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8,589 results for “evidence”
Evidence of alliesthesia during a neighborhood thermal walk in a hot and dry city (Phoenix, Arizona)
Thermal comfort should be an integral part of urban design in the context of global warming and urbanization. The influence of built infrastructure on thermal perceptions of walking pedestrians is not well explored, but thermal walks that combine sensing technologies with simultaneous collection of user experiences is a promising research direction to shorten the gap. We examined the relationships between the built environment, heat perception, and behavioral coping mechanisms in one of the most heat vulnerable Phoenix neighborhoods. Using Phoenix as an example, where extremely hot summer temperatures are becoming a norm, can help to address heat challenges of other cities that are facing rising temperatures. This study is an experimental citizen science project in which participants were surveyed during a 1-hour walk around the neighborhood and recorded their experience in a field guide. Walkers wore GPS devices and microclimate measurements were taken to gain deeper insights on subjective heat perception and physical body heat accumulation during the walk. Results revealed the differences in heat perception across a variety of urban landscapes. Participants identified preferred and most challenging locations. Combined GPS and microclimate data mapped in GIS visualized dependencies between the streetscape, microclimate, and thermal perceptions. Moreover, we presented the evidence of thermal alliesthesia, a feeling of pleasure from relieving of thermal discomfort. This project is one of the first to examine the impact of urban environment on dynamic psychological and physiological responses to heat. Using sensing technologies and collecting subjective perceptions, this research will inform the design changes in the neighborhood that will undergo redevelopment. It can serve as an example for other cities striving to adapt urban microclimates to new extremes.
Evidence accumulation relates to perceptual consciousness and monitoring
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Evidence Accumulation in Value-Based decisions
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Field Evidence of Carbon and Nitrogen Stabilization through Mineral Associated Organic Matter Formation in Coastal Wetland Soils from Apalachicola, Florida, collected in June, 2022.
This data set was used to observe the role of Mineral Associated Organic Matter Formation (MAOM) on biogeochemical soil properties in three coastal wetlands in Apalachicola, Florida. One wetland was restored using beneficial dredged sediment, increasing the soil's inorganic matter content. Soil samples were collected in June 2022 from this wetland and two nearby reference wetlands: one with high organic matter and the other with higher inorganic matter content. The samples were analyzed at the University of Central Florida for biogeochemical properties to determine which properties were most related to MAOM pools.
Data and code from Artificial light at night increases top-down pressure on caterpillars: experimental evidence from a light-naive forest - 2021-2022
This dataset has been prepared in support of a paper to be published in Proceedings of the Royal Society B: Biological Sciences. It includes both data files and R scripts used for the analysis in this publication: Deitch, J.F. and S.A. Kaiser. 2023. Artificial light at night increases top-down pressure on caterpillars: experimental evidence from a light-naive forest. Proceedings of the Royal Society B: Biological Sciences. (https://doi.org/10.1098/rspb.2023.0153) Artificial light at night (ALAN) is a globally widespread and expanding form of anthropogenic change that impacts arthropod biodiversity. ALAN alters interspecific interactions between arthropods, including predation and parasitism. Despite their ecological importance as prey and hosts, the impact of ALAN on larval arthropod stages, such as caterpillars, is poorly understood. We examined the hypothesis that ALAN increases top-down pressure on caterpillars from arthropod predators and parasitoids. We experimentally illuminated study plots with moderate levels (10-15 lux) of LED lighting at light-naive Hubbard Brook Experimental Forest, New Hampshire. We measured and compared between experimental and control plots: 1) predation on clay caterpillars and 2) abundance of arthropod predators and parasitoids. We found that predation rates on clay caterpillars and abundance of arthropod predators and parasitoids were significantly higher on ALAN treatment plots relative to control plots. These results suggest that moderate levels of ALAN increases top-down pressure on caterpillars. We did not test mechanisms, but sampling data indicates that increased abundance of predators near lights may play a role. This study highlights the importance of examining the effects of ALAN on both adult and larval life stages and suggests potential consequences of ALAN on arthropod populations and communities. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubba
Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"
<p>This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process. </p> <p><strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.</p> <p><strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).</p> <p>Please find the code needed to replicate the main findings of the paper described below:</p> <ul> <li>Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.</li> <li>Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".</li> <li>Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.</li> <li>SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.</li> </ul> <p><strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong></p>
Supplementary Materials: A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education
<p>Example data sets, syntax files and macros for the tutorials in: Balloo, K., & Winstone, N. E. (2021). A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education.<em> Frontline Learning Research</em>. <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.14786%2Fflr.v9i2.675&data=04%7C01%7Ck.balloo%40surrey.ac.uk%7C50bb47bb433744dc8da208d8c2116202%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637472728228002863%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fyA0y2hUkHESUJ7sVJ3s42Re4Yqa5XbgwW7AvEyGDdk%3D&reserved=0">https://doi.org/10.14786/flr.v9i2</a><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.14786%2Fflr.v9i2.675&data=04%7C01%7Ck.balloo%40surrey.ac.uk%7C50bb47bb433744dc8da208d8c2116202%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637472728228002863%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=fyA0y2hUkHESUJ7sVJ3s42Re4Yqa5XbgwW7AvEyGDdk%3D&reserved=0">.675</a> </p> <p><strong>The data for all examples are fictional, and have only been designed to simulate the possible behaviour of institutional data for the purposes of demonstrating the analytical approaches in the primer. No inferences or conclusions should be drawn from the findings of these examples, because the results are not real. </strong></p> <p>We anticipate that readers can use the example data sets as templates and substitute in their own data.</p>
