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18,657 results for “Impact”
Dataset of "The Impact of Local Strain Fields in Noncollinear Antiferromagnetic Films"
<p>Antiferromagnets hosting structural or magnetic order that breaks time reversal symmetry are of increasing interest for “beyond von Neumann” computing applications because the topology of their band structure allows for intrinsic physical properties, exploitable in integrated memory and logic function. One such group are the noncollinear antiferromagnets. Essential for domain manipulation is the existence of small net moments found routinely when the material is synthesized in thin film form and attributed to symmetry breaking caused by spin canting, either from the Dzyaloshinskii–Moriya interaction or from strain. Although the spin arrangement of these materials makes them highly sensitive to strain, there is little understanding about the influence of local strain fields caused by lattice defects on global properties, such as magnetization and anomalous Hall effect. This premise is investigated by examining noncollinear antiferromagnetic films that are either highly lattice mismatched or closely matched to their substrate. In either case, edge dislocation networks are generated and for the former case, these extend throughout the entire film thickness, creating large local strain fields. These strain fields allow for finite intrinsic magnetization in seemingly structurally relaxed films and influence the antiferromagnetic domain state and the intrinsic anomalous Hall effect. The dataset consists of total energies calculated by density functional theory (VASP). The experimental data presented in the paper were obtained by international partners not supported by OP-JAK project.</p>
Replication Data for: Replication for: How Much Do Startups Impact Employment Growth in the U.S.?
<p>These are data files to support the replication of the blog post "How Much Do Startups Impact Employment Growth in the U.S.?" The replication is not a complete replication attempt. Files were downloaded from the U.S. Census Bureau at https://www.census.gov/ces/dataproducts/bds/data_firm.html on 2019-04-23. A codebook, as provided by the U.S. Census Bureau on the same date, is provided.</p>
Vegetation survey (BACI and Paired-plots) from arid central Australia for impacts of buffel grass on resident native plant communities
<p>The data set accompanies the accepted paper in Ecosphere. The data set includes two experimental appraoches to assess the spread and impacts of buffel grass, Cenchrus cilairis, in the Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands of arid central Australia: a Before-After-Control-Impact (BACI) experiment over 25 years at 15 sites (surveyed in 1994-95 and 2018-19), and a spatially paired-plot (randomised-block) experiment at 18 sites (surveyed in 2018-19). Both experiments spanned two geographic regions (~ 300 km apart) and multiple vegetation communities amongst flat plains and rocky hills landforms. Each experimental design has a plant species data set, and a data set that includes site variables and summed relative cover of plant functional groups. Data collection methodology is described in the accompanying paper, and summarised here.</p> <p>Each site was one hectare in size. The ecological data was collected in accordance with standard biological survey methods in South Australia (Heard and Channon 1997), including recording of plant species and cover abundance, life form, height class and habitat variables including percent bare earth, litter, rock/strew and soil type (clay percent). Fire history for the previous 25 years was also available from fire scar mapping. Species cover-abundance was estimated in the field using a modified Braun-Blanquet scale and later converted to a raw continuous variable based on the mid-point of the cover class: 1% (1-10 plants, <5% cover); 2% (sparsely present, <5% cover; 3% (plentiful but <5% cover); 15% (5 to 25% cover class); 37% (25 to 50% cover class); 63% (50 to 75% cover class). Buffel grass was recorded on the same scale. Plant species were vouchered and identification checked post-field by the South Australian Hebarium. Plant taxonomy reflects current names (as of 2015) in the Biological Databases of South Australia and taxonomy was aligned between the 1990s and 2020s decades. Recently some species have been split into multiple species (e.g. <em>Acacia aneura</em>, Mulga) but this latest taxonomy was not adopted to retain taxonomic alignment within the dataset. The raw mid-point percent cover was converted to relative percent cover by dividing each species’ (or groups’) raw cover by the summed cover of all species at that site (including buffel grass + understorey + overstorey species). Classification of plants into functional groups was based on field assessed (1) height class + (2) life form, and literature-derived (3) life strategy (perennial or annual) + (4) Native status to South Australia. Height classes were grouped into overstorey (>1m in height) and understorey (≤1m). Summed relative cover for each functional group per site is included in the site and cover data sets to facilitate modelling of cover with site variables. The plant species data sets is the full list of species and cover abundance recorded at each site which can be used for analysis of community composition, diversity, turnover