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Dataset and analyses for publication entitled: “Acclimation of the nitrogen cycle to changes in precipitation”
This dataset contains data and analysis code for the paper entitled “Acclimation of the nitrogen cycle to changes in precipitation" by Currier et al. As the frequency of precipitation extremes are expected to increase, especially in arid regions, we asked how prolonged shifts in water availability facilitate acclimation of the N cycle in a semiarid grassland. Using natural abundances of stable nitrogen isotopes for dominant plants and soils and rainfall manipulation experiments, we tested the hypothesis that N cycling will interact with water availability further amplifying the openness of the N cycle through time. For the dominant plant species, we found the relationship for N availability vs. ambient annual precipitation to be significantly positive, contrary to global spatial models. We also considered the temporal dynamics of our experiments, which imposed directional rainfall manipulations in duration ranging from 5 to 14 years. The slopes of these relationships decreased (became less positive) with more time since the onset of the directional precipitation extremes. These data and metadata supplement long-term foliar and soil isotope data from the Jornada LTER (Dataset ID: knb-lter-jrn.210586001) with a large spatial dataset from NEON data package DP1.10026.001 and Craine et al. 2018 (https://doi.org/10.5061/dryad.v2k2607).
Point locations for spatial and morphological analyses of barchans in swarms
<div>This dataset contains the long-lat coordinates of seven points on ~6000 barchans located in six swarms.</div> <div> </div> <div>Four of the locations are on Earth (three in the Tarfaya region of the Western Sahara, one in Mauritania).</div> <div>The other two swarms are from high latitudes of the northern hemisphere of Mars.</div> <div> </div> <div>In each location between 850 and 1112 barchans were measured.</div> <div> </div> <div>The measurements were carried out manually by Dominic T Robson and Andreas CW Baas according to the method described in</div> <div>Robson, D. T., Annibale, A., & Baas, A. C.W. (2022). Reproducing size distributions of swarms of barchan dunes on Mars and Earth using a mean-field model. Physica A: Statistical Mechanics and its Applications, 606, 128042.</div> <div> </div> <div>The included metadata file lists the copyrights and dates (DD/MM/YYYY) for the imagery used, all imagery was accessed through Google Earth. </div> <div> </div> <div>The metadata file also includes descriptions of the format of the data. The data themselves are provided in separate comma delimited files for each location. Only the bedforms identified as barchans are included although other bedforms in the locations were also measured (see Robson et al. Physica A (2022)).</div> <div> </div> <div>Using the seven points recorded for each dune it is possible to calculate:</div> <div>Body length</div> <div>Total length</div> <div>Horn lengths</div> <div>Total width</div> <div>Horn-to-horn width</div> <div>Port flank width</div> <div>Starboard flank width</div> <div>Slipface length</div> <div>Dune orientation</div> <div> </div> <div>The area of the polygons formed by the points also provides an estimate for the basal area of the dune though it is not a perfect match.</div> <div> </div> <div>We hope that these data will be of use to those seeking to study the morphology, size, asymmetry, and spatial distribution of barchans in swarms.</div> <div> </div> <div>Dominic T Robson and Andreas CW Baas.</div>
StopptCOVID-Studie - Daten, Analyse und Ergebnisse
<p>Die getroffenen Maßnahmen zur Kontrolle von Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) haben während der Coronavirus Disease 2019-(COVID-19-) Pandemie zu starken Einschränkungen des öffentlichen Lebens in Deutschland geführt. Das übergeordnete Ziel des Projekts "StopptCOVID" bestand darin, die Evidenzgrundlage für die Beurteilung der Effektivität verschiedener antipandemischer, nicht-pharmazeutischer Maßnahmen (NPI) zu verbessern. Dabei war die Frage, inwiefern verordnete Maßnahmen einen Anstieg der COVID-19-Inzidenz bremsen konnten. An dieser Stelle veröffentlichen wir Daten und Code für die Analyse der NPI in Deutschland.</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>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.5. Pottery (A, C), animal bones (B), a human skull (C, D), and a flint tool (D) excavated from underneath the stone layer in Kaliszany (archaeological site no. 3)