Centre frequencies and uncertainties for "Evidence for a kilometre-scale seismically slow layer atop the core-mantle boundary from normal modes"
<p>A table containing the centre frequencies and uncertainties used for the study presented in "Evidence for a kilometre-scale seismically slow layer atop the core-mantle boundary from normal modes". This table is the same as is contained in the supplementary materials of that paper.</p> <p>Russell, S., Irving, J. C. E., Jagt, L., & Cottaar, S. (2023). Evidence for a kilometer-scale seismically slow layer atop the core-mantle boundary from normal modes. Geophysical Research Letters, 50, e2023GL105684. <a href="https://doi.org/10.1029/2023GL105684">https://doi.org/10.1029/2023GL105684</a></p>
Data from: "Little evidence of inbreeding depression for birth mass, survival and growth in Antarctic fur seal pups"
<p>This data repository contains:</p> <ul> <li><span>"msats_growth_individuals.xlsx" - Microsatellite data (39 loci) of Antarctic fur seals<br></span></li> <li><span>"pup_growth_2017-2020.xlsx" - Birth weight and tagging weight data for pups collected in 2017-2020.<br></span></li> <li><span>"Rebeccas_Samples_Mendel_OriginalPedigree" - SNP array data (75k SNPs) in PLINK format for a subset of individuals<br></span></li> <li><span>"GrowthRM_BI1820_Day60.new.csv" - Repeated weight measures for a subset of individuals</span></li> </ul> <p><strong><br>Manuscript abstract</strong></p> <p><span>Inbreeding depression, the loss of offspring fitness due to consanguineous mating, is generally detrimental for individual performance and population viability.<span> </span>We therefore investigated inbreeding effects in a declining population of Antarctic fur seals (<em>Arctocephalus gazella</em>) at Bird Island, South Georgia.<span> </span>Here, localised warming has reduced the availability of the seal’s staple diet, Antarctic krill, leading to a temporal increase in the strength of selection against inbred offspring, which are increasingly failing to recruit into the adult breeding population.<span> </span>However, it remains unclear whether selection operates before or after nutritional independence at weaning.<span> </span>We therefore used microsatellite data from 885 pups and their mothers, and SNP array data from 98 mother-offspring pairs, to quantify the effects of individual and maternal inbreeding on three important neonatal fitness traits: birth mass, survival and growth.<span> </span>We did not find any clear or consistent effects of offspring or maternal inbreeding on any of these traits.<span> </span>This suggests that selection filters inbred individuals out of the population as juveniles during the time window between weaning and recruitment.<span> </span>Our study brings into focus a poorly understood life-history stage and emphasises the importance of understanding the ecology and threats facing juvenile pinnipeds.</span></p> <p><strong><span>Funding</span></strong></p> <p><span>This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) priority programme “Antarctic Research with Comparative Investigations in Arctic Ice Areas” SPP 1158 (project number 424119118) and the SFB TRR 212 (NC³) (Project Numbers 316099922 & 396774617). This work contributes to the Ecosystems project of the British Antarctic Survey, Natural Environmental Research Council, and is part of the Polar Science for Planet Earth Programme.</span></p>
Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals
<p><strong>Supplementary Material for:</strong></p> <p>Emerling C.A., Springer M.S., Gatesy J., Jones Z., Hamilton D., Xia-Zhu D., Collin M.A., and Delsuc F. (2021). Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals.<strong><em> Open Research Europe</em></strong> 1:75. doi:10.12688/openreseurope.13795.1.</p> <p> </p> <p><strong>Supplementary File Legends:</strong></p> <p><strong>- Supplementary_Figure_S1.pdf:</strong> <em>AANAT</em> PAML ‘master model’ showing branch categories, corresponding to “Model 1: 24 ratio” in Supplementary Table S7.</p> <p><strong>- Supplementary_Figure_S2.pdf:</strong> <em>ASMT</em> PAML ‘master model’ showing branch categories, corresponding to “Model 2: 24 ratio” in Supplementary Table S8.</p> <p><strong>- Supplementary_Figure_S3.pdf:</strong> <em>MTNR1A</em> PAML ‘master model’ showing branch categories, corresponding to “Model 1: 27 ratio” in Supplementary Table S9.</p> <p><strong>- Supplementary_Figure_S4.pdf:</strong> <em>MTNR1B</em> PAML ‘master model’ showing branch categories, corresponding to “Model 1: 46 ratio” in Supplementary Table S10.</p> <p><strong>- Supplementary_Figure_S5.pdf:</strong> RAxML <em>AANAT</em> gene tree. Numbers at nodes correspond to bootstrap support values.</p> <p><strong>- Supplementary_Figure_S6.pdf: </strong>RAxML <em>ASMT</em> gene tree. Numbers at nodes correspond to bootstrap support values.</p> <p><strong>- Supplementary_Figure_S7.pdf: </strong>RAxML <em>MTNR1A</em>+<em>MTNR1B</em> tree. Numbers at nodes correspond to bootstrap support values.</p> <p><strong>- Supplementary_Figure_S8.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> exon 2 in cetaceans. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S9.pdf: </strong>Supporting data showing the inactivation of <em>ASMT</em> in spalacids and <em>Fukomys damarensis</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S10.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in hyracoids and <em>Cyclopes didactylus</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S11.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in sirenians. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S12.pdf: </strong>Supporting data showing the inactivation of <em>AANAT</em> in sirenians and a polymorphic premature stop codon in exon 5 of <em>ASMT</em> in <em>Trichechus manatus</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S13.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in <em>Condylura cristata</em>. Read Supplementary Table S13 for further details.</p> <p><strong>- Supplementary_Figure_S14.pdf: </strong>Supporting data showing the inactivation of <em>MTNR1A</em> in <em>Phataginus tricuspis</em>. Read Supplementary Table S14 for further details.</p> <p><strong>- Supplementary_Figure_S15.pdf: </strong>PAML <em>AANAT</em> results, Model 1: 24 ratio (see Supplementary Table S7).