or individual species change. Sensitive species (one species in this dataset) has had the coordinates denatured by 10km due according to the requirements of the Biological Database of South Australia for sensitive species. All coordinates provided in MGA 52 Eastings and Northings (UTM, Australian National Grid). </p> <p>The authors wish to acknowledge Traditional Owners and Aṉangu Pitjantjatjara Yankunytjatjara (APY) Lands Organisation who gave permission for collaboration, data collection, photographs and reporting on and about their Traditional Lands. Data is jointly the Intellectual Property of Aṉangu as the Traditional Owners and the author team, and approval has been granted for research and publication use with appropriate acknowledgment of Aṉangu and the author team. The 1990s baseline data is also the Intellectual Property of the South Australian Government and is made publicly available under a licencing agreement with the Biological Databases of South Australia (licence number 2412). Many people assisted in the field during the 1990s and 2020s vegetation surveys and are wholly acknowledged. APY Land Management, Alinytjara Wilurara Landscape Board, Central Land Council, Ten Deserts Project, Charles Darwin University, South Australian Department for Environment and Water, State Herbarium of South Australia, Holsworth Wildlife Research Endowment, Jill Landsberg Trust and Ecological Society of Australia all provided either funding and/or in-kind support of the project. Study conducted with APY Executive Board approval, South Australian Scientific Permit Q26782 and Northern Territory Wildlife Permit 63104. </p> <p> </p> <p> </p>
A large ensemble of CMIP6-based transient climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments often require a large ensemble of local-scale transient climate scenarios. Each ensemble member represents plausible long weather series at a local scale. The climate projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. Moreover, very few projections are usually available for each GCM due to a high computational cost. An alternative approach involves employing a stochastic weather generator to produce a large number of transient scenarios based on the climate projections from GCMs. In a current dataset, transient climate scenarios were generated using the LARS-WG weather generator, based on climate projections from GCMs from the CMIP6 ensemble across 26 representative sites throughout the UK. Each transient scenario spans the period from 2020 to 2090. At each site, 100 transient scenarios were generated for two emission scenarios (SSP2-4.5 and SSP5-8.5) and five selected GCMs from CMIP6 (ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR, and MRI-ESM2-0). The choice of GCMs were based on their performance over northern Europe and their climate sensitivity. The use of a subset of GCMs substantially reduces computational time required for impact assessment, while allowing to quantify uncertainties in impacts related to uncertain future climate. The dataset can be used with impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare, when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</p>
CMIP6-based local-scale climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments require local-scale climate scenarios. The climate change projections from <span>Global Climate Models (GCMs) </span>are difficult to use at local scale due to their <span>coarse spatial and temporal resolution. </span><span>It is important to have climate change scenarios based on GCMs climate projections GCMs ensembles, e.g. CMIP6, downscaled to local scale to account for their inherent uncertainty, and to generate a sufficient large number of </span>realisations <span>to account for inter-annual climate variability and low frequency but high impact extreme climatic events. A</span><span> <span>dataset of future climate change scenarios was therefore generated at </span></span><span>26 representative sites across the UK</span><span> based on the latest </span><span>CMIP6 multi-model ensemble </span><span>downscaled to local-scale by using a </span><span>stochastic weather generator LARS-WG 7.0. The data set provides </span><span>1,000 years of daily weather at each selected site for a baseline (1985-2015), and very near- (2030) and near-future (2050) climate change scenarios, based on five GCMs and two emission scenarios (</span><span>Shared Socioeconomic Pathways - SSPs <em>viz</em>. </span>SSP2-4.5 and <span>SSP5-8.5)</span><span>.</span><span> </span><span>A total of </span>15 GCMs from the CMIP6 ensemble were integrated in LARS-WG 7.0. <span>LARS-WG downscales future climate projections from the GCMs and incorporates changes at local scale in the mean climate, climatic variability, and extreme events by modifying the statistical distributions of the weather variables at each site. </span>Based on the performance of the GCMs over northern Europe and their climate sensitivity, a subset of five GCMs was selected, <em>viz</em>.; ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR and MRI-ESM2-0. The selected GCMs are evenly distributed among the full set of 15 GCMs. The use of a subset of GCMs substantially reduces computational time, while allowing assessment of uncertainties in impact studies related to uncertain future climate projections arising from GCMs.<span> <span>The 1000 years of </span></span>realisations <span>of daily weather for the baseline as well as future climate change scenarios are helpful for estimating </span>seasonality and<span> inter-annual variation, and for detecting short, </span>low frequency but high impact extreme climatic signals, such as heat waves, floods and drought events. The dataset <span>can be used as an input to climate change impact models in various fields, including, </span><span>land and water resources, agriculture and food production, </span>ecology and epidemiology, and <span>human health and welfare. Researchers, breeders, farm and programme managers, social and public sector leaders, and policymakers may benefit from this new dataset when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</span></p>
