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BRAMS-SFIRE outputs to South of Portugal
<p>The BRAMS-SFIRE (Menezes, 2015) consists of a mesoscale atmospheric model, the Brazilian developments on the Regional Atmospheric Modelling System (BRAMS (Freitas et al., 2009; 2017)), coupled with the Spread Fire model (SFIRE) (Mandel et al., 2009; 2011). Here we presented the outputs of the simulations of this model for a domains of south of Portugal in some locations, namely, in Alcácer do Sal, Reguengos de Monsaraz and in Ossa mountain range. The data are grouped in netcdf simulations data and scripts GRADS, to images visualization of this outputs, for some ambient situation with forestry fire and without forestry fire, simulated for day 7th August 2006 (Ossa mountain range, around 7.54°W, 38.73°N), for day 4th July 2007 (Reguengos de Monsaraz, approximately 7.59°W, 38.37°N) and for day 28th July 2010 (Alcácer do Sal, around 8.69°W, 38.35°N).</p>
Survey data of "Mapping Research Output to the Sustainable Development Goals (SDGs)"
<p><strong>This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs).</strong></p> <p><a href="https://sustainabledevelopment.un.org/sdgs">Sustainable Development Goals</a> are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">Aurora SDG dashboard</a> is the initial outcome as proof of practice.)</p> <p>In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to<strong> capture expert knowledge from senior researchers in their research domain related to the SDG</strong>. The survey was open to the world, but mainly distributed to researchers from the <a href="https://aurora-network.global/">Aurora Universities Network</a>. <strong>The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America.</strong></p> <p>17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey.<strong> The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs</strong>.</p> <p><strong>The survey contains the following questions (see inside dataset for exact wording):</strong></p> <ul> <li><strong>Are you familiar with this SDG?</strong> <ul> <li>Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.</li> </ul> </li> <li><strong>Suggest research papers that are relevant for this SDG (upload list)</strong> <ul> <li>This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)</li> </ul> </li> <li><strong>Select research papers that are relevant for this SDG (radio buttons: accept, reject)</strong> <ul> <li>A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.</li> </ul> </li> <li><strong>Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions)</strong> <ul> <li>The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Suggest SDG related glossaries with relevant keywords (text fields: url)</strong> <ul> <li>Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions)</strong> <ul> <li>The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Suggest improvements for the current queries (text field: suggestions per target)</strong> <ul> <li>We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.</li> </ul> </li> </ul> <p><strong>In the dataset root you'll find the following folders and files:</strong></p> <ul> <li><strong>/00-survey-input/</strong> <ul> <li>This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.</li> </ul> </li> <li><strong>/01-raw-data/</strong> <ul> <li>This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.</li> </ul> </li> <li><strong>/02-aggregated-data/</strong> <ul> <li>This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.</li> </ul> </li> <li><strong>/03-scripts/</strong> <ul> <li>This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.</li> </ul> </li> <li><strong>/04-processed-data/</strong> <ul> <li>This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.</li> </ul> </li> <li><strong>/images/</strong> <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li><strong>LICENSE.md</strong> <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li><strong>README.md</strong> <ul> <li>description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.