</p> <p><strong>- Supplementary_Figure_S16.pdf: </strong>PAML <em>ASMT</em> results, Model 2: 24 ratio (see Supplementary Table S8).</p> <p><strong>- Supplementary_Figure_S17.pdf: </strong>PAML <em>MTNR1A</em> results, Model 1: 27 ratio (see Supplementary Table S9).</p> <p><strong>- Supplementary_Figure_S18.pdf: </strong>PAML <em>MTNR1B</em> results, Model 1: 46 ratio (see Supplementary Table S10).</p> <p><strong>- Supplementary_Table_S1.xlsx: </strong>List of species examined in this study and the sources of the genes. Source key: WGS: Sequences derived from NCBI's Whole Genome Shotgun database, with accession prefix provided; Whole Genome Sequencing of Short Reads: whole genomes were sequenced using short-read technologies. The methodologies varied for the species, and will be or have been published with other projects, so please contact the author(s) for information on the specific methodology and samples used (Xenarthrans, <em>Proteles cristatus</em>, <em>Otocyon megalotis</em>: Frédéric Delsuc, e-mail: Frederic.Delsuc@umontpellier.fr; Crocodylians: John Gatesy, e-mail: jgatesy@amnh.org; <em>Dugong dugon</em>: Mark Springer, e-mail: mark.springer@ucr.edu; SRA: sequences derived from NCBI's Sequence Read Archive; GenBank: sequences derived from NCBI's nucleotide collection; Bowhead Whale Genome Resource: sequences derived from http://www.bowhead-whale.org; Ensembl: sequences derived from Ensembl genome browser (www.ensembl.org)l; Discovar de novo: sequences derived genomes assembled via Discovar de novo (<a href="https://software.broadinstitute.org/software/discovar/blog/">https://software.broadinstitute.org/software/discovar/blog/</a>). Coverage: indicates coverage of the whole genome (reported in NCBI or other source) or individual genes (derived from short read mapping). Scaffold and contig N50: reported in NCBI or other source.</p> <p><strong>- Supplementary_Table_S2.xlsx: </strong>Accession numbers and functionality of <em>AANAT</em> in species examined. If Accession # indicated as “New”, sequence generated for this study and can be found in Supplementary Dataset S1. Parentheses after accession number indicates coordinates for sequence on the contig / scaffold. Exon colors code for the following: green = putatively functional; yellow = missing (e.g., negative BLAST results, negative mapping results); pink = one or more inactivating mutations found. Abbreviations for mutations are as follows: del = deletion; ins = insertion; start = start codon mutation; stop = premature stop codon; ? = ambiguity whether the mutation is shared among all members of the clade. Abbreviations in brackets following an inactivating mutation indicate shared inactivating mutation. Key for each abbreviation follows: Bacu = <em>Balaenoptera acutorostrata</em>; BALA = Balaenidae; BALAEN = Balaenopteridae; Bbon = <em>Balaenoptera bonaerensis</em>; CAB = <em>Cabassous</em>; Ccap = <em>Cebus capucinus</em>; CETA = Cetacea; CHLAM = Chlamyphoridae; CHOL = <em>Choloepus</em>; Cjac = <em>Callithrix jacchus</em>; CING = Cingulata; DASY = Dasypodidae; DELP = Delphinidae; DERM = Dermoptera; Erob = <em>Eschrichtius robustus</em>; INIA = <em>Inia</em>; FOLI = Folivora; GALE = <em>Galeopterus</em>; LIPO = <em>Lipotes</em>; Lobl = <em>Lagenorhynchus obliquidens</em>; MANI = Manidae; MONO = Monodontidae; MYRM = Myrmecophagidae; MYST = Mysticeti; NPP = Not present in <em>Platanista</em> or Physeteroidea, but present in other Odontocetes; NPZ = Not present in Ziphiidae, but present in other Odontocetes; Oorc = <em>Orcinus orca</em>; PEUT = Tolypeutinae; PHOC = Phocoenidae; PHOL = Pholidota; PHOR = Chlamyphorinae; PILO = Pilosa; PHYS = Physeteroidea; PONT = <em>Pontoporia</em>; Schi = <em>Sousa chinensis</em>; SIRE = Sirenia; Tadu = <em>Tursiops aduncus</em>; TOLY = <em>Tolypeutes</em>; VERM = Vermilingua; XEN = Xenarthra.</p> <p><br> <strong>- Supplementary_Table_S3.xlsx: </strong>Accession numbers and functionality of <em>ASMT</em> in species examined. See Table S2 caption for details.</p> <p><strong>- Supplementary_Table_S4.xlsx: </strong>Accession numbers and functionality of <em>MTNR1A</em> in species examined. See Table S2 caption for details.</p> <p><strong>- Supplementary_Table_S5.xlsx: </strong>Accession numbers and functionality of <em>MTNR1B</em> in species examined. See Table S2 caption for details.</p> <p><strong>- Supplementary_Table_S6.xlsx: </strong>Codon frequency model selection. These are the results from one ratio dN/dS analyses using different codon frequency models. AIC = Akaike Information Criterion.</p> <p><strong>- Supplementary_Table_S7.xlsx: </strong>Results of <em>AANAT</em> PAML dN/dS analyses for mammals. Model: BG = branch(es) grouped with background; fixed 1 = branch(es) fixed at 1. p’-value: p-value after Holm-Bonferroni correction for multiple testing. Model Comparison: if model comparison yields statistically significant differences (p < 0.05), model comparison bolded and given green background; if model comparison is still significant after Holm-Bonferroni correction, asterisk (*) added. For most models, w only shown for branch(es) of interest. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S1.</p> <p><strong>- Supplementary_Table_S8.xlsx: </strong>Results of <em>ASMT</em> PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S2.</p> <p><strong>- Supplementary_Table_S9.xlsx: </strong>Results of <em>MTNR1A</em> PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S3.</p> <p><strong>- Supplementary_Table_S10.xlsx: </strong>Results of <em>MTNR1B</em> PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S4.</p> <p><strong>- Supplementary_Table_S11.xlsx: </strong>Results of PAML analyses for sauropsids.</p> <p><strong>- Supplementary_Table_S12.xlsx: </strong>Results of BLASTing and mapping short reads from <em>Alligator mississippiensis</em> RNA sequencing experiments.</p> <p><strong>- Supplementary_Table_S13.xlsx: </strong>Supporting data for validating putative inactivating mutations. Validating data came from four general sources of information: mutations shared by more than one species within a clade, mutations shared by two sources of sequencing data for the same species, mutations validated by coverage of mapped short reads and statistically elevated dN/dS ratio estimates. For additional details, see Supplementary Tables S2–S5 and S7–S10, as well as Figure 2 and Supplementary Figures S8–S18.</p> <p><strong>- Supplementary_Dataset_S1.txt:</strong><strong> </strong>Genomic alignments in fasta format used to determine the pseudogene/functional status of all four melatonin genes in different taxonomic groups.