cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.n4288
title: Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales

description: 1. Multiscale codependence analysis (MCA) quantifies the joint spatial
 distribution of a pair of variables in order to provide a
 spatially-explicit assessment of their relationships to one another. For
 the sake of simplicity, the original definition of MCA only considered a
 single response variable (e.g. a single species). However, that definition
 would limit the application of MCA when many response variables are
 studied jointly, for example when one wants to study the effect of the
 environment on the spatial organisation of a multi-species community in an
 explicit manner. 2. In the present paper, we generalize MCA to multiple
 response variables. We conducted a simulation study to assess the
 statistical properties (i.e. type I error rate and statistical power) of
 multivariate MCA (mMCA) and found that it had honest type I error rate and
 sufficient statistical power for practical purposes, even with modest
 sample sizes. We also exemplified mMCA by applying it to two ecological
 data sets. 3. The simulation study confirmed the adequacy of mMCA from a
 statistical standpoint: it has honest type I error rates and sufficient
 power to be useful in practice. Using mMCA, we were able to detect
 variation in fish community structure along the Doubs River (in France),
 which was associated with large spatial structures in the variation of
 physical and chemical variables related to water quality. Also, mMCA
 usefully described the spatial variation of an Oribatid mite community
 structure associated with a gradient of water content superimposed on
 various smaller-scale spatial features associated with vegetation cover in
 the peat blanket surrounding Lac Geai (in Québec, Canada). 4. In addition
 to demonstrating the soundness of mMCA in theory and practice, we further
 discuss the strengths and assumptions of mMCA and describe other potential
 scenarios where it would be helpful to biologists interested in assessing
 influence of environmental conditions on community structure in a
 spatially-explicit way.
description: Script performing
 simulations and examples calculationsThe tarball (.tar.gz)
 archive contains the files to perform the simulation study outlined in the
 paper and calculate the application examples. R language code is commented
 in French and English. It uses MySQL for data management, the R language
 and environment for calculations, and a bash script to launch the
 simulation scripts. R package codep is used for MCA calculation and is
 available from the CRAN
 (https://cran.r-project.org/web/packages/codep/index.html). The tarball
 contains a README file explaining the purposes of the enclosed directories
 and files in addition to the R, SQL, and bash scripts.MS15 -
 Multi-codep.tar.gzR package
 codepA copy of the R package
 codep used to run the scripts. Up-to-date versions are available from the
 Comprehensive R Archive Network (CRAN) at
 https://cran.r-project.org/web/packages/codep/index.html
DOI: 10.6084/m9.figshare.30048583
title: Whale shark genome assembly sRhiTyp1.2

description: Whale shark genome assembly sRhiTyp1.2 reported in Kawaguchi et al. produced by Squalomix consortium
DOI: 10.5061/dryad.157c8
title: Data from: Time-lagged effect of predators on tadpole behaviour and parasite infection

description: Prey should adjust their defences against natural enemies to match their
 current level of risk and balance other needs. This is particularly
 important when optimal defences represent trade-offs, as is the case with
 many predator-induced trait-mediated indirect effects (TMIEs) that are
 antagonistic to those promoting host resistance to parasites and
 pathogens. However, trade-offs may depend on whether different natural
 enemies are present simultaneously or represent temporally discrete
 threats. We found that larval amphibians (Anaxyrus americanus) previously
 exposed to predator cues did not engage in anti-parasite behaviours
 (activity increases) in response to a current risk of infection by a
 pathogenic trematode parasite compared to controls, resulting in higher
 infection intensities. This suggests that the memory of the likely more
 lethal threat (predation) had greater influence, maladaptively dampening
 tadpole activity. Incorporating complexity inherent in natural systems,
 including spatial and temporal overlap, is necessary to better understand
 natural enemy ecology and how TMIEs relate to infectious diseases.
description: Koprivnikar &
 Urichuk Biol Letter 2017 Dryad dataLarval amphibian
 activity with/without predator cue exposure and parasite
 risk
DOI: 10.6084/m9.figshare.30048583.v1
title: Whale shark genome assembly sRhiTyp1.2

description: Whale shark genome assembly sRhiTyp1.2 reported in Kawaguchi et al. produced by Squalomix consortium
DOI: 10.6084/m9.figshare.30048583.v2
title: Whale shark genome assembly sRhiTyp1.2

