescens; 24. Mt. nigriceps. 25–26. Female propodeum in posterior view: 25. Mb. angulosa; 26. Mt. azurescens. 27–28. Female lower propodeum in lateral view: 27. Mt. nigriceps; 28. Mt. coeruleorufa. 29–30. Female tergum I in lateral view: 29. Mb. quadridens; 30. Mt. azurescens. 31–32. Female sternum I in ventral view: 31. Mb. schrottkyi; 32. Mt. azurescens. Scale bars for 23 = 0.3 mm; 24 = 0.5 mm; 25–26, 30–31 = 1 mm; 27–28 = 0.5 mm; 29, 32 = 1 mm.

opencc-by-4.0Sep 2021View details →

Molecular Determinants and Pharmacological Analysis for a Class of Competitive Non-transported Bicyclic Inhibitors of the Betaine/GABA Transporter BGT1: Modeling Data

This archive contains the modeling data for the study <a href="https://www.frontiersin.org/articles/10.3389/fchem.2021.736457/full">"Molecular Determinants and Pharmacological Analysis for a Class of Competitive Non-transported Bicyclic Inhibitors of the Betaine/GABA Transporter BGT1"</a> (doi: 10.3389/fchem.2021.736457). The following data sets are available: <ul> <li>Induced fit docking results of all mentioned compounds in the study:  ifd_hBGT1_occ_clustering_all_compounds.zip  </li> <li>MD simulations of bicyclo-GABA and compound 1 (100ns, 3 replica): MD_simulation_bicyclo-GABA_run1.zip MD_simulation_bicyclo-GABA_run2.zip MD_simulation_bicyclo-GABA_run3.zip MD_simulation_cmd1_run1.zip MD_simulation_cmd1_run2.zip MD_simulation_cmd1_run3.zip</li> </ul> A detailed description of the methods is available in the aforementioned publication.   The compound numbering in the uploaded files differs from the compound numbering in the mentioned study:   study / upload bicyclo-GABA / cmd4 1 / IIa 2 /8-2 3 / 8-3 4a / 7-1 4b / 7-2 4c / 7-3

opencc-by-4.0Dec 2017View details →

Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742. in New Hyaenodonts (Ferae, Mammalia) From The Early Miocene Of Napak (Uganda), Koru (Kenya) And Grillental (Namibia)

Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742.

opencc-by-4.0Sep 2021View details →

Data from: Biodiversity and biogeographic affinity of benthic amphipods from the Yucatan Shelf: an analysis across the warm Northwest Atlantic ecoregions

The resource provides a list of 491 species representing the biodiversity of benthic amphipods from 12 ecoregions in the Northwest Atlantic. The dataset contains an Excel sheet with species occurrence data from benthic marine habitats of the continental shelf (< 200 m), forming a comprehensive collection of distributional data (presence-only) that was obtained from different sources and newly-sampled material in the Yucatan continental shelf, and sorted according to the ecoregions. This information form part of a published article in the journal Systematics and Biodiversity (Paz-Ríos et al. 2021). To link to this article: <a href="https://doi.org/10.1080/14772000.2021.1947920">https://doi.org/10.1080/14772000.2021.1947920</a> Eleven ecoregions defined by Spalding et al. (2007) were used for sorting species occurrence data; an additional ecoregion defined by Wilkinson et al. (2009) was also used to represent the species occurrence data in the Yucatan continental shelf. Ecoregion names: (BAH) Bahamian; (BER) Bermuda; (CAR) Carolinian; (ECA) Eastern Caribbean; (FLO) Floridian; (GRA) Greater Antilles; (NGM) Northern Gulf of Mexico; (SCA) Southern Caribbean; (SWCA) Southwestern Caribbean; (SGM) Southern Gulf of Mexico; (WCA) Western Caribbean; and (YUC) Yucatan.

dryad40/100

Data for Integrated Step Selection Analysis of translocated female greater sage-grouse in the 60 days post-release, North Dakota 2018-2020

The data include used and random available steps at 11-hour resolution generated for 26 female greater sage-grouse in the 60 days post-translocation to North Dakota, with associated environmental predictors and individual information. The code fits individual habitat selection models in an Integrated Step Selection Analysis framework. Data used to fit the models described in: Picardi, S., Ranc, N., Smith, B.J., Coates, P.S., Mathews, S.R., Dahlgren, D.K. Individual variation in temporal dynamics of post-release habitat selection. Frontiers in Conservation Science (in review) Code used to implement the analysis is available on GitHub: https://github.com/picardis/picardi-et-al_2021_sage-grouse_frontiers-in-conservation

opencc-zeroSep 2021View details →

opencc-by-4.0Dec 2016View details →

PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Survey at citizen level and data relative to regional performance of the Cohesion Policy and institutional quality

1. Orignal PERCEIVE survey data (STATA file) 2. description of survey questions, descriptive results (word file) 3. EU Deliverable document with descriptive analysis of survey questions   ***please cite the following when using the microdata: Bauhr, M., & Charron, N. (2020). The EU as a savior and a saint? Corruption and public support for redistribution. Journal of European Public Policy, 27(4), 509-527. https://www.tandfonline.com/doi/full/10.1080/13501763.2019.1578816