Dataset from: "Voluntary Control of Task Selection Does Not Eliminate the Impact of Selection History on Attention"
<p>Dataset for Henare, D. T., Kadel, H., & Schubö, A. (2020). Voluntary Control of Task Selection Does Not Eliminate the Impact of Selection History on Attention. <em>Journal of Cognitive Neuroscience</em>, <em>32</em>(11), 2159-2177. <a href="https://doi.org/10.1162/jocn_a_01609">https://doi.org/10.1162/jocn_a_01609</a></p>
Macroeconomic assessment of Climate Change Impacts
<p>Macroeconomic assessment of impacts on: Agriculture, Fishery, Forestry, Sea level rise, Riverine floods, Transport, Energy supply, Energy demand, Labour productivity, plus compounded assessment of all impacts</p>
BIP! DB: A Dataset of Impact Measures for Research Products
<h2>Overview</h2> <p>This dataset contains citation-based impact indicators (also referred as <em>measures</em>) for ~296M distinct persistent identifiers (PIDs) that correspond to various types of research products (publications, datasets, software, and other products).</p> <p>The calculated indicators are organized into categories based on the aspect of impact they capture. </p> <h3>Influence indicators</h3> <p>Reflect the "total" impact of a research product; how established it is in general.</p> <ul> <li><strong><em>Citation Count:</em></strong> The total number of citations of the product, the most well-known influence indicator.</li> <li><strong><em>PageRank score:</em> </strong>An influence indicator based on the PageRank (Page et al., 1999), a popular network analysis method. PageRank estimates the influence of each product based on its centrality in the whole citation network. It alleviates some issues of the Citation Count indicator (e.g., two products with the same number of citations can have significantly different PageRank scores if the aggregated influence of the products citing them is very different - the product receiving citations from more influential products will get a larger score). </li> </ul> <h3>Popularity indicators</h3> <p>Capture the "current" impact of a research product; how popular it currently is.</p> <ul> <li><strong><em>RAM score:</em></strong> A popularity indicator based on the RAM (Ghosh et al., 2011) method. It is essentially a Citation Count where recent citations are considered as more important. This type of "time awareness" alleviates problems of methods like PageRank, which are biased against recently published products (new products need time to receive a number of citations that can be indicative for their impact).</li> <li><strong><em>AttRank score:</em></strong><strong> </strong>A popularity indicator based on the AttRank (Kanellos et al., 2020) method. AttRank alleviates PageRank's bias against recently published products by incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to examine products which received a lot of attention recently.</li> </ul> <h3>Impulse indicators</h3> <p>Measure the initial momentum that a research product received right after its publication.</p> <ul> <li><em><strong>Incubation Citation Count (3-year CC):</strong> </em>This impulse indicator is a time-restricted version of the Citation Count, where the time window length is fixed for all products and the time window depends on the publication date of the product, i.e., only citations 3 years after each product's publication are counted.</li> </ul> <h3>FIeld-weighted indicators</h3> <p>Capture the impact of a research product relative to the average performance in its field, accounting for differences in citation practices across disciplines.</p> <ul> <li><strong>Field-Weighted Citation Impact (FWCI):</strong> A field-weighted indicator that measures how a research product performs compared to the global average in its research field. An FWCI of 1.0 indicates that the product is cited exactly as expected for similar publications in the same field; values above 1.0 indicate above-average impact, while values below 1.0 indicate below-average impact.</li> <li><strong>3-year FWCI:</strong> A time-restricted version of the FWCI that considers citations received within the first three years after publication. By limiting the citation window, this indicator captures the early relative impact of a research product, providing insight into how quickly it gains influence in its field.</li> </ul> <p>In our analysis, the expected number of citations for each research product is computed by <em>grouping them by concept, publication year, and product type and then averaging the citations within each group</em>. </p> <p><em>More details about the aforementioned impact indicators, the way they are calculated and their interpretation can be found <a href="https://bip.imsi.athenarc.gr/site/indicators">here</a> and in the respective references (Kanellos et al., 2019).