description: Whale shark genome assembly sRhiTyp1.2 reported in Kawaguchi et al. produced by Squalomix consortium
DOI: 10.5061/dryad.2mv73
title: Data from: Causations of phylogeographic barrier of some rocky shore species along the Chinese coastline

description: Background: Substrate, ocean current and freshwater discharge are
 recognized as important factors that control the larval dispersal and
 recruitment of intertidal species. Life history traits of individual
 species will determine the differential responses to these physical
 factors, and hence resulting in contrasting phylogeography across the same
 biogeographic barrier. To determine how these factors affect genetic
 structure of rocky shore species along the China coast, a comparative
 phylogeographic study of four intertidal and subtidal species was
 conducted using mitochondrial and nuclear DNA by combining new sequences
 from Siphonaria japonica with previously published sequences from three
 species (Cellana toreuma, Sargassum horneri and Atrina pectinata).
 Results: Analysis of molecular variance and pairwise ΦST revealed
 significant genetic differences between the Yellow Sea (YS) and the other
 two marginal seas (East China Sea, ECS and South China Sea, SCS) for
 rocky-shore species (S. japonica, C. toreuma, S. horneri), but not for
 muddy-shore species Atrina pectinata. Demographic history analysis proved
 that the population size of all these four species were persistent though
 the Last Glacial Maximum (LGM, ~20 ka BP). Migration analysis
 revealed that gene flow differentiated northward and southward migration
 for these four species. However, the inferred direction of gene flow using
 alternatively mitochondrial or nuclear markers was contradictory in S.
 japonica. Conclusions: It is concluded that there is a phylogeographical
 break at the Yangtze River estuary for the rocky shore species and the
 causation of the barrier is mainly due to the unsuitable substratum and
 freshwater discharge. All four intertidal and subtidal species appear to
 have persisted through the LGM in China, indicating the lower impact of
 LGM on intertidal and subtidal species than generally anticipated. The
 imbalanced gene flow between YS and ESCS groups for these four species
 could be explained by historical refugia. The discordance between
 mitochondrial and nuclear markers in the MIGRATE analysis of S. japonica
 prove the importance of employing multi-locus data in biogeographic study.
 Climate change, land reclamation and dam construction, which are changing
 substrate and hydrological conditions around Yangtze River estuary, will
 consequently affect the biogeographic pattern of intertidal species.
description: Sequence alignments
 files of MEG formatincludes the COI and ITS
 alignment of Siphonaria japonica in the present study, the COI alginment
 of Cellana toreuma by Dong et al. 2012, the COI alignment of Atrina
 pectinata by Liu et al. 2011, and the COIII alignment of Sargassum horneri
 by Hu et al. 2011Alignment.rar
DOI: 10.15468/dl.jecrsa
title: Occurrence Download

description: A dataset containing 1281 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Sagittaria trifolia"
 ]
}

 The dataset includes 1281 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001407-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.6d57c
title: Data from: Positive biodiversity-productivity relationships in forests: climate matters

description: While it is widely acknowledged that forest biodiversity contributes to
 climate change mitigation through improved carbon sequestration,
 conversely how climate affects tree species diversity - forest
 productivity relationships is still poorly understood. We combined the
 results of long-term experiments where forest mixtures and corresponding
 monocultures were compared on the same site to estimate the yield of
 mixed-species stands at a global scale, and its response to climatic
 factors. We found positive mixture effects on productivity using a
 meta-analysis of 126 cases studies established at 60 sites spread across
 five continents. Overall, the productivity of mixed-species forests was
 15% greater than the average of their component monocultures, and not
 statistically lower than productivity of the best component monoculture.
 Productivity gains in mixed-species stands were not affected by tree age
 or stand species composition but significantly increased with local
 precipitation. The results should guide better use of tree species
 combinations in managed forests and suggest that increased drought
 severity under climate change might reduce the atmospheric carbon
 sequestration capacity of natural forests.
description: Jactel-Forest-Diversity-Productivity-Climate-meta-analysis-dataSummary of data included in the meta-analyses
DOI: 10.5061/dryad.ph602
title: Data from: Phylogeny of salmonids (Salmoniformes: Salmonidae) and its molecular dating: analysis of nuclear RAG1 gene

