Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,281
datasets available to search
ShareScore release 0.7.1
Dataset results
6,281 results for “Landscape”
Data and documentation from: Microclimate explains little variation in year-round decomposition across an Arctic tundra landscape
<p>The zip file contains data and code to reproduce the analysis in the submitted manuscript entitled <i>Microclimate explains little variation in year-round decomposition across an Arctic tundra landscape</i>. Please see the manuscript for further details on background, methodology, results and discussion.</p>
Dataset from paper "Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest"
<h3><strong><span>Dataset from the paper “Quantifying landscape fragmentation and forest carbon dynamics over 35 years in the Brazilian Atlantic Forest”</span></strong></h3> <p><span> </span><span>This repository contains:</span></p> <ul> <li><span>Dataset Description: “raster_labels.xlsx” (an Excel spreadsheet detailing raster pixel values and their respective fragmentation classes).</span></li> <li><span>Fragmentation Raster Files: “forest_fragmentation_mspa_2020.tif” and “forest_fragmentation_mspa_2020.tif” (GeoTIFF files of landscape forest fragmentation for 1985 and 2020).</span></li> </ul> <p><span> </span><span>If you need anything else, please contact the corresponding author, Igor Broggio (<a href="mailto:isbbroggio@gmail.com">isbbroggio@gmail.com</a>).</span></p> <p><span> </span></p> <p><span>If you use these data, please cite the paper: </span><span>[Citation]</span></p>
Data and Code for "Why are generalists the 'winners' of habitat loss? Unveiling the process underlying specialist-generalist replacements in fragmented landscapes"
<p><span>Data and R-based workflow for the study "Why are generalists the ‘winners’ of habitat loss? Unveiling the process underlying specialist-generalist replacements in fragmented landscapes".</span></p>
The Femern-project: a large-scale excavation of a Stone Age landscape - supplementary data
<p>This dataset contains all radiocarbon dates from the Femern project.</p> <p>Please cite the dataset as: </p> <p>Måge, B.T., Groß, D., Kanstrup, M. 2023. The Femern-project: a large-scale excavation of a Stone Age landscape. In: Groß, D. and Rothstein, M.: Changing Identity in a Changing World. Archaeological Studies on Human Interaction in Northern Europe around 4000 cal BC. Leiden: Sidestone, supplementary material.</p> <p>19.02.2024: Dataset updated: Wrong species ID in original dataset for AAR-27426</p>
[Dataset] Plant–pollinator in a highly intensive agricultural landscape LTSER Zone Atelier Plaine & Val de Sèvre
<p>We built bipartite networks formed by pollinators and the flowers they forage on, using data collected in the Long Term Socio-Ecological Research site "Zone Atelier Plaine & Val de Sèvre" (Bretagnolle et al. 2028). We compiled a six-year monitoring dataset of plant–pollinator interactions, sampling by sweep-nets along transects in the main crop types of this intensive agricultural plain. </p> <p>The dataset contained all the "pollinator-plant" pair observed in each crop samples.</p>
Data and supplementary material used for Soundscapes to Landscapes soundscape mapping
<p>This repository contains supporting data products to enable the soundscape mapping outlined in the associated publication (DOI forthcoming). Data were used to extract acoustic recording location environmental data for training random forest models to spatially predict 2021 ecoacoustic metrics. The accompanying code will be linked to the GitHub repository. Files include:</p> <p>Data:</p> <ul> <li>clustered_fold_k10.rsd: indices of the model data used if geoCV approach</li> <li>extracted_predictors_vif3.csv: site-specific predictor values extracted from predictors_annual_20230223.tif</li> <li>final_predictors_vif3.csv: a two column table summarizing the VIF selected predictors</li> <li>final_sites_2017-2021.csv: the list of 1,195 potential sites</li> <li>predictor_sprmn_corr.csv: correlation matrix for predictors in model data</li> <li>predictors_annual_20230223.tif: all predictors </li> <li>response_df_200623.csv: site level ecoacoustic metrics</li> </ul> <p>Results:</p> <ul> <li>map_correlations.tar: pairwise response map correlations</li> <li>pdps.tar: partial dependence plot data</li> <li>performance.tar: model performance summaries</li> <li>predictions_maps.tar: final median and IQR model prediction surfaces</li> <li>variable_importance.tar: summaries for variable importance analyses</li> </ul> <p>Contact Colin Quinn at cq73@nau.edu for questions related to this repository or the underlying work. Original wav recordings are expected to be made publicly available on the NASA DAACs in the near future. </p>