<p>The set contains a figure, with with photographs that show examples of finds discovered during excavations at archaeological site 3 in Kaliszany, Wągrowiec commune, Poland. It is a stone and earth structure in which a hoard of metal objects dating to the Late Bronze Age was discovered in 1943. The photo is from the 2022 survey, when the south-western part of the structure was explored. <br><br>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.1. Location of hoards mentioned in the text: white dots represent locations of hoards examined in the Biography of Hoards project; black dots represent locations of hoards examined in other multi-faceted projects
<p>The set contains a figure, with data, on the location of the hoards included (described in the related paper).<br><br>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Multi-faceted analyses of Poland's Bronze and Early Iron Age hoards: Fig.3. Workflow in the Biography of Hoards project
<p>The set contains a figure and editable files associated with the figure.</p> <p>Figure presenting workflow of the project described in the related paper.</p> <p>The paper and data were prepared as part of a project funded by the National Science Centre, Poland: <em>A Biography of Late Bronze and Early Iron Ages Hoards. A Multi-Faceted Analysis of Metal Objects Related to Monumental Constructions in Poland</em> (UMO-2021/41/B/HS3/00038)</p>
Integrated Datasets for analyses on potentially hazardous locations for women in Valencia, Dublin, San Francisco, and Toluca
<p>This dataset provides a compilation of the data used to analyze and identify potentially dangerous<br>places for women. Multiple data collection techniques, including official data downloads, web<br>scraping, and participatory mapping, were combined for integration, applying specific processing.<br>The datasets refer to four cities: Valencia (Spain), Dublin (Ireland), San Francisco (United States),<br>and Toluca (Mexico).<br>Depending on the availability and context of each city, the datasets are classified into three<br>categories: DATA, TWT, and MAP. The DATA prefix refers to files containing the results of the<br>analysis of socioeconomic variables downloaded from official sources; for the mapping, the<br>standard territorial unit was a 25x25 m grid for Valencia and 50x50 m for Dublin and San Francisco.<br>The files with the prefix TWT are composed of datasets containing tweets collected through web<br>scraping and analyzed using natural language processing (NLP) algorithms and neural networks;<br>the purpose is to identify and classify tweets related to gender violence, feelings of fear, or<br>perceptions of insecurity. For MAP files, participants gathered them through participatory<br>mapping processes, using specific calls to public space users and a supporting web application<br>designed for this purpose. The files with the prefix POL contain datasets used for crime prediction based on crime density for the city of Valencia.</p>
Supplementary data for analysing distributed temperature sensing (DTS) measurements from Helsinki, Finland
<p>Supplementary data used in the analysis of distributed temperature sensing (DTS) measurements from Helsinki, Finland, as described in a journal article manuscript "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland".</p> <p>Eddy covariance, radiation and precipitation data is provided from the SMEAR III station by the Institute for Atmospheric and Earth System Research at the University of Helsinki under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). The data can also be accessed programmatically via https://smear.avaa.csc.fi/. All SMEAR III data is time referenced to UTC+2.</p> <p>The 2-metre temperature data is provided by the Finnish Meteorological Institute under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). All Finnish Meteorological Institute data is referenced to UTC.</p>
bollito: a flexible pipeline for comprehensive single-cell RNA-seq analyses - Melanoma tutorial