description: The phylogenetic relationships among 26 species of salmonid fishes (family
 Salmonidae) were studied using the RAG1 gene as phylogenetic marker. No
 unambiguous relationships between thymallins, coregonins, and salmonins it
 was possible to establish. It seems likely, that divergence of these
 lineages took place during rather short time interval (about 3 to 4
 million years). The thymallins are thought to be the first radiated
 lineage. The genera of the subfamily Salmoninae form two distinct
 monophyletic groups, represented by (1) Brachymystax and Hucho and (2)
 Salmo, Parahucho, Salvelinus, Parasalmo and Oncorhynchus. Ancestral forms
 of these two evolutionary lineages could diverge at the Oligocene-Miocene
 boundary (about 24 million years ago). It is suggested that
 diversification of the main lineages within the second group was rather
 rapid, and took place in middle Miocene (about 19-16 million years ago).
 Moreover, the lineages of Salvelinus, Parasalmo and Oncorhynchus were the
 latest to diverge. It seems likely that divergence of the Prosopium and
 Corergonus ancestral lineages occurred during the same time interval.
description: salmonids.RAG1.timetreeBayesian chronogram (NEXUS tree format) of salmonid cladogenesis
DOI: 10.17632/rswk8kyycm
title: Structure of Glaciozyma antarctica Arginase Reveals Cold-active Adaptation via Increased Flexible Access to the Active Site. Quay et al.

description: The data are the three-dimensional structure coordinates of a cold-active arginase from Glaciozyma antarctica, a psychrophilic yeast.
DOI: 10.17632/rswk8kyycm.1
title: Structure of Glaciozyma antarctica Arginase Reveals Cold-active Adaptation via Increased Flexible Access to the Active Site. Quay et al.

description: The data are the three-dimensional structure coordinates of a cold-active arginase from Glaciozyma antarctica, a psychrophilic yeast.
DOI: 10.15468/dl.rcbggk
title: Occurrence Download

description: A dataset containing 1664 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Miconia cooperi (Cogn.) Michelang."
 ]
}

 The dataset includes 1664 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021837-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5vkrqw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Corvus corax Linnaeus, 1758)",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021436-250525065834625/datasets/export for details.
DOI: 10.15468/dl.dt3sxj
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xenodermichthys copei (Gill, 1884)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((33.4566 -25.7816,35.1352 -25.1504,26.9648 -25.1504,26.9648 -34.0826,27.209 -34.0314,28.1962 -33.4486,30.3395 -31.6744,31.7308 -29.6968,32.7846 -28.846,33.3559 -27.1274,33.4566 -25.7816))"
 ]
}