Data from a cross-sectional study of fifth grade children in a sample of primary schools in Belgium that differ in amount of greenness at school and landscape level
<p>The data in this deposit were collected as part of the <code>B@SEBALL</code> project (Biodiversity at School Environments - Benefits for All). </p> <p>The project investigated how biodiversity in the school environment can positively affect children’s health and mental well-being. <code>B@SEBALL</code> also investigated the opportunities for reducing health inequalities among children via biodiversity at school environments.</p> <p>The data are organized according to the <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package standard</a>. All child-level and school-level data have been anonymized. Each data package is a collection of <code>csv</code> files and a <code>json</code> file. The <code>json</code> file holds descriptive information for all variables in all <code>csv</code> files. The <code>zip</code> file contains two frictionless data packages. The data packages contain information on 37 primary schools and 513 children. </p> <p>The data package, <code>data_package_an_zenodo_cleaned_data</code>, contains the original data in a tidied and cleaned format. It consists of 46 <code>csv</code> files. The files relate to the following contents:</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>landscape level variables</td> <td>wp1_landscape_level_data.csv</td> </tr> <tr> <td>metadata about participants</td> <td>wp2_participants_metadata.csv</td> </tr> <tr> <td>general school level data</td> <td>wp2_school_data.csv</td> </tr> <tr> <td>pollution data at school level</td> <td>wp3_ua_sirm_data.csv</td> </tr> <tr> <td>classroom data about air quality</td> <td>wp3_ucl_classroom_airquality.csv</td> </tr> <tr> <td>area of ecotopes in the school environment</td> <td>wp3_ucl_ecotope_categories.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_indicators.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_key.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenpatches.csv</td> </tr> <tr> <td>playground biodiversity indicators</td> <td>wp3_ucl_playground_biodiversity.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_child.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_line.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_linegroup.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_data.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_questions.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_data.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_questions.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_data.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_questions.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_data.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_key.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part1.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part2.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_key.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_data.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_key.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_data.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_key.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_data.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_key.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_data.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_key.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_data.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_key.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_data.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_key.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part1.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part2.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part3.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part4.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_key.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part1.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part2.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_key.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_data.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_key.csv</td> </tr> </tbody> </table> <p> </p> <p>The <code>data_package_an_zenodo_derived_data</code> data package, contains derived data that was calculated based on input from <code>data_package_an_zenodo_cleaned_data</code> at either child-level or at school-level.