</p> <p><strong>- Supplementary_Dataset_S2.txt:</strong><strong> </strong>Alignment of <em>AANAT</em> in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML. </p> <p><strong>- Supplementary_Dataset_S3.txt: </strong>Alignment of <em>ASMT</em> in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML. </p> <p><strong>- Supplementary_Dataset_S4.txt: </strong>Alignment of <em>MTNR1A</em> and <em>MTNR1B</em> in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML. </p> <p><strong>- Supplementary_Dataset_S5.txt:</strong><strong> </strong>Codon alignments of <em>AANAT</em> used in selection pressure analyses with PAML. </p> <p><strong>- Supplementary_Dataset_S6.txt: </strong>Codon alignments of <em>ASMT</em> used in selection pressure analyses with PAML.</p> <p><strong>- Supplementary_Dataset_S7.txt:</strong><strong> </strong>Codon alignments of <em>MTNR1A</em> used in selection pressure analyses with PAML.</p> <p><strong>- Supplementary_Dataset_S8.txt: </strong>Codon alignments of <em>MTNR1B</em> used in selection pressure analyses with PAML.</p> <p><strong>- Supplementary_Dataset_S9.txt: </strong>Tree topologies in newick format used in selection pressure analyses with PAML.</p>
Additional evidence for a pulsar wind nebula in SN 1987A from multi-epoch X-ray data and MHD modelling
<p>This is a basic reproduction package for the paper "Additional evidence for a pulsar wind nebula in the hearth of sN 1987A from multi-epoch X-ray data and MHD modeling" by Greco et al. 2022. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Citation network data sets for 'Oxytocin – a social peptide? Deconstructing the evidence'
<p><strong>Introduction</strong></p> <p>This note describes the data sets used for all analyses contained in the manuscript 'Oxytocin - a social peptide?’<a href="#_ftn1">[1]</a> </p> <p><strong>Data Collection</strong></p> <p>The datasets described here were originally retrieved from Web of Science (WoS) Core Collection via the University of Edinburgh’s library subscription <a href="#_ftn2">[2]</a>. The aim of the original study for which these data were gathered was to survey peer-reviewed primary studies on oxytocin and social behaviour. To capture relevant papers, we used the following query:</p> <p><em>TI = (“oxytocin” OR “pitocin” OR “syntocinon”) AND TS = (“social*” OR “pro$social” OR “anti$social”)</em></p> <p>The final search was performed on the 13 September 2021. This returned a total of 2,747 records, of which 2,049 were classified by WoS as ‘articles’. Given our interest in primary studies <em>only</em> – articles reporting original data – we excluded all other document types. We further excluded all articles sub-classified as ‘book chapters’ or as ‘proceeding papers’ in order to limit our analysis to primary studies published in peer-reviewed academic journals. This reduced the set to 1,977 articles. All of these were published in the English language, and no further language refinements were unnecessary.</p> <p>All available metadata on these 1,977 articles was exported as plain text ‘flat’ format files in four batches, which we later merged together via Notepad++. Upon manually examination, we discovered examples of papers classified as ‘articles’ by WoS that were, in fact, reviews. To further filter our results, we searched all available PMIDs in PubMed (1,903 had associated PMIDs - ~96% of set). We then filtered results to identify all records classified as ‘review’, ‘systematic review’, or ‘meta-analysis’, identifying 75 records <a href="#_ftn3">[3]</a> (thus, ~4% of records classified by WoS were classified as reviews in PubMed). After examining a sample and agreeing with the PubMed classification, these were removed these from our dataset - leaving a total of 1,902 articles.</p> <p>From these data, we constructed two datasets via parsing out relevant reference data via the Sci2 Tool <a href="#_ftn4">[4]</a>. First, we constructed a ‘node-attribute-list’ by first linking unique reference strings (‘Cite Me As’ column in WoS data files) to unique identifiers, we then parsed into this dataset information on the identify of a paper, including the title of the article, all authors, journal publication, year of publication, total citations as recorded from WoS, and WoS accession number. Second, we constructed an ‘edge-list’ that records the citations from a <em>citing paper</em> in the ‘Source’ column and identifies the <em>cited paper</em> in the ‘Target’ column, using the unique identifies as described previously to link these data to the node-attribute-list.</p> <p>We then constructed a network in which papers are nodes, and citation links between nodes are directed edges between nodes. We used Gephi Version 0.9.2 <a href="#_ftn5">[5]</a> to manually clean these data by merging duplicate references that are caused by different reference formats or by referencing errors. To do this, we needed to retain both all retrieved records (1,902) as well as including <em>all</em> of their references to papers whether these were included in our original search or not. In total, this produced a network of 46,633 nodes (unique reference strings) and 112,520 edges (citation links). Thus, the average reference list size of these articles is ~59 references. The mean indegree (within network citations) is 2.4 (median is 1) for the entire network reflecting a great diversity in referencing choices among our 1,902 articles.</p> <p>After merging duplicates, we then restricted the network to include <em>only</em> articles fully retrieved (1,902), and retrained <em>only</em> those that were connected together by citations links in a large interconnected network (i.e. the largest component). In total, 1,892 (99.5%) of our initial set were connected together via citation links, meaning a total of ten papers were removed from the following analysis – and these were neither connected to the largest component, nor did they form connections with one another (i.e. these were ‘isolates’).</p> <p>This left us with a network of 1,892 nodes connected together by 26,019 edges. <strong><em>It is this network that is described by the ‘node-attribute-list’ and ‘edge-list’ provided here</em></strong>. This network has a mean in-degree of 13.76 (median in-degree of 4). By restricting our analysis in this way, we lose 44,741 unique references (96%) and 86,501 citations (77%) from the full network, but retain a set of articles tightly knitted together, all of which have been fully retrieved due to possessing certain terms related to oxytocin AND social behaviour in their title, abstract, or associated keywords.</p> <p>Before moving on, we calculated indegree for all nodes in this network – this counts the number of citations to a given paper from other papers within this network – and have included this in the <em>node-attribute-list</em>. We further clustered this network via modularity maximisation via the Leiden algorithm <a href="#_ftn6">[6]</a>. We set the algorithm to resolution 1, and allowed the algorithm to run over 100 iterations and 100 restarts. This gave <em>Q</em>=0.43 and identified seven clusters, which we describe in detail within the body of the paper. We have included cluster membership as an attribute in the node-attribute-list.