 The dataset includes 10 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010543-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.thkfj9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Malva sylvestris",
 "Country is Angola",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025589-250811113504898/datasets/export for details.
DOI: 10.15468/dl.ae7479
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Malva sylvestris",
 "Country is Argentina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 66 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025590-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17611/dp/20323273
title: Global ShakeMovie synthetic for July 21, 2022 MW 5.9 MYANMAR-CHINA BORDER REGION
DOI: 10.17611/dp/20324783
title: Global ShakeMovie synthetic for July 22, 2022 MW 5.5 VANUATU ISLANDS
DOI: 10.17611/dp/20331462
title: Global ShakeMovie synthetic for July 23, 2022 MW 5.5 FLORES SEA
DOI: 10.17611/dp/20331466
title: Global ShakeMovie synthetic for July 23, 2022 MW 5.6 SOUTHERN IRAN
DOI: 10.17611/dp/20331470
title: Global ShakeMovie synthetic for July 25, 2022 MW 5.6 COLOMBIA-ECUADOR BORDER REGION
DOI: 10.17611/dp/20361273
title: Global ShakeMovie synthetic for July 27, 2022 MW 7.1 LUZON, PHILIPPINES
DOI: 10.17611/dp/20361277
title: Global ShakeMovie synthetic for July 27, 2022 MW 5.5 TONGA ISLANDS
DOI: 10.17611/dp/20363705
title: Global ShakeMovie synthetic for July 27, 2022 MW 5.8 SOUTH OF FIJI ISLANDS
DOI: 10.17611/dp/20363709
title: Global ShakeMovie synthetic for July 27, 2022 MW 5.6 KERMADEC ISLANDS REGION
DOI: 10.17611/dp/20363713
title: Global ShakeMovie synthetic for July 27, 2022 MW 6.2 NORTHERN CHILE
DOI: 10.17611/dp/20366106
title: Global ShakeMovie synthetic for July 28, 2022 MW 6.1 NEAR COAST OF NORTHERN CHILE
DOI: 10.17611/dp/20366145
title: Global ShakeMovie synthetic for July 28, 2022 MW 5.9 EASTERN NEW GUINEA REG., P.N.G.
DOI: 10.17611/dp/20371070
title: Global ShakeMovie synthetic for July 29, 2022 MW 5.5 RUSSIA-MONGOLIA BORDER REGION
DOI: 10.17611/dp/20371074
title: Global ShakeMovie synthetic for July 29, 2022 MW 5.6 OFF EAST COAST OF KAMCHATKA
DOI: 10.17611/dp/20377217
title: Global ShakeMovie synthetic for August 02, 2022 MW 5.4 MINDANAO, PHILIPPINES
DOI: 10.17611/dp/20377221
title: Global ShakeMovie synthetic for August 02, 2022 MW 5.7 FIJI ISLANDS REGION
DOI: 10.17611/dp/20379649
title: Global ShakeMovie synthetic for August 04, 2022 MW 5.8 SOUTHEAST OF HONSHU, JAPAN
DOI: 10.17611/dp/20382077
title: Global ShakeMovie synthetic for August 05, 2022 MW 5.8 KERMADEC ISLANDS, NEW ZEALAND
DOI: 10.17611/dp/20390927
title: Global ShakeMovie synthetic for August 08, 2022 MW 5.6 NEW BRITAIN REGION, P.N.G.
DOI: 10.17611/dp/20391675
title: Global ShakeMovie synthetic for August 06, 2022 MW 5.2 GULF OF CALIFORNIA
DOI: 10.17611/dp/20391679
title: Global ShakeMovie synthetic for August 07, 2022 MW 6.0 EAST OF KURIL ISLANDS
DOI: 10.17611/dp/20391683
title: Global ShakeMovie synthetic for August 09, 2022 MW 5.7 OFF COAST OF SOUTHERN CHILE
DOI: 10.17611/dp/20397142
title: Global ShakeMovie synthetic for August 13, 2022 MW 6.0 EASTERN NEW GUINEA REG., P.N.G.
DOI: 10.17611/dp/20397146
title: Global ShakeMovie synthetic for August 14, 2022 MW 5.8 NORTHERN MOLUCCA SEA
DOI: 10.17611/dp/20397150
title: Global ShakeMovie synthetic for August 14, 2022 MW 5.9 VANUATU ISLANDS
DOI: 10.17611/dp/20397154
title: Global ShakeMovie synthetic for August 14, 2022 MW 5.7 QINGHAI, CHINA
DOI: 10.17611/dp/20397158
title: Global ShakeMovie synthetic for August 14, 2022 MW 6.5 SOUTH OF KERMADEC ISLANDS
DOI: 10.17611/dp/20397162
title: Global ShakeMovie synthetic for August 14, 2022 MW 6.3 SOUTHEAST OF LOYALTY ISLANDS
DOI: 10.17611/dp/20397166
title: Global ShakeMovie synthetic for August 15, 2022 MW 5.6 MINDANAO, PHILIPPINES
DOI: 10.17611/dp/20397170
title: Global ShakeMovie synthetic for August 15, 2022 MW 5.4 ASCENSION ISLAND REGION
DOI: 10.17611/dp/20397174
title: Global ShakeMovie synthetic for August 16, 2022 MW 5.5 MACQUARIE ISLAND REGION
DOI: 10.17611/dp/20397922
title: Global ShakeMovie synthetic for August 17, 2022 MW 5.6 OFF EAST COAST OF HONSHU, JAPAN
DOI: 10.17611/dp/20398676
title: Global ShakeMovie synthetic for August 17, 2022 MW 5.6 BANDA SEA
DOI: 10.17611/dp/20416926
title: Global ShakeMovie synthetic for August 19, 2022 MW 5.4 SOUTHERN MID-ATLANTIC RIDGE
DOI: 10.17611/dp/20418869
title: Global ShakeMovie synthetic for August 21, 2022 MW 5.5 NEAR COAST OF ECUADOR
DOI: 10.17611/dp/20418873
title: Global ShakeMovie synthetic for August 22, 2022 MW 5.5 SOUTH OF BALI, INDONESIA
DOI: 10.17611/dp/20419621
title: Global ShakeMovie synthetic for August 23, 2022 MW 6.3 SOUTHERN SUMATRA, INDONESIA
DOI: 10.17611/dp/20419625
title: Global ShakeMovie synthetic for August 23, 2022 MW 5.8 BALLENY ISLANDS REGION
DOI: 10.17611/dp/20420373
title: Global ShakeMovie synthetic for August 24, 2022 MW 5.5 NORTHERN CHILE
DOI: 10.17611/dp/20425330
title: Global ShakeMovie synthetic for August 28, 2022 MW 5.6 FIJI ISLANDS REGION
DOI: 10.17611/dp/20425334
title: Global ShakeMovie synthetic for August 28, 2022 MW 5.9 SOUTHERN SUMATRA, INDONESIA
DOI: 10.17611/dp/20425338
title: Global ShakeMovie synthetic for August 29, 2022 MW 6.2 SOUTHERN SUMATRA, INDONESIA
DOI: 10.17611/dp/20425342
title: Global ShakeMovie synthetic for August 29, 2022 MW 5.5 RYUKYU ISLANDS, JAPAN
DOI: 10.34740/kaggle/dsv/12817000
title: LinkedIn Job Market Analysis
DOI: 10.7910/dvn/t0ukcn
title: Replication Data for "Measuring and understanding parties' anti-elite strategies"