<p>Downsampled version of the melanoma dataset originally published by <em><a href="https://genome.cshlp.org/content/28/9/1353">Ho et al </a>(1)</em>. The dataset is composed by cells from the 451Lu cell line. There are two samples available:</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> <td><strong>R1/R2</strong></td> </tr> <tr> <td>451LU</td> <td>Parental cell line</td> <td>2500K_451LU_L003_R*_001.fastq.gz</td> </tr> <tr> <td>451LUBR3</td> <td>Vemurafenib-resistant sample treated with targeted BRAF inhibitors</td> <td>500K_451LUBR3_L004_R*_001.fastq.gz</td> </tr> </tbody> </table> <p><br> (1) Ho YJ, Anaparthy N, Molik D, et al. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. <em>Genome Res</em>. 2018;28(9):1353-1363. doi:10.1101/gr.234062.117</p>
Supporting Information for 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'
<p><strong>Supporting Information of 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'</strong></p> <p>This dataset contains the Supporting Information of the publication </p> <p>Rühr PT & Blanke A <strong>(2022)</strong>: 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'. doi: <a href="https://doi.org/10.1111/2041-210X.13909">10.1111/2041-210X.13909</a>.</p> <p>It includes</p> <ul> <li>validation measurements the forceX setups (1 Ruehr Blanke 2022 validation measurements.zip)</li> <li>all CAD files to build the forceX setup (3D-printed or metal-turned) (2 Ruehr Blanke 2022 forceX CAD files.zip)</li> <li>forceX assembly instructions in HTML format, including schematics of custom electronics (3 Ruehr Blanke 2022 forceX Assembly instructions.html)</li> <li>forceX assembly instructions as video (4 Ruehr Blanke 2022 forceX assembly video 03.mp4)</li> <li>R code that produced all validation-related figures used in the original publication and that functions as a forceR v.1.0.13 example workflow (5 Ruehr Blanke 2022 forceR_workflow_example.R)</li> <li>Python code to take videos of force measurements using the forceX camera module (6 Ruehr Blanke 2022 forceX_RPi_camera_code.py)</li> <li>bundled version of forceR v.1.0.15 (forceR_1.0.15.tar.gz)</li> </ul> <p>The CAD files and assembly instructions are also available on <a href="https://www.thingiverse.com/thing:4961834">Thingiverse</a>. The forceR package is available on <a href="https://cran.r-project.org/web/packages/forceR/index.html">CRAN</a> (stable version) and <a href="https://github.com/Peter-T-Ruehr/forceR">GitHub</a> (development version).</p>
Supplementary input data for accounting for component condition and preventive retirement in power system reliability of supply analyses
<div> <div>This data set contains supplementary data used for case studies on accounting for transformer condition in reliability of supply analyses in the following manuscripts: <br>1) H. Toftaker, J. Foros, I. B. Sperstad, "Accounting for component condition and preventive retirement in power system reliability of supply analyses", IET Generation, Transmission & Distribution, vol. 5, no. 1, 2023, DOI: 10.1049/gtd2.12761. <br>2) I. Bjerkebæk, I. B. Sperstad, H. Toftaker, G. Kjølle, "Simulating the Long Term Effect of Asset Management Strategies on Reliability of Supply", pre-print submitted for peer review, 2024. DOI: 10.36227/techrxiv.172107759.95745501/v1.</div> <div> See README.md for details.</div> </div>
Datensatz zu: Fachgesellschaften und Open Access in Deutschland – eine Analyse zur Herausgabe von Zeitschriften
<p>In der Debatte um die Open-Access-Transformation wird auch die Rolle wissenschaftlicher Fachgesellschaften diskutiert. Bisher gab es keine systematische Erhebung zum Einfluss von Fachgesellschaften auf das Publikationssystem. Dieser unbefriedigende Forschungsstand führte dazu, dass das Potenzial dieses wichtigen Akteurs bei der Open-Access-Transformation bisher weitgehende unbeachtet blieb und möglichen Barrieren auf Seiten der Fachgesellschaften nicht adressiert wurden. Im Rahmen des Projekts „Options4OA“ wurden darum Publikations- und Open-Access-Aktivitäten deutscher Fachgesellschaften untersucht.</p> <p>Vorliegender Datensatz dokumentiert die dem Poster zugrundeliegenden Forschungsdaten in drei Datensätzen.</p> <p>Diese Datensätze beschreiben 182 Zeitschriften, die wissenschaftliche Fachgesellschaften, die in Deutschland angesiedelt sind, veröffentlichen. Neben allgemeinen Metadaten zu den Zeitschriften und den herausgebenden Fachgesellschaften finden sich in den Datensätzen Informationen zum Open-Access-Status der Zeitschriften und den Open-Access-Publikationsgebühren. Auch sind die Zeitschriften den Notationen der Fachsystematik der Deutschen Forschungsgemeinschaft (DFG) zugeordnet.</p> <p>Das Vorhaben wurde vom Bundesministerium für Bildung und Forschung (BMBF) im Rahmen des Projektes „Options4OA” gefördert (Förderkennzeichen: 16OA034).</p> <p>Weitere Informationen unter: <a href="https://os.helmholtz.de/projekte/options4oa/">https://os.helmholtz.de/projekte/options4oa/</a></p>