</p> <p>For additional analysis, we also analysed the full reference list data to examine the most commonly cited references between 2016 and 2021 - the results of this are described in OTSOC_Cited_2016-2021.csv. This takes the reference lists of all retrieved papers within the network and examines their full reference lists (including references to other papers not contained within the network). These data were cleaned by matching DOIs and manual cleansing. </p> <p><strong>Data description</strong></p> <p>We include here two network datasets: (i) ‘OTSOC-node-attribute-list.csv’ consists of the attributes of 1,892 primary articles retrieved from WoS that include terms indicating a focus on oxytocin and social behaviour; (ii) ‘OTSOC-edge-list.csv’ records the citations between these papers. Together, these can be imported into a range of different software for network analysis; however, we have formatted these for ease of upload into Gephi 0.9.2. Finally, we include (iii) 'OTSOC_Cited_2016-2021' that lists all papers cited by >10 papers in the OTSOC network following any analysis of the bibliographies of retrieved papers. Below, we detail their contents:</p> <p><strong>1. ‘OTSOC-node-attribute-list.csv’</strong> is a comma-separate values file that contains all node attributes for the citation network (n=1,892) analysed in the paper. The columns refer to:</p> <p><em>Id</em>, the unique identifier</p> <p><em>Label</em>, the reference string of the paper to which the attributes in this row correspond. This is taken from the ‘Cite Me As’ column from the original WoS download. The reference string is in the following format: last name of first author, publication year, journal, volume, start page, and DOI (if available). </p> <p><em>Wos_id</em>, unique Web of Science (WoS) accession number. These can be used to query WoS to find further data on all papers via the ‘UT= ’ field tag.</p> <p><em>Title</em>, paper title.</p> <p><em>Authors</em>, all named authors.</p> <p><em>Journal, </em>journal of publication.</p> <p><em>Pub_year</em>, year of publication.</p> <p><em>Wos_citations</em>, total number of citations recorded by WoS Core Collection to a given paper as of 13 September 2021</p> <p><em>Indegree</em>, the number of within network citations to a given paper, calculated for the network shown in Figure 1 of the manuscript.</p> <p><em>Cluster</em>, provides the cluster membership number as discussed within the manuscript (Figure 1). This was established via modularity maximisation via the Leiden algorithm (Res 1; Q=0.43|7 clusters)</p> <p><strong>2. ‘OTSOC-edge -list.csv’</strong> is a comma-separated values file that contains all citation links between the 1,892 articles (n=26,019). The columns refer to:</p> <p><em>Source</em>, the unique identifier of the citing paper.</p> <p><em>Target, </em>the unique identifier of the cited paper.</p> <p><em>Type, </em>edges are ‘Directed’, and this column tells Gephi to regard all edges as such.</p> <p><em>Syr_date, </em>this contains the date of publication of the citing paper.</p> <p><em>Tyr_date, </em>this contains the date of publication of the cited paper.</p> <p><strong>3. 'OTSOC_Cited_2016-2021.csv'</strong> is a comma-separated values file that contain citations to all cited references that were cited by at least 10 of the retrieved papers within the OTSOC network published from 2016 onwards. The columns refer to: </p> <p><em>Reference, </em>the cited reference string extracted from the bibliographies of retrieved papers.</p> <p><em>Publication year, </em>the publication year of the cited reference.</p> <p><em>DOI</em>, the DOI of the cited reference. </p> <p><em>indegree_2016, </em>the total number of citations to a cited reference from papers published in 2016 and contained within the OTSOC network. </p> <p><em>indegree_2017, </em>the total number of citations to a cited reference from papers published in 2017 and contained within the OTSOC network. </p> <p><em>indegree_2018, </em>the total number of citations to a cited reference from papers published in 2018 and contained within the OTSOC network. </p> <p><em>indegree_2019, </em>the total number of citations to a cited reference from papers published in 2019 and contained within the OTSOC network. </p> <p><em>indegree_2020, </em>the total number of citations to a cited reference from papers published in 2020 and contained within the OTSOC network. </p> <p><em>indegree_2021, </em>the total number of citations to a cited reference from papers published in 2021 and contained within the OTSOC network. </p> <p><em>total indegree 2016-21</em>, the total number of citation to a cited reference from papers published between 2016-2021 and contained within the OTSOC network. </p> <p><strong>Software recommended for analysis</strong></p> <p>Gephi version 0.9.2 was used for the visualisations within the manuscript, and both files can be read and into Gephi without modification.</p> <p><strong>Notes</strong></p> <p><a href="#_ftnref1">[1]</a> Leng, G., Leng, R. I., Ludwig, M. (Submitted). Oxytocin – a social peptide? Deconstructing the evidence.</p> <p><a href="#_ftnref2">[2]</a> Edinburgh University’s subscription to Web of Science covers the following databases: (i) Science Citation Index Expanded, 1900-present; (ii) Social Sciences Citation Index, 1900-present; (iii) Arts & Humanities Citation Index, 1975-present; (iv) Conference Proceedings Citation Index- Science, 1990-present; (v) Conference Proceedings Citation Index- Social Science & Humanities, 1990-present; (vi) Book Citation Index– Science, 2005-present; (vii) Book Citation Index– Social Sciences & Humanities, 2005-present; (viii) Emerging Sources Citation Index, 2015-present.</p> <p><a href="#_ftnref3">[3]</a> For those interested, the following PMIDs were identified as ‘articles’ by WoS, but as ‘reviews’ by PubMed: ‘34502097’ ‘33400920’ ‘32060678’ ‘31925983’ ‘31734142’ ‘30496762’ ‘30253045’ ‘29660735’ ‘29518698’ ‘29065361’ ‘29048602’ ‘28867943’ ‘28586471’ ‘28301323’ ‘27974283’ ‘27626613’ ‘27603523’ ‘27603327’ ‘27513442’ ‘27273834’ ‘27071789’ ‘26940141’ ‘26932552’ ‘26895254’ ‘26869847’ ‘26788924’ ‘26581735’ ‘26548910’ ‘26317636’ ‘26121678’ ‘26094200’ ‘25997760’ ‘25631363’ ‘25526824’ ‘25446893’ ‘25153535’ ‘25092245’ ‘25086828’ ‘24946432’ ‘24637261’ ‘24588761’ ‘24508579’ ‘24486356’ ‘24462936’ ‘24239932’ ‘24239931’ ‘24231551’ ‘24216134’ ‘23955310’ ‘23856187’ ‘23686025’ ‘23589638’ ‘23575742’ ‘23469841’ ‘23055480’ ‘22981649’ ‘22406388’ ‘22373652’ ‘22141469’ ‘21960250’ ‘21881219’ ‘21802859’ ‘21714746’ ‘21618004’ ‘21150165’ ‘20435805’ ‘20173685’ ‘19840865’ ‘19546570’ ‘19309413’ ‘15288368’ ‘12359512’ ‘9401603’ ‘9213136’ ‘7630585’</p> <p><a href="#_ftnref4">[4]</a> 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></p> <p><a href="#_ftnref5">[5]</a> 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. Gephi is available via <a href="https://gephi.org/">https://gephi.org/</a></p> <p><a href="#_ftnref6">[6]</a> Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233. <a href="https://doi.org/10.1038/s41598-019-41695-z">https://doi.org/10.1038/s41598-019-41695-z</a></p>