</li> </ul> </li> </ul> <p><strong>In the /04-processed-data/ you'll find in each SDG sub-folder the following files.:</strong></p> <ul> <li><strong>SDG-survey-questions.pdf</strong> <ul> <li>This file contains the survey questions</li> </ul> </li> <li><strong>SDG-survey-questions.doc</strong> <ul> <li>This file contains the survey questions</li> </ul> </li> <li><strong>SDG-survey-respondents-per-sdg.csv</strong> <ul> <li>Basic information about the survey and responses</li> </ul> </li> <li><strong>SDG-survey-city-heatmap.csv</strong> <ul> <li>Origin of the respondents per SDG survey</li> </ul> </li> <li><strong>SDG-survey-suggested-publications.txt</strong> <ul> <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-publications-with-eid-match.csv</strong> <ul> <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li> </ul> </li> <li><strong>SDG-survey-selected-publications-accepted.csv</strong> <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li> </ul> </li> <li><strong>SDG-survey-selected-publications-rejected.csv</strong> <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li> </ul> </li> <li><strong>SDG-survey-selected-keywords.csv</strong> <ul> <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-unselected-keywords.csv</strong> <ul> <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-keywords.csv</strong> <ul> <li>List of keywords researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-glossaries.csv</strong> <ul> <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-selected-journals.csv</strong> <ul> <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-unselected-journals.csv</strong> <ul> <li>As "selected-journals", this is the list of journals that respondents have not selected to represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-journals.csv</strong> <ul> <li>List of journals researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-suggested-query.csv</strong> <ul> <li>List of query improvements researchers suggest to use to find papers related to this SDG</li> </ul> </li> </ul> <p><strong>Cite as:</strong></p> <blockquote> <p><em>Survey data of "Mapping Research output to the SDGs"</em> by Aurora Universities Network (AUR) <a href="http://doi.org/10.5281/zenodo.3798385">doi:10.5281/zenodo.3798385</a></p> </blockquote> <p><strong>Attribute as:</strong></p> <blockquote> <p><em><strong>Survey data of "Mapping Research output to the SDGs</strong>"</em> by Aurora Universities Network (AUR); Alessandro Arienzo (UNA); Roberto Delle Donne (UNA); Ignasi Salvadó Estivill (URV); José Luis González Ugarte (URV); Didier Vercueil (UGA); Nykohla Strong (UAB); Eike Spielberg (UDE); Felix Schmidt (UDE); Linda Hasse (UDE); Ane Sesma (UEA); Baldvin Zarioh (UIC); Friedrich Gaigg (UIN); René Otten (VUA); Nicolien van der Grijp (VUA); Yasin Gunes (VUA); Peter van den Besselaar (VUA); Joeri Both (VUA); Maurice Vanderfeesten (VUA);<strong> is licensed under a Creative Commons Attribution 4.0 International License.</strong> <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/</a></p> </blockquote>
Mass spectrometry output SILAC labelled (F/Y) biological replicate 1 - anti-HLA-A29 antibody DK1G8
<p>Mass spectrometry output from SILAC labelled (F/Y) ERAP2 wildetype versus (CRISPR Cas9-mediated) ERAP2-KO lymphoblastoid cell line from a Birdshot Uveitis patient (ERAP1 hap10/10 ERAP2 hapA/A). Peptides were eluted from immuno-purifications with anti-HLA-A29 antibody DK1G8. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
Model outputs for update of occurrence and hunting yield-based data models for wild boar at European scale: new approach to handle the bioregion effect, May 2020 update
<p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results.<br> <br> Objectives:<br> <br> - Incorporate additional data to provide new maps of wild boar suitability with a resolution of 2x2 km >>> file 3_June_2020_suitability_2x2.tif<br> - New model based on hunting yield with different approaches to handle the biorregion effect >>> files 1_June_2020_HY_nut01_10x10_twostep.tif & 2_June_2020_HY_nut01_10x10_pca.tif<br> <br> Model settings and predictors: <br> - Hunting yield modeling including biorregion effect as bioclimatic PCA scores<br> - Hunting yield addressing biorregion effect in a two-step procedure with independent parametrization for each bioregion<br> <br> Conclusions guiding future methodological steps:<br> - For wild boar suitability maps at 2x2 km, additional data on survey effort is critical in the southern bioregion<br> - Hunting yield model predictions at 10x10 km grids overestimated the hunting bag numbers obtained from the external datasets<br> - HY model with independent parametrization for each bioregion performed better that previous and new strategies<br> <br> For further details and methodological approach see the paper:<br> ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, Soriguer, J. Vicente (2020) update of occurrence and hunting yield-based data models for wild boar at European scale: new approach to handle the bioregion effect. EFSA supporting publication 2020 TO BE COMPLETED<br> <br> Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.</p>