Dataset for Training Material - Galaxy Workflow - Analyse unaligned ncRNAs
<p>Input dataset for Galaxy Training Material for the Analyze unaligned ncRNAs workflow.</p> <p>See https://github.com/galaxyproject/training-material for more information.</p>
Datasets for phylogenetic analyses and phylogenetic trees for: Genetic barcodes for species identification and phylogenetic estimation in ghost spiders (Araneae: Anyphaenidae: Amaurobioidinae). Invertebrate Systematics, 2024
<p>We combined the COI sequence data with legacy multigene sequence data to create a new, taxon-rich phylogeny for the Amaurobioidinae. We used sequences for four loci that have been used in previous studies on the subfamily: two mitochondrial loci, COI (658bp) and ribosomal subunit 16S (16S, 410bp); and two nuclear loci, Histone H3 (H3, 327bp) and ribosomal subunit 28S (28S, 839bp). We complemented the Amaurobioidinae data with sequences from several non-amaurobioidine anyphaenids and two clubionids as outgroups. Sequence alignment was performed using the MAFFT (ver. 7.308) plugin in Geneious, allowing MAFFT to automatically select an appropriate alignment strategy based on the properties of each locus, or with the online MAFFT server (https://mafft.cbrc.jp), which consistently selected the L-INS-i algorithm. Finally, alignments of the four loci were concatenated to construct a 2234 bp multigene sequence matrix containing 692 taxa, with about 55% missing/gap data (“full” matrix henceforth). To ensure that excessive missing data did not affect the resulting topology, we also constructed a reduced matrix by removing additional COI-only specimens so that each species and morphotype was represented by just one or two specimens for which all loci were available (where possible). After realignment, this reduced matrix was 2235 bp long, included 167 taxa, and had about 22% missing/gap data (“reduced” matrix henceforth). Phylogenetic analyses under maximum likelihood, including model selection, were then conducted with IQ-TREE 2. We performed phylogenetic analyses on both concatenated matrices (the full matrix and the reduced matrix) and on each individual locus. For model selection, we provided an initial scheme that partitioned the matrix by locus, and further partitioned the protein-coding loci (COI and H3) by codon position. We used ModelFinder and searched for the best partition scheme, all in IQ-TREE. The best models (partitions) for the full dataset were: GTR+F+I+G4 (16S), GTR+F+I+I+R4 (28S), TVM+F+I+I+R2 (COI-1), TIM2+F+R4 (COI-2), GTR+F+R5 (COI-3), TVMe+G4 (H3-1-H3-2), SYM+G4 (H3-3); and for the reduced dataset: GTR+F+I+G4 (16S), GTR+F+I+G4: (28S), GTR+F+I+G4: (COI-2), GTR+F+I+G4: (COI-3), TVM+F+I+G4: (COI-1, H3-2), GTR+F+I+G4: (H3-1), GTR+F+I+G4: (H3-3). For each dataset, once the best models and partitions were defined, we executed 10 independent replicates of tree calculations followed by 1000 ultrafast bootstrap replicates, and the replicate reaching the maximum likelihood was chosen. Phylogenetic analyses under parsimony were made with TNT, under equal weights, using the “new technology” search with default values, asking for 10 independent hits to the minimal length, and submitting the resulting trees to a round of TBR branch swapping. </p>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at NUTS3 level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct NUTS3 regions in continetal Europe. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each NUTS3 area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles (EPSG:4326) sourced from Eurostat's official repository (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts</a>). These shapefiles link the air quality data to precise NUTS3 regions through unique identifiers.</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each NUTS3 polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p>
Data from nitrogen isotopic analyses used to calculate biological nitrogen fixation (BNF) rates and field measurements from lichen, bryophyte, litter, and soil samples in MAT2006 plots, Arctic LTER, Toolik Field Station, Alaska, summers 2022-2023.