Evidence of a Vulnerable Marine Ecosystem documented via tourist submarine off Cape Well-Met, Vega Island, Eastern Antarctic Peninsula (Subarea 48.1) - Multimedia
<p>Video evidence of a Vulnerable Marine Ecosystem (VME) was collected via submarine deployed by the tourist super-yacht MY Scenic Eclipse flagged with Malta. The dive was conducted on the 29th November 2019 within Subarea 48.1. The video of this resource supplements the dataset "Evidence of a Vulnerable Marine Ecosystem documented via tourist submarine off Cape Well-Met, Vega Island, Eastern Antarctic Peninsula (Subarea 48.1) - data'' available at <a href="https://ipt.biodiversity.aq/resource?r=cape-well-met_2019">https://ipt.biodiversity.aq/resource?r=cape-well-met_2019</a>.</p> <p>Method step description:</p> <ol> <li> <p>Video evidence of a Vulnerable Marine Ecosystem (VME) was collected via submarine deployed by the tourist super-yacht MY Scenic Eclipse flagged with Malta. Recordings begin at the greatest depth and continue as the submarine travels up the wall. Footage was taken with a GoPro Hero 7 Black mounted in the pilot window of a U-Boat Worx Cruise Sub 7-300<a href="https://www.uboatworx.com/model/cruisesub"> (https://www.uboatworx.com/model/cruisesub).</a> Four submarine dives were filmed.</p> </li> <li> <p>Prior to footage clean-up it was decided that the longest resulting video would be the one that would be analysed. Footage of each of these dives were provided in multiple files.</p> </li> <li> <p>Final Cut Pro X was first used to join the files into one video file per dive.</p> </li> <li> <p>The videos were then cropped to remove the edge of the pilot’s window frame and to adjust the colour balance.</p> </li> <li> <p>Clean-up then followed the same methodology as was used for analyzing the submarine footage for the successful nomination of four VMEs in WG-EMM-18/35 to remove unusable sequences. For the Cape Well-Met footage that meant the removal of any sequences where the submarine was too far from the wall, where the visibility was poor and when the submarine was paused.</p> </li> <li> <p>Footage from Dive C was the longest resulting video after the completion of this clean-up procedure, thus it became the footage that was analysed.</p> </li> </ol> <p>This project is funded by The Soap and The Sea, a Swiss organic and ocean-friendly soap enterprise that donates half of its profits to Ocean Conservation initiatives.</p>
Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - images
<p>This resource contains images that are framegrabs from video recorded by submarine deployed by the MY Arctic Sunrise during their Antarctica expeditions. The first took place in 2018 and focused within the Gerlache Strait and along the western Antarctic Peninsula and the Antarctic Sound in January 2018. Dives were conducted beginning 19th to 27th January 2018. This resource supplement the images for “Vulnerable Marine Ecosystem Indicator Taxa recorded by submarine as evidence of the presence of Vulnerable Marine Ecosystems, Antarctic Peninsula - data”</p>
Evidence of dual Shapiro steps in a Josephson junction array
<p>QCodes type databases containing raw data associated with the paper "Evidence of dual Shapiro steps in a Josephson junctions array" by N. Crescini, S. Cailleaux et al. acquired in the Institut Neel, CNRS, Grenoble, France between January 2022 and June 2022.</p> <p>There are two databases: one contains the characterization of the sample without microwave pump and the other one contains the study of the sample under microwave irradiation.</p> <p>Two Jupyter notebooks (python 3.8.11) are provided to analyze the datasets contained in the databases and reproduce the results of the article.</p> <p>For any additional information please contact: nicolo.crescini@neel.cnrs.fr or samuel.cailleaux@neel.cnrs.fr</p> <p> </p>
Merging Morphological and Genetic Evidence to assess hybridization in Eurasian Late Pleistocene hominins
<pre>Previous scientific consensus saw human evolution as defined by adaptive differences (behavioural and/or biological) and the emergence of Homo sapiens as the ultimate replacement of non-modern groups by a modern, adaptively more competitive one. However, recent research has shown that the process underlying our origins was considerably more complex. While archaeological and fossil evidence suggests that behavioural complexity may not be confined to the modern human lineage, recent paleogenomic work shows that gene flow between distinct lineages (e.g., Neanderthals, Denisovans, early H. sapiens) occurred repeatedly in the Late Pleistocene, likely contributing elements to our genetic make-up that might have been crucial to our success as a diverse, adaptable species. Following these advances, the prevailing human origins model has shifted from one of near-complete replacement to a more nuanced view of partial replacement with considerable reticulation. Here we provide a brief introduction to the current genetic evidence for hybridization among hominins, its prevalence in, and effects on, comparative mammal groups, and especially how it manifests in the skull. We then explore the degree to which cranial variation seen in the fossil record of Late Pleistocene hominins from Western Eurasia corresponds with our current genetic and comparative data. We are especially interested in understanding the degree to which skeletal data can reflect admixture. Our findings indicate some correspondence between these different lines of evidence, flag individual fossils as possibly admixed, and suggest that different cranial regions may preserve hybridisation signals differentially. We urge further studies of the phenotype in order to expand our ability to detect the ways in which migration, interaction and genetic exchange have shaped the human past, beyond what is currently visible with the lens of ancient DNA. </pre>
A tectonic model reconciling evidence for the collisions between India, Eurasia and intra-oceanic arcs of the central-eastern Tethys