FESOM2 output. Pi mesh
<p>Small test dataset for FESOM2 post processing. MESH is located in pi.tar.gz file. Feel free to use it for development and testing but be aware that these data are in no way suitable for scientific use.</p>
Bibliographic data and analysis of COVID-19 research outputs from Imperial College London 16.01.2020-02.04.2020
<p>Bibliographic data and analysis of 41 research outputs, including reports/preprints/published articles/code, identified as having Imperial authorship and being relevant to COVID-19, published between 16.01.2020 - 02.04.2020. </p> <p>Related report can be found at: Price RC and Ozkan YA. 13 weeks in a pandemic: a descriptive study of Imperial College London’s COVID-19 publications. Imperial College London (April 2020), https://doi.org/10.25561/77970</p>
Output dataset for the SIM4NEXUS Sweden national level case study
<p>Complete baseline system dynamics model output for the national level Sweden case study of the SIM4NEXUS project. The data are provided split into three regions in Sweden. Aggregating across the regions gives national totals.</p>
Model outputs for the paper "Drastic Vegetation Change in the Guajira Peninsula (Colombia) during the Neogene" submitted to Paleoceanography and Paleoclimatology
<p>We provide the netcdf files used to plot precipitation figures over South America in the paper. Four files are provided. Three files are for "preindustrial", "no Andes" and "open Central American Seaway" experiments that were carried out with the fully coupled model IPSL-CM4 (Marti et al., 2010). One file contains the outputs from a Miocene simulation carried out with NCAR CESM in Zhou et al. (2018).</p> <p>References :</p> <p>Marti, Olivier, P. Braconnot, J.-L. Dufresne, J. Bellier, R. Benshila, S. Bony, P. Brockmann, et al. 2010. « Key Features of the IPSL Ocean Atmosphere Model and Its Sensitivity to Atmospheric Resolution ». <em>Climate Dynamics</em> 34 (1): 1‑26. https://doi.org/10.1007/s00382-009-0640-6.</p> <p>Zhou, Haoran, Brent R. Helliker, Matthew Huber, Ashley Dicks, et Erol Akçay. 2018. « C4 Photosynthesis and Climate through the Lens of Optimality ». <em>Proceedings of the National Academy of Sciences</em> 115 (47): 12057‑62. https://doi.org/10.1073/pnas.1718988115.</p> <p> </p>
Model outputs to "Langtang Ice Cliff Modelling Study"
<p>This dataset contains all the model results of our study "Supraglacial ice cliffs can substantially increase the mass loss of debris-covered glaciers".</p> <p>The zip-file contains one main folder: "Cliffmodel_Results" (produced using the 3D-backwasting ice cliff model from Buri et al., 2016b, Buri & Pellicciotti, 2018).</p> <p>More raw data upon request.</p> <p> </p> <p><strong>Folder structure of "Langtang_IceCliffModellingStudy.7z":</strong></p> <p>───Cliffmodel_Results<br> ├───LangshishaGlacier<br> │ ├───Output_Cliffs_Langshisha2014_A_3m<br> │ ├───Output_Cliffs_Langshisha2014_B_3m<br> │ ├───Output_Cliffs_Langshisha2014_C_3m<br> │ ├───Output_Cliffs_Langshisha2014_D_3m<br> │ ├───Output_Cliffs_Langshisha2014_E_3m<br> │ ├───Output_Cliffs_Langshisha2014_F_3m<br> │ ├───Output_Cliffs_Langshisha2014_G_3m<br> │ └───Output_Cliffs_Langshisha2014_H_3m<br> ├───LangtangGlacier<br> │ ├───Output_Cliffs_Langtang2014_A_3m<br> │ ├───Output_Cliffs_Langtang2014_B_3m<br> │ ├───Output_Cliffs_Langtang2014_C_3m<br> │ ├───Output_Cliffs_Langtang2014_D_3m<br> │ ├───Output_Cliffs_Langtang2014_E_3m<br> │ ├───Output_Cliffs_Langtang2014_F_3m<br> │ ├───Output_Cliffs_Langtang2014_G_3m<br> │ ├───Output_Cliffs_Langtang2014_H_3m<br> │ ├───Output_Cliffs_Langtang2014_I_3m<br> │ ├───Output_Cliffs_Langtang2014_J_3m<br> │ ├───Output_Cliffs_Langtang2014_K_3m<br> │ ├───Output_Cliffs_Langtang2014_L_3m<br> │ ├───Output_Cliffs_Langtang2014_M_3m<br> │ ├───Output_Cliffs_Langtang2014_N_3m<br> │ ├───Output_Cliffs_Langtang2014_O_3m<br> │ ├───Output_Cliffs_Langtang2014_P_3m<br> │ ├───Output_Cliffs_Langtang2014_Q_3m<br> │ ├───Output_Cliffs_Langtang2014_R_3m<br> │ ├───Output_Cliffs_Langtang2014_S_3m<br> │ └───Output_Cliffs_Langtang2014_T_3m<br> ├───LirungGlacier<br> │ ├───Output_Cliffs_Lirung2014_A_3m<br> │ │ └───intermediateOutput<br> │ └───Output_Cliffs_Lirung2014_B_3m<br> │ └───intermediateOutput<br> └───ShalbachumGlacier<br> ├───Output_Cliffs_Shalbachum2014_A_3m<br> │ └───intermediateOutput<br> ├───Output_Cliffs_Shalbachum2014_B_3m<br> │ └───intermediateOutput<br> ├───Output_Cliffs_Shalbachum2014_C_3m<br> │ └───intermediateOutput<br> ├───Output_Cliffs_Shalbachum2014_D_3m<br> │ └───intermediateOutput<br> └───Output_Cliffs_Shalbachum2014_E_3m<br> └───intermediateOutput<br> </p> <p> </p> <p><strong>File structure within Cliffmodel_Results \ <GLACIERNAME> \ Output_Cliffs_<GLACIERNAME>2014_A_3m:</strong></p> <p><em>DC_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda</em></p> <p>→List of 10 matrices for each energy balance flux with average diurnal cycle (rows) and cliff (columns) for specific model period </p> <p><em>geom_ts<TIMESTEP1>_Cliffs_Lirung2014_A_3m.txt</em></p> <p>→Characteristics (Elevation,Slope,Aspect,x,y,ID,VsI,VsL,Vd) for each pixel of all cliffs (specific ID) for specific model period </p> <p><em>MODELSETTINGS_GSM_<GLACIERNAME>_<GLACIERSECTOR>_22Aug_Cliffs_<GLACIER>2014.txt</em></p> <p>→Details on all modelsettings</p> <p><em>PC_Melt_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Melt values for each cliff (columns) and timestep (rows) for specific model period </p> <p><em>PC_Stats_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Summary statistics for each cliff (rows) for specific model period </p> <p><em>perPx_DC_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda</em></p> <p>→List of list with per pixel diurnal cycle and standard deviation of each energy flux for specific model period </p> <p><em>SD_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda</em></p> <p>→List of 10 matrices for each energy balance flux with standard deviation for each hour of the day (rows) and cliff (columns) for specific model period </p> <p><em>Stats_MeanFluxes_tts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Mean energy fluxes per timestep (rows) averaged over all ice cliffs</p> <p><em>Stats_NonGriddedData_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt</em></p> <p>→Statistics of non-distributed parameters averaged over all cliffs per timestep (rows)</p>
5-day WRF case study for the Southern Alps of New Zealand (hourly output)
<p>Full output file from the Weather and Research Forecasting (WRF) model for a 2 km resolution domain centered over the Southern Alps of New Zealand. Modelling period is 02-Feb-2011 00:00:00 UTC to 07-Feb-2011 00:00:00 UTC (5 days) with hourly output frequency. The data-set was generated in the framework of a case study to investigate the mesoscale influence of an atmospheric river on the mass balance of Brewster Glacier (Southern Alps) during a summertime melt event. For the related publication refer to <a href="https://doi.org/10.1029/2020JD034217">https://doi.org/10.1029/2020JD034217</a>.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL winter wheat simulations
<p>This data set contains output data from simulations with the model LPJmL for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL spring wheat simulations
<p>This data set contains output data from simulations with the model LPJmL for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').</p> <p>Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.</p>
Output reports and supplementary data for MTB workflow
<p>The archive contains the following data:</p> <ul> <li>Output of the workflow on all validation samples (tabular summaries and full HTML reports).</li> <li>Database with AMR regions and mutations.</li> </ul> <p> </p>
Run and output files from: Holocene population expansion of a tropical bee coincides with early human colonisation of Fiji rather than climate change
<p><span><span><span><span><span><span><span><span><span><span><span>There is substantial debate about the relative roles of climate change and human activities on biodiversity and species demographies over the Holocene. In some cases, these two factors can be resolved using fossil data, but for many taxa such data are not available. Inferring historical demographies of taxa has become common, but the methodologies are mostly recent and their shortcomings often unexplored. The bee genus <i>Homalictus</i> is developing into a tractable model system for understanding how native bee populations in tropical islands have responded to past climate change. We greatly expand on previous studies using sequences of the mitochondrial gene COI from 474 specimens and between 171 and 3,928 autosomal (DArTSeq) SNP loci from 19 specimens of the native Fijian bee, <i>Homalictus fijiensis</i> (Perkins & Cheesman, 1928), to explore its historical demography using coalescent and mismatch analyses. We ask whether past changes in demography were human- or climate-driven, while considering analytical assumptions. We show that inferred changes in population sizes are