description: This repository contains code and data to replicate the analysis in the JOP article "Measuring and understanding parties' anti-elite strategies"

Paper Abstract:
This article presents a new measure and analysis of parties' anti-elite appeals. In order to measure parties' anti-elite appeals we apply crowd-sourced coding, supervised machine learning, and novel cross-lingual transfer learning techniques to parties' Twitter posts. Our dataset records quarterly estimates of parties' anti-elite strategies for 20 countries between 2008 and 2021. Based on these indicators, we analyze whether parties' anti-elite rhetoric reflects the potential costs and benefits of this electoral strategy. We find that mainstream parties use anti-elite rhetoric less frequently when they are more likely to be included in the next governing coalition. When challenger parties do well in the polls they become more anti-elitist. Our article not only contributes to the literature on democratic competition by introducing and applying a new measure of anti-elite strategies, but also outlines a novel, modular and scalable procedure to measure party appeals using social media posts.
DOI: 10.7910/dvn/ukw952
title: A16_31069.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/6a2670a2-a853-4224-b0ca-aae6322f39a4'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/6a2670a2-a853-4224-b0ca-aae6322f39a4
DOI: 10.7910/dvn/tzade8
title: Passed Acts; St. 1843, c.69, SC1/series 229

description: &lt;p&gt;Petition subject: Great Massachusetts Petition &lt;/p&gt; &lt;p&gt;Original: &lt;a href="http://nrs.harvard.edu/urn-3:FHCL:13448109"&gt;http://nrs.harvard.edu/urn-3:FHCL:13448109&lt;/a&gt; &lt;/p&gt; &lt;p&gt;Date of creation: (unknown) &lt;/p&gt; &lt;p&gt;Petition location: Bernardston &lt;/p&gt; &lt;p&gt;Total signatures: 59 &lt;/p&gt; &lt;p&gt;Unidentified signatures: 59 &lt;/p&gt; &lt;p&gt;Additional non-petition or unrelated documents available at archive: additional documents available &lt;/p&gt; &lt;p&gt;Additional archivist notes: George Latimer [note: this data reflects the information on the spreadsheet submitted by Henry I. Bowditch and William F. Channing to the legislature, which was filed with St. 1843, c.69, passed April 26, 1843. The majority of the Great Massachusetts Petition has not been located.] &lt;/p&gt; &lt;p&gt;Location of the petition at the Massachusetts Archives of the Commonwealth: St. 1843, c.69, passed April 26, 1843 &lt;/p&gt; &lt;p&gt;Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.&lt;/p&gt;
DOI: 10.7910/dvn/uiuv3u
title: Replication Data for: Urban-Rural Differences in Non-Voting Political Behaviors

description: R files and data for "Urban-Rural Differences in Non-Voting Political Behaviors". Recommend placing all files into the same folder and placing all .csv files into a folder called "Aux_Data/" for easier code use.