</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>derived data at child level</td> <td>wp1_child_level_key_variables.csv</td> </tr> <tr> <td>derived attention score based on d2-test data, aggregated to line-level</td> <td>wp1_d2_by_line_attention_score.csv</td> </tr> <tr> <td>derived data at school level</td> <td>wp1_school_level_key_variables.csv</td> </tr> </tbody> </table> <p>These data packages only store information for participants that gave consent for a particular part of the study and that gave consent for long-term storage of the data. There may therefore be slight differences between results published as part of the project consortium, which could make use of participant data that did not give consent for long-term data storage, and reproduction of these results based on the data in this data repository. We also note that the derived variables in the derived data package were calculated with these participants included and removal of participants for which we had no long-term storage consent was done after these calculations.</p> <p>As part of the project, microbiome data were also collected (both from cheek swabs on the children and from environmental samples), but this part of the data are not a part of this deposit and will be deposited in the European Nucleotide Archive (ENA).</p>
Scaling landscape fire history in sagebrush: Wildfires not historically frequent in the main population of threatened Gunnison Sage-grouse
<p>The main population of ∼5,000 Threatened Gunnison sage-grouse (GUSG; Centrocercus minimus) in Colorado depends on sagebrush that are killed by wildfires, with recovery taking decades, so frequent fire is a threat, but did it occur historically? Early land surveys showed that the historical (preindustrial) fire rotation (FR), the expected period to burn area equal to a focal land area, was 90-143 years in GUSG ranges, which is not frequent fire (≤25 years). However, recent research, based on fire scars on trees at ten sites near sagebrush, suggested some frequent fire historically in the main population. That study was not spatial, essential to estimate FR, so spatial data were created in GIS with land-survey reconstructions, survey dates, fire-scar sites, Thiessen polygons around sites, and sagebrush. The previous study assumed fires that burned 2+ sites likely burned across sagebrush. Historical FRs were calculated several ways over a common period. A recovery estimate of FR was 90-135 years, a land-survey estimate 82-131 years, and three spatial scar-based estimates 93-107 years, showing agreement. However, comparing land-survey and fire-scar results showed that using fire scars spatially only 43% matched land surveys. Detailed analysis showed that 10 fire-scar sites were insufficient to detect historical fire sizes and distributions across the large 168,753 ha sagebrush area. An adequate historical fire reconstruction could require ∼45-60 fire-scar sites, making only ∼30,000 ha of sagebrush feasible. Using the two remaining methods, which cross-validate, showed frequent fire did not occur historically in the study area, as historical FRs were 82-135 years. </p>
The location of solar farms within England's ecological landscape: implications for biodiversity conservation
<p>Data associated to the article entitled 'The location of solar farms within England's ecological landscape: implications for biodiversity conservation'. </p> <p> </p>
m6Am landscape of human cell lines
<p>Tables of m6Am profiling results by CROWN-seq. </p> <p>Cell lines in included in this version are:</p> <ol> <li>HEK293T (wild-type, PCIF1 KO, FTO KO)</li> <li>A549</li> <li>HepG2</li> <li>Huh-7</li> <li>CCD841 CoN</li> <li>HT-29</li> <li>HCT-116</li> <li>K562</li> <li>Jurkat E6.1</li> </ol> <p>ReCappable-seq data for HEK293T and A549 (WT and PCIF1 KO) are also included.</p> <p>Description of the table columns can be found in <code>README.md</code>.</p>
Destruction of the Cultural-Archaeological Landscape in the Gaza Strip
This dataset contains 10 archaeological sites in Gaza strip destroyed during the war 2023-2024. The dataset was compiled by Ministry of Tourism & Antiquities of Palestine (MOTA) team, Dr. Sufyan Deis.
Neogene–Quaternary uplift and landscape evolution in northern Greenland recorded by subglacial valley morphology: Datasets
<p>This dataset contains a csv file of subglacial valley morphology derived from radio-echo sounding datasets in northern Greenland, and an ESRI shapefile of the interpreted channel network. For further documentation of the data please view the README.txt file.</p> <p>RADAR-DERIVED VALLEY MORPHOLOGY</p> <ul> <li><strong>northern_Greenland_valley_morphology.csv</strong>: location and morphology of subglacial valleys in northern Greenland, as imaged by airborne radio-echo sounding datasets.</li> </ul> <p>SUBGLACIAL VALLEY NETWORK</p> <ul> <li><strong>northern_Greenland_valley_network.shp (and ancillary files: .cpg, .dbf, .prj, .qpj, .shx)</strong>: ESRI shapefile of the interpreted valley network in the northern Greenland subglacial drainage catchment.</li> </ul>
Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes
<p>We use computational models based on Direct Coupling Analysis - DCA - trained on PFAM domains of distant distant homologues to accurately predict the polymorphisms segregating in a panel of 61,157 <em>Escherichia coli </em>genomes.</p> <p>We show that the genetic context (<em>i.e. </em>the rest of the protein sequence) strongly constrains the tolerable amino acids in 30% to 50% of amino-acid sites. Our study also suggests the gradual build-up of genetic context over long evolutionary timescales by the accumulation of small epistatic contributions.</p> <p>Please refer to the README file for additional information on the structure of this dataset.</p> <p>Code to analyse this dataset is available at https://github.com/GiancarloCroce/DCA_polymorphism_Ecoli.</p> <p> </p>
19th Century United States Newspaper images predicted as Photographs with labels for "human", "animal", "human-structure" and "landscape"
<p>The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (<a href="https://chroniclingamerica.loc.gov/">chroniclingamerica.loc.gov/</a>). </p> <blockquote> <p>[The Newspaper Navigator dataset] consists of extracted visual content for 16,358,041 historic newspaper pages in <em>Chronicling America</em>. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the <a href="https://labs.loc.gov/work/experiments/beyond-words/">Beyond Words</a> crowdsourcing project.</p> <p>source:<a href="https://news-navigator.labs.loc.gov/"> https://news-navigator.labs.loc.gov/</a></p> </blockquote> <p>One of these categories is 'photographs'. This dataset contains a sample of these images with additional labels indicating if the photograph has one or more of the following labels: "human", "animal", "human-structure" and "landscape"</p> <p>The data is organised as follows:</p> <ul> <li>The images themselves can be found in `images.zip`</li> <li>`newspaper-navigator-sample-metadata.csv` contains metadata about each image drawn from the Newspaper Navigator Dataset.</li> <li>`multi_label.csv` contains the labels for the images as a CSV file</li> <li>`annotations.csv` conains the labels for the images with additional metadata</li> </ul> <p>This dataset was created for use in an under-review Programming Historian tutorial (<a href="http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt2">http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt2</a>) The primary aim of the data was to provide a realistic example dataset for teaching computer vision for working with digitised heritage material. The data is shared here since it may be useful for others. <strong>This data documentation is a work in progress and will be updated when the Programming Historian tutorial is released publicly. </strong></p> <p>The metadata CSV file contains the following columns:</p> <p>- filepath<br> - pub_date<br> - page_seq_num<br> - edition_seq_num<br> - batch<br> - lccn<br> - box<br> - score<br> - ocr<br> - place_of_publication<br> - geographic_coverage<br> - name<br> - publisher<br> - url<br> - page_url<br> - month<br> - year<br> - iiif_url</p>
Morpho-sedimentary outlines displayed in Figures 1, S1, and S6-S15 of the article "Source-to-sink aeolian fluxes from arid landscape dynamics in the Lut Desert"
<p>Morpho-sedimentary outlines of the aeolian landforms in the Lut Desert.</p>
The genomic and transcriptional landscape of primary central nervous system lymphoma
<p>Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Despite extensive research, the molecular alterations leading to PCNSL have not been fully elucidated. In order to provide a comprehensive description of the genomic and transcriptional landscape of PCNSL, we here performed whole-genome and transcriptome sequencing and integrative analysis of 51 lymphomas presenting in the CNS, including 42 EBV-negative PCNSL, 6 secondary CNS lymphomas (SCNSL) and 3 EBV+ CNSL and matched controls. The results were compared to an independent validation cohort of 31 FFPE CNSL specimens (PCNSL, n = 19; SCNSL, n = 9; EBV+ CNSL, n = 3) and 36 systemic DLBCL cases outside the CNS.