<p>2 February 2014<br>Version 1.2</p> <p>Supplement and plate model accompanying: <br>Gibbons, A., Zahirovic, S., Müller, R., Whittaker, J., and Yatheesh, V., 2015, A tectonic model reconciling evidence for the collisions between India, Eurasia and intra-oceanic arcs of the central-eastern Tethys: Gondwana Research FOCUS.</p> <p><strong>Gibbons_etal_2015_GR_PlateModel.zip</strong></p> <p>This directory contains four files:</p> <p>TPW_CK95G94_Rigid_Gibbons.rot - the Gibbons et al. global rotation model TPW_CK95G94_PP_Rigid_Gibbons.gpml - the Gibbons et al. global evolving topologies <br>CK95G94_Coastlines.gpmlz - Present-day coastlines with Plate ID assignments<br>CK95G94_StaticPolygons_Gibbons.gpmlz - Present-day block outlines </p> <p>To load these datasets in GPlates do the following:</p> <p>1. Open GPlates<br>2. Pull down the GPlates File menu and select the operation Open Feature Collection<br>3. Click all the files while holding down the shift key to select all files. All the files should be highlighted.<br>4. Click Open </p> <p>Alternatively, drag and drop the files onto the globe.<br> <br>Play around with the GPlates buttons to make an animation, select features, draw features, etc. For more information, read the GPlates manual which can be downloaded from <a href="https://www.gplates.org">www.gplates.org</a></p> <p><strong>Zahirovic_etal_2014_SE</strong></p> <p>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Zahirovic, S., Seton, M., & Müller, R. D. (2014). The Cretaceous and Cenozoic tectonic evolution of Southeast Asia. Solid Earth, 5(1), 227-273. doi:<a href="https://doi.org/10.5194/se-5-227-2014" target="_blank" rel="noopener">10.5194/se-5-227-2014</a></p> <p>Any questions, please email: <br>Ana Gibbons <angi@statoil.com><br>Sabin Zahirovic <sabin.zahirovic@sydney.edu.au></p>
Supplementary table 1 for 'Imperial timber? Dendrochronological evidence for large-scale road building along the Roman limes in the Netherlands' (2015)
<p>This supplementary table to Visser(2015) was not openly available. This dataset provides the supplementary table in the open ODS-format and also as XLS and CSV.</p> <div> <div>Publication: Visser, RM. 2015 Imperial timber? Dendrochronological evidence for large-scale road building along the Roman limes in the Netherlands. <em>Journal of Archaeological Science</em> 53: 243–254. DOI: <a href="https://doi.org/10.1016/j.jas.2014.10.017">https://doi.org/10.1016/j.jas.2014.10.017</a>.</div> </div>
Dataset for 'A Matter of Culture? Conceptualising and Investigating 'Evidence Cultures' within Research on Evidence-Informed Policymaking'
<p><strong><span>Introduction</span></strong><strong><span><br></span></strong><span>This document describes the data collection and datasets used in the manuscript "A Matter of Culture? Conceptualising and Investigating ‘Evidence Cultures’ within Research on Evidence-Informed Policymaking" <span>[1].</span></span></p> <p><strong><span>Data Collection</span></strong></p> <p><span>To construct the citation network analysed in the manuscript, we first designed a series of queries to capture a large sample of literature exploring the relationship between evidence, policy, and culture from various perspectives. Our team of domain experts developed the following queries based on terms common in the literature. These queries search for the terms included in the titles, abstracts, and associated keywords of WoS indexed records (i.e. ‘TS=’). While these are separated below for ease of reading, they combined into a single query via the OR operator in our search. Our search was conducted on the Web of Science’s (WoS) Core Collection through the University of Edinburgh Library subscription on 29/11/2023, returning a total of <strong><u>2,089 records</u></strong>.</span></p> <p><em><span>TS = ((“cultures of evidence” OR “culture of evidence” OR “culture of knowledge” OR “cultures of knowledge” OR “research culture” OR “research cultures” OR “culture of research” OR “cultures of research” OR “epistemic culture” OR “epistemic cultures” OR “epistemic community” OR “epistemic communities” OR “epistemic infrastructure” OR “evaluation culture” OR “evaluation cultures” OR “culture of evaluation” OR “cultures of evaluation” OR “thought style” OR “thought styles” OR “thought collective” OR “thought collectives” OR “knowledge regime” OR “knowledge regimes” OR “knowledge system” OR “knowledge systems” OR “civic epistemology” OR “civic epistemologies”) AND (“policy” OR “policies” OR “policymaking” OR “policy making” OR “policymaker” OR “policymakers” OR “policy maker” OR “policy makers” OR “policy decision” OR “policy decisions” OR “political decision” OR “political decisions” OR “political decision making”))</span></em></p> <p><em><span>OR</span></em></p> <p><em><span>TS = ((“culture” OR “cultures”) AND ((“evidence-based” OR “evidence-informed” OR “evidence-led” OR “science-based” OR “science-informed” OR “science-led” OR “research-based” OR “research-informed” OR “evidence use” OR “evidence user” OR “evidence utilisation” OR “evidence utilization” OR “research use” OR “researcher user” OR “research utilisation” OR “research utilization” OR “research in” OR “evidence in” OR “science in”) NEAR/1 (“policymaking” OR “policy making” OR “policy maker” OR “policy makers”)))</span></em></p> <p><em><span>OR</span></em></p> <p><em><span>TS = ((“culture” OR “cultures”) AND (“scientific advice” OR “technical advice” OR “scientific expertise” OR “technical expertise” OR “expert advice”) AND (“policy” OR “policies” OR “policymaking” OR “policy making” OR “policymaker” OR “policymakers” OR “policy maker” OR “policy makers” OR “political decision” OR “political decisions” OR “political decision making”))<span> </span></span></em></p> <p><em><span>OR</span></em></p> <p><em><span>TS = ((“culture” OR “cultures”) AND (“post-normal science” OR “trans-science” OR “transdisciplinary” OR “transdisiplinarity” OR “science-policy interface” OR “policy sciences” OR “sociology of knowledge” OR “sociology of science” OR “knowledge transfer” OR “knowledge translation” OR “knowledge broker” OR “implementation science” OR “risk society”) AND (“policymaking” OR “policy making” OR “policymaker” OR “policymakers” OR “policy maker” OR “policy makers”))</span></em></p> <p><strong><span>Citation Network Construction</span></strong></p> <p><span>All bibliographic metadata on these 2,089 records were downloaded in five batches in plain text and then merged in R. We then parsed these data into network readable files. All unique reference strings are given unique node IDs. A node-attribute-list (‘CE_Node’) links identifying information of each document with its node ID, including authors, title, year of publication, journal WoS ID, and WoS citations. An edge-list (‘CE_Edge’) records all citations from these documents to their bibliographies – with edges going <em>from</em> a citing document <em>to</em> the cited – using the relevant node IDs. These data were then cleaned by (a) matching DOIs for reference strings that differ but point to the same paper, and (b) manual merging of obvious duplicates caused by referencing errors.