too recent to be explained by past climate change. Instead we find that a dramatic increase in population size for the main island of Viti Levu coincides with increasing occupation by humans and their modification of the environment. We found no corresponding change in bee population size for another major island, Kadavu, where human populations and agricultural activities have been historically very low. Our analyses indicate that molecular approaches can be used to disentangle the impacts of humans and climate change on a major tropical pollinator and that stringent analytical approaches are required for reliable interpretation of results. </span></span></span></span></span></span></span></span></span></span></span></p>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
Country scientific output by Scopus/SCImago major areas (1996-2006)
<p>CSV file with scientific output of 102 countries (rows) by 27 major Scopus/SCImago subject areas, from 1996 to 2006. Data extracted from SCImago Journal & Country Rank (scimagojr.com).</p>
Top topics from word2vec run of EFSA outputs
<p>List of neural word embeddings extracted from EFSAs outputs and their closest neighbours, which have a distance < 0.3 to the original word.</p> <p>These group of words form a congruent groups which for a human seems to represent a topic.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Output of webXray analysis of German library websites
<p>[1] was analyzed with [2]. The deposited files are the result.</p> <p> </p> <p>[1] Steeg, Fabian et al.. (2016). URLs von Webseiten mit Typ Bibliothek aus Lobid.org. Zenodo. 10.5281/zenodo.50969</p> <p>[2] Tim Libert et al.. (2016). webXray: First Release. Zenodo. 10.5281/zenodo.57272</p> <p> </p>
Data used in "Fast metabolite identification with Input Output Kernel Regression"
<p>This repository contains the data used in [1] to evaluate the performance for metabolite identification from tandem mass spectra. These data have been extracted and processed in [2]. We used a subset of 4138 MS/MS spectra extracted from the GNPS public spectral library (https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp) for training and evaluation. For searching, we used molecular structures from PubChem as candidate sets.</p> <p>Please mention and cite GNPS when using these data.</p> <p>The implementation of the method proposed in [1] is available on: https://version.aalto.fi/gitlab/kepaco/Fast-metabolite-identification-with-IOKR</p> <p><strong>Files description:</strong></p> <ul> <li><em>spectra.txt</em>: informations about the MS/MS spectra (GNPS identifier, compound name and INCHI identifier)</li> <li><em>data_GNPS.mat</em>: contains the molecular fingerprints, molecular formula and InCHI corresponding to the MS/MS spectra</li> <li><em>cv_ind.txt</em>: indices of the cross-validation folds</li> <li><em>ind_eval.txt</em>: indices of the examples used for evaluation</li> <li><em>candidates</em>: fingerprints and INCHI for the different candidate sets</li> <li><em>input_kernels</em>: contains 24 input kernel matrices</li> </ul> <p><strong>References:</strong></p> <p>[1] Brouard, C., Shen, H., Dührkop, K., d'Alché-Buc, F., Böcker, S. and Rousu, J.: Fast metabolite identification with Input Output Kernel Regression. In the proceedings of ISMB 2016, Bioinformatics 32(12): i28-i36, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw246</p> <p>[2] Dührkop, K., Shen, H., Meusel, M., Rousu, J. and Böcker, S.: Searching molecular structure databases with tandem mass spectra using CSI:FingerID. PNAS, 112(41), 12580-12585, 2015. doi:10.1073/pnas.1509788112</p> <p> </p> <p> </p>
Model output and figure code for "Tradeoffs Between Vertical and Lateral Resilience in a Salt Marsh Restoration Model"
<p>Simulation output data from COLT_Restorations model stored on the Community Surface Dynamics Modeling System public model repository at <a href="https://csdms.colorado.edu/wiki/Model:COLT_Restorations">https://csdms.colorado.edu/wiki/Model:COLT_Restorations</a> and on a GitHub public repository at<a href="https://github.com/mbbarksdale/CoLT_Restorations"> https://github.com/mbbarksdale/CoLT_Restorations</a>.</p> <p>These data were processed and graphed with model code that can also be found in a GitHub public repository at <a href="https://github.com/mbbarksdale/COLTRestorationsPaper_dataANDfigures">https://github.com/mbbarksdale/COLTRestorationsPaper_dataANDfigures</a>. </p>
ScienceDex guides
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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.