</p> <p>This repository contains tab separated value text files:</p> <p>- Radke_et_al_supplementary_somatic_CNVs.tsv (somatic copy number variations predicted by ACEseq)<br> - Radke_et_al_supplementary_somatic_indels.tsv (somatic indels predicted by the DKFZ platypus workflow)<br> - Radke_et_al_supplementary_somatic_indels_exonic.tsv (somatic exonic indels predicted by the DKFZ platypus workflow)<br> - Radke_et_al_supplementary_somatic_mutations_integrated.tsv (table of gene by patients, stating which mutations were observed) <br> - Radke_et_al_supplementary_somatic_mutations_integr_integrated_including_kataegis_counts.tsv (table of gene by patients, stating which mutations were observed, including the count of mutations falling into kataegis hotspots) <br> - Radke_et_al_supplementary_somatic_SNVs.tsv (somatic SNVs predicted by the DKFZ mpileup workflow)<br> - Radke_et_al_supplementary_somatic_SNVs_exonic_functional.tsv (somatic exonic SNVs predicted by the DKFZ mpileup workflow)<br> - Radke_et_al_supplementary_somatic_SNVs_rescued_by_TiNDA (mutations initially classified as germline, but likely tumor mutations based on VAF modelling by TiNDA)<br> - Radke_et_al_supplementary_somatic_SVs.tsv (somatic structural variations predicted by the DKFZ Sophia workflow)<br> - Radke_et_al_supplementary_RNAseq_numReads_CNSLs.tsv (RNAseq read counts calculated by the DKFZ RNAseq workflow)</p> <p>This repository contains the raw unedited images from the manuscript:</p> <p>- Radke_et_al_Main_Figure_1c_BCL6.tif (raw unedited image for Main Figure 1c - BCL6)<br> - Radke_et_al_Main_Figure_1c_CD10.tif (raw unedited image for Main Figure 1c - CD10)<br> - Radke_et_al_Main_Figure_1c_MUM1.tif (raw unedited image for Main Figure 1c - MUM1)<br> - Radke_et_al_Supplementary_Figure_1a_CD20.tif (raw unedited image for Supplementary Figure 1a - CD20)<br> - Radke_et_al_Supplementary_Figure_1a_EBV.tif (raw unedited image for Supplementary Figure 1a - EBV)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(FFPE).tif (raw unedited image for Supplementary Figure 1a - HE (FFPE))<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_1.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 1)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_2.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 2)<br> - Radke_et_al_Supplementary_Figure_1a_HE_(frozen)_3.tif (raw unedited image for Supplementary Figure 1a - HE (frozen) 3)<br> - Radke_et_al_Supplementary_Figure_1a_Ki67.tif (raw unedited image for Supplementary Figure 1a - Ki67)<br> - Radke_et_al_Supplementary_Figure_1b_EBV_PCR.pptx (raw unedited image for Supplementary Figure 1b - EBV PCR )<br> - Radke_et_al_Supplementary_Figure_1c_CDKN2A_FISH_1.jpg (raw unedited image for Supplementary Figure 1c - CDKN2A FISH 1)<br> - Radke_et_al_Supplementary_Figure_1c_CDKN2A_FISH_2.jpg (raw unedited image for Supplementary Figure 1c - CDKN2A FISH 2)<br> - Radke_et_al_Supplementary_Figure_7h_PD-L1_LS-033.tif (raw unedited image for Supplementary Figure 7h - PD-L1 LS-033)<br> - Radke_et_al_Supplementary_Figure_7h_PD-L1_LS-031.tif (raw unedited image for Supplemnetary Figure 7h - PD-L1 LS-031)</p>
Data from: Absence of genetic isolation across highly fragmented landscape in the ant Temnothorax nigriceps
<p><strong>This README accompanies data_genotyping.txt</strong></p> <p> </p> <p><strong><em>Associate publication : </em></strong></p> <p>Absence of genetic isolation across highly fragmented landscape in the ant Temnothorax nigriceps</p> <p>M. Cordonnier<sup>a</sup>, D. Felten<sup>a</sup>, A. Trindl<sup>a</sup>, J. Heinze<sup>a</sup>*, A. Bernadou<sup>a</sup>*</p> <p><sup>a</sup>Lehrstuhl für Zoologie / Evolutionsbiologie, Univ. Regensburg</p> <p>*Equal contribution</p> <p> </p> <p>****************************** CONTENTS *******************************</p> <p>The data can be readily imported in any statistical package or spreadsheet program. Please, contact me if you need the file formatted in other ways.</p> <p> </p> <p>This file includes a description of the variables.</p> <p>***********************************************************************</p> <p>Variable names and descriptions</p> <p> </p> <p><strong>Sample:</strong> ID of the sampled nest</p> <p><strong>Location:</strong> Population of the sampled nest</p> <p> </p> <p><strong>List of genotypes </strong></p> <p>Microsatellite primers used in the study</p> <table> <tbody> <tr> <td> </td> <td> <p>Annealing temperature [°C]</p> </td> <td> <p>Orientation</p> </td> <td> <p>Sequence of primers</p> </td> </tr> <tr> <td> <p>LX GT218</p> </td> <td> <p>57</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-GTTCTTGCGCGGATGCATAC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-TGTACTCGCGTGTCTATCGG-3’</p> </td> </tr> <tr> <td> <p>Ant3993</p> </td> <td> <p>57</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-TGATCCGCTCTTAAAATTTAGATGGA-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-ACTTTCCGCRGCATTAAACATTTTCTT-3’</p> </td> </tr> <tr> <td> <p>L-18</p> </td> <td> <p>57</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-TGAATTTGGATGGCGGTAGAC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-ACCTAATGCACGCTTTAGAAT-3’</p> </td> </tr> <tr> <td> <p>LXA