This dataset contains nitrogen (N) fixation and isotope data from experimental samples collected at Toolik Lake, Alaska during the 2022 and 2023 growing seasons. Sampling was conducted across multiple block treatments to capture spatial variability and included four substrate types: lichen, moss, litter, and soil. Within each plot, substrates were collected systematically along transects to ensure representative sampling, with lichen samples collected opportunistically due to lower abundance. Samples were incubated in the field under ambient conditions using 15N₂ to measure biological nitrogen fixation (BNF). In 2023, a short-term wetting experiment was conducted to assess the influence of moisture on BNF rates, with subsamples exposed to controlled additions of water. Across both years, data include isotope ratios, incubation conditions, moisture, fresh and dry biomass, and treatment assignments. The dataset provides information on BNF across substrate types, moisture regimes, and fertilization treatments in Arctic tundra. These data support investigation of N cycling processes, the influence of moisture and fertilization on fixation rates, and variability across vegetation types. The dataset is complete for the two field seasons (2022 and 2023) and includes sample- and block-level metadata necessary for reuse in ecological and biogeochemical research.
Catalog of GenBank sequence read archive (SRA) entries of metagenomic DNA sequence analyses of bacterial and archaeal water column communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2012
In contrast to temperate systems, Arctic lagoons that span the Alaska Beaufort Sea coast face extreme seasonality. Nine months of ice cover up to ∼1.7 m thick is followed by a spring thaw that introduces an enormous pulse of freshwater, nutrients, and organic matter into these lagoons over a relatively brief 2–3 week period. Prokaryotic communities link these subsidies to lagoon food webs through nutrient uptake, heterotrophic production, and other biogeochemical processes, but little is known about how the genomic capabilities of these communities respond to seasonal variability. This study characterizes the metabolic capabilities of microbial communities across three seasons in two lagoons and one open coastal site along the eastern Alaska Beaufort Sea coast. We used metagenomic DNA sequence data of bacterial and archaeal water column communities to identify genes of relevant biogeochemical pathways. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA642637 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA642637. This data package is associated with the following publication: Baker, Kristina D., Colleen T. E. Kellogg, James W. McClelland, Kenneth H. Dunton, and Byron C. Crump. “The Genomic Capabilities of Microbial Communities Track Seasonal Variation in Environmental Conditions of Arctic Lagoons.” Frontiers in Microbiology 12 (2021). https://doi.org/10.3389/fmicb.2021.601901. Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provi
Riparian and upland understory vegetation lifeforms and leaf-litterfall ordination analyses in the Luquillo Forest Dynamics Plot
Riparian areas are proportionally a small component of the forested landscape, they are significant contributors to ecosystem process, terrestrial and aquatic linkages, plant community composition, as well to basal energy resources for aquatic fauna. We describe vegetation and leaf-litterfall composition in relation to past land use in riparian and upland locations in tropical wet forest, Luquillo Forest Dynamics Plot (LFDP), Luquillo Experimental Forest, Puerto Rico. Data collected from 2003 to 2005. Stratified sampling was conducted in riparian and upland areas of LFDP with high and low past land use. Understory vegetation life-form composition were sampled in plots. \<para\> Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.\</para\>
Adelie penguin diet composition, preliminary analyses of whole samples, 1991-2024
The fundamental long-term objective of the seabird component of the Palmer LTER (PAL) has been to identify and understand the mechanistic processes that regulate the mean fitness (population growth rate) of regional penguin populations. Since the inception of PAL, Adélie penguin populations have effectively collapsed, gentoo penguin populations have increased dramatically and chinstrap penguin populations have remained relatively stable. These trends are spatially and temporally coherent with regional warming and decreasing sea ice duration. Adélie penguins are an ice-obligate polar species whose life history is intimately linked to the presence of sea ice, while chinstrap and gentoo penguins are ice-intolerant species whose life histories evolved in the sub-Antarctic, where sea ice is a less permanent feature of the marine ecosystem. The PAL study region includes five main islands on which Adélie penguin colonies have historically occurred, with each island containing a different number of spatially segregated sub-colonies. These colonies are censused to determine the total number of nests and chicks produced each year, and breeding success. Diet samples are acquired to understand diet composition (e.g., krill, fish) and krill length-frequencies. In general, krill constitute the most important component of the summer diets by mass of these three penguin species, but changes in PAL krill abundances have exhibited no long-term trends and thus far, have failed to explain the divergent patterns in penguin populations evident in our time series. Chick fledging masses are recorded as a cumulative measure of climate, weather, diet, and parental influences on chick health at the end of the breeding season. These data have provided valuable insights into the marine and terrestrial factors that influence Adélie penguin population fitness. No data were collected during the 2021-2022 season due to the Palmer Station pier rebuild.
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.