</span></p> <p><span>Our initial dataset consisted of 2,089 <em>retrieved</em> documents and 123,772 <em>unretrieved</em> cited documents (i.e. documents that were cited within the publications we retrieved but which were not one of these 2,089 documents). These documents were connected by 157,229 citation links, but ~87% of the documents in the network were cited just once. To focus on relevant literature, we filtered the network to include <em>only</em> documents with at least three citation or reference links. We further refined the dataset by focusing on the main connected component, resulting in 6,650 nodes and 29,198 edges. <strong><u>It is this dataset that we publish here</u></strong>, and it is this network that underpins Figure 1, Table 1, and the qualitative examination of documents (see manuscript for further details). </span></p> <p><span>Our final network dataset contains 1,819 of the documents in our original query (~87% of the original retrieved records), and 4,831 documents not retrieved via our Web of Science search but cited by at least three of the retrieved documents. We then clustered this network by modularity maximization via the Leiden algorithm <span>[2]</span>, detecting 14 clusters with Q=0.59. Citations to documents within the same cluster constitute ~77% of all citations in the network. </span></p> <p><strong><span>Citation Network Dataset Description</span></strong></p> <p><span>We include two network datasets: (i) ‘CE_Node.csv’ that contains 1,819 retrieved documents, 4,831 unretrieved referenced documents, making for a total of 6,650 documents (nodes); (ii)’CE_Edge.csv’ that records citations (edges) between the documents (nodes), including a total of 29,198 citation links. These files can be used to construct a network with many different tools, but we have formatted these to be used in Gephi 0.10<span>[3]</span>. </span></p> <p><strong><span>‘CE_Node.csv’</span></strong><span> is a comma-separate values file that contains two types of nodes: </span></p> <p><span><span>i.<span> </span></span></span><span>Retrieved documents – these are documents captured by our query. These include full bibliographic metadata and reference lists. </span></p> <p><span><span>ii.<span> </span></span></span><span>Non-retrieved documents – these are documents referenced by our retrieved documents but were not retrieved via our query. These only have data contained within their reference string (i.e. first author, journal or book title, year of publication, and possibly DOI). </span></p> <p><span>The columns in the .csv refer to:</span></p> <p><span><span>-<span> </span></span></span><em><span>Id</span></em><span>, the node ID</span></p> <p><span><span>-<span> </span></span></span><em><span>Label</span></em><span>, the reference string of the document</span></p> <p><span><span>-<span> </span></span></span><em><span>DOI</span></em><span>, the DOI for the document, if available</span></p> <p><span><span>-<span> </span></span></span><em><span>WOS_ID</span></em><span>, WoS accession number</span></p> <p><span><span>-<span> </span></span></span><em><span>Authors</span></em><span>, named authors</span></p> <p><span><span>-<span> </span></span></span><em><span>Title</span></em><span>, title of document</span></p> <p><span><span>-<span> </span></span></span><em><span>Document_type</span></em><span>, variable indicating whether a document is an article, review, etc.</span></p> <p><span><span>-<span> </span></span></span><em><span>Journal_book_title, </span></em><span>journal of publication or title of book</span></p> <p><span><span>-<span> </span></span></span><em><span>Publication year</span></em><span>, year of publication.</span></p> <p><span><span>-<span> </span></span></span><em><span>WOS_times_cited</span></em><span>, total Core Collection citations as of 29/11/2023</span></p> <p><span><span>-<span> </span></span></span><em><span>Indegree</span></em><span>, number of <strong><em>within</em></strong> network citations to a given document</span></p> <p><span><span>-<span> </span></span></span><em><span>Cluster</span></em><span>, provides the cluster membership number as discussed in the manuscript (Figure 1)</span></p> <p><strong><span>‘CE_Edge.csv’</span></strong><span> is a comma-separated values file that contains edges (citation links) between nodes (documents) (<em>n</em>=29,198). The columns refer to:</span></p> <p><span><span>-<span> </span></span></span><em><span>Source</span></em><span>, node ID of the <em>citing</em> document</span></p> <p><span><span>-<span> </span></span></span><em><span>Target, </span></em><span>node ID of the <em>cited</em> document</span></p> <p><strong><span>Cluster Analysis</span></strong></p> <p><span>We qualitatively analyse a set of publications from seven of the largest clusters in our manuscript. For this, we calculated the within cluster indegree of nodes, and read through the 10 most cited retrieved documents and 10 most cited unretrieved documents. To generate these lists, sub-graphs for each cluster needed to be generated, and then indegree was measured (i.e. counting the number of citations from papers within a cluster to other papers in that same cluster).</span></p> <p><strong><span>Notes</span></strong></p> <p><a href="https://zenodo.org/records/6615221#_ftnref1"><span>[1]</span></a><span> Bandola-Gill, J., Andersen, N., Leng, R. I., Pattyn, V., & Smith, K. E. (forthcoming). A Matter of Culture? Conceptualising and Investigating ‘Evidence Cultures’ within Research on Evidence-Informed Policymaking. Policy and Society</span></p> <p><a href="https://zenodo.org/records/6615221#_ftnref6"><span>[2]</span></a><span> Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233. </span><a href="https://doi.org/10.1038/s41598-019-41695-z"><span>https://doi.org/10.1038/s41598-019-41695-z</span></a></p> <p><a href="https://zenodo.org/records/6615221#_ftnref5"><span>[3]</span></a><span> 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. Gephi is available via </span><a href="https://gephi.org/"><span>https://gephi.org/</span></a></p> <p><span> </span></p>
Dataset for the manuscript Marmet, Wicki, Gmel, Gachoud, Daeppen, Bertholet, Studer (2021). The psychological impact of the COVID-19 crisis is higher among young Swiss men with a lower socioeconomic status: evidence from a cohort study. Plos One. DOI:10.1371/journal.pone.0255050
<p>Dataset for the manuscript Marmet, Wicki, Gmel, Gachoud, Daeppen, Bertholet, Studer (2021). The psychological impact of the COVID-19 crisis is higher among young Swiss men with a lower socioeconomic status: evidence from a cohort study. Plos One DOI:10.1371/journal.pone.0255050</p> <p>The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.