GT1</p> </td> <td> <p>57</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-GTGGCGACCAATTCTGCAAG-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-GCAGGACCAGCATCAAATGACAG-3’</p> </td> </tr> <tr> <td> <p>2MS17</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-CAGCCTCTATTTTGTTCGAAG-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-TTTACTGCGGCTCCATAATC-3’</p> </td> </tr> <tr> <td> <p>2MS46</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-GCTCACTACTATGCTGCCAGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-CTTTCCTGCAAACCACGTGT-3’</p> </td> </tr> <tr> <td> <p>2MS60</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-TATGCGCCGGACAATAATCGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-GTTCATTGTCCGAGGCGCAGC-3’</p> </td> </tr> <tr> <td> <p>2MS67</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-GAAGATTCGTCAGGATGCAGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-AACTCTCGCTGGCAAGCGAGC-3’</p> </td> </tr> <tr> <td> <p>2MS82</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-AAAAGAGCATGCAACAGGTCAGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-TTTCTTAAGTCGCAAGCGAGC-3’</p> </td> </tr> <tr> <td> <p>2MS87</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-GGAACCTCACTCAACCTCGGT-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-ACGCGGACTACTTTAACCGGA-3’</p> </td> </tr> <tr> <td> <p>2MS91</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-AAAGTCTCGGAGTGGCTTTGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-ATTCTCGTCCATTTGTTCTAA-3’</p> </td> </tr> <tr> <td> <p>Ant11893</p> </td> <td> <p>55</p> </td> <td> <p>Forward</p> </td> <td> <p>5’-CAGGCTCGGRACGTTAATGC-3’</p> </td> </tr> <tr> <td> <p>Reverse</p> </td> <td> <p>5’-GGTGCCGACGTCTAGCTAGC-3’</p> </td> </tr> </tbody> </table> <p> </p> <p>Missing data are encoded “-9”.</p> <p> </p> <p>****************************** CONTACTING *****************************</p> <p>Contact me at:</p> <p> </p> <p>Marion Cordonnier</p> <p>e-mail: marion.cordonnier@hotmail.com</p> <p> </p> <p>***********************************************************************</p> <p> </p>
Data and code for the publication "DNA methylation underpins the epigenomic landscape regulating genome transcription in Arabidopsis"
<p>The zipped file of this repository contains code and data to reproduce the results of the publication:</p> <p>Zhao et al, DNA methylation underpins the epigenomic landscape regulating genome transcription in Arabidopsis. Genome Biology (2022). </p> <p>All sequence data have been deposited in NCBI GEO accession codes GSE183987 and GSE169497.</p> <p> </p> <p>Please see the README document for detailed:</p> <p>- Descriptions of the code and data provided</p> <p>- Lists of the required dependencies</p>
Code and data accompanying Palmeirim et al. (2022) Emergent properties of species-habitat networks in an insular forest landscape. Science Advances
<p>Dataset containing species distribution in insular forest fragments at Balbina and full R code for analyses and figures.</p> <p>For deatails, please see the original publication: "Emergent properties of species-habitat networks in an insular forest landscape". Ana Filipa Palmeirim, Carine Emer, Maíra Benchimol, Danielle Storck-Tonon, Anderson S. Bueno, Carlos A. Peres. Science Advances (2022). 10.1126/sciadv.abm0397.</p> <p> </p>
Supplementary material for "Patterns of high-flying insect abundance are shaped by landscape type and abiotic conditions"
<p><strong>Abstract</strong></p> <p>Insects are of increasing conservation concern as a severe decline of both biomass and biodiversity have been reported. At the same time, data on where and when they occur in the airspace is still sparse, and we currently do not know whether their density is linked to the type of landscape above which they occur. Here, we combine data of high-flying insect abundance from six locations across Switzerland representing rural, urban and mountainous landscapes, which was recorded using vertical-looking radar devices. We analysed the abundance of high-flying insects in relation to meteorological factors, daytime, and type of landscape. Air pressure was positively related to insect abundance, wind speed showed an optimum, and temperature and wind direction did not show a clear relationship. Mountainous landscapes showed a higher insect abundance than the other two landscape types. Insect abundance increased in the morning, decreased in the afternoon, had a peak after sunset, and then declined again, though the extent of this general pattern slightly differed between landscape types. We conclude that the abundance of high-flying insects is not only related to abiotic parameters, but also to the type of landscapes. Thus, conservation measures implemented on the ground should start to also account for the needs of high-flying insects.</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.