</em></p> <h2>Indicator calculation levels</h2> <p>The impact indicators are calculated in two levels:</p> <ul> <li><strong>PID level: </strong> assuming that each PID corresponds to a distinct research product. Currently PIDs are DOIs, PMCIDs, and PMIDs.</li> <li><strong>OpenAIRE-id level: </strong>leveraging PID synonyms based on OpenAIRE's deduplication algorithm (Manghi et al., 2020) - each distinct article has its own OpenAIRE id.</li> </ul> <h2>Impact classes</h2> <p>Each researcj product is also assigned an impact class, reflecting its percentile rank among all products in the dataset: </p> <table style="border-collapse: collapse; width: 100%; height: 39.1876px;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>Class</strong></td> <td style="height: 19.5938px;"><strong>Percentile</strong></td> <td style="height: 19.5938px;"><strong>Description</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">C1</td> <td style="height: 19.5938px;">Top 0.01%</td> <td style="height: 19.5938px;">Exceptional impact</td> </tr> <tr> <td>C2</td> <td>Top 0.1%</td> <td>Very high impact</td> </tr> <tr> <td>C3</td> <td>Top 1%</td> <td>High impact</td> </tr> <tr> <td>C4</td> <td>Top 10%</td> <td>Good impact</td> </tr> <tr> <td>C5</td> <td>Rest 90%</td> <td>Remaining products</td> </tr> </tbody> </table> <h2>File structure</h2> <p>For each calculation level (PID / OpenAIRE-id) we provide five (5) compressed CSV files (one for each measure/score provided). The structure of the files differs slightly depending on the level:</p> <ul> <li> <p><strong>PID-level files:</strong> Each line follows the format:<br><code>identifier <tab> identifier_type <tab> score <tab> class</code></p> </li> <li> <p><strong>OpenAIRE-id-level files:</strong> These files contain the keyword "openaire_ids" in the filename. Each line follows the format:<br><code>identifier <tab> score <tab> class</code></p> </li> </ul> <p><em>The parameter setting of each measure is encoded in the corresponding filename. For more details on the different measures/scores see our extensive experimental study (Kanellos et al., 2019) and the configuration of AttRank in the original paper (Kanellos et al., 2020).</em></p> <h3>Topic-related files</h3> <p>In addition to the main indicator files, the dataset also includes <em>topic-level outputs</em>, providing <em>field-weighted impact indicators</em> as well <em>percentile classes</em> within the associated <em>2nd-level concepts from OpenAlex</em>. </p> <p>Specifically, we associated all research products with their 2nd level concepts from OpenAlex (using only their <em>DOIs</em>); we kept only the three most dominant concepts for each product, based on their confidence score, and only if this score was greater than 0.3.</p> <p>Since currently only the DOIs are used to associate concepts from OpenAlex to research products, all identifiers in these files refer to DOIs. </p> <ul> <li><strong>Topic-specific impact classes file:</strong> Fore each concept and indicator, precentile classes are computed and provided in <code>topic_based_impact_classes.txt</code> in the following format:</li> </ul> <p><code>identifier <tab> concept <tab> pagerank_class <tab> attrank_class <tab> 3-cc_class <tab> cc_class</code></p> <ul> <li><strong>Field-weighted indicator files:</strong> Each line follows the format:<br><code>identifier <tab> concept <tab> score</code></li> </ul> <p><em>Note that to prevent division by zero, the score column is left empty whenever the average score for a specific combination of concept, publication year, and product type equals zero.</em></p> <h2>Data sources</h2> <p>The data used to produce the citation network on which we calculated the provided measures have been gathered from the OpenAIRE Graph v10.5.0, including data from (a) <em>OpenCitations' COCI & POCI dataset</em>, (b) <em>MAG</em> (Sinha et al, 2015; Wang et al., 2019), and (c) <em>Crossref</em>. The union of all distinct citations that could be found in these sources have been considered. </p> <p>Additionally, all topic-related computations are derived from OpenAlex concepts.</p> <h2>Access and Use</h2> <p>Find our Academic Search Engine built on top of these data <a href="https://bip.imsi.athenarc.gr/">here</a>. Further note, that we also provide all calculated scores through <a href="https://bip-api.imsi.athenarc.gr/documentation">BIP! Finder's API</a>. </p> <p><em>Terms:</em> These data are provided "as is", without any warranties of any kind. The data are provided under the CC0 license.</p> <h2>Changelog</h2> <p><strong>v19.1</strong></p> <ul> <li>[major update] Added field-weighted indicators: FWCI and 3-year FWCI.</li> </ul> <p><strong>v19.0</strong></p> <ul> <li>Added PMCID as an additional type of PID.</li> </ul> <p><strong>v15.1</strong></p> <ul> <li>Fixed missing records that were unintentionally omitted in v15.0</li> <li>Ensures all popularity indicators correctly use <code>current_year = 2025</code></li> </ul> <p><strong>v12.0</strong></p> <ul> <li>Added PMIDs as an additional type of PID.</li> </ul> <p><strong>v10.0</strong></p> <ul> <li>[Major update] Introduced deduplication of research products using the latest <a href="https://graph.openaire.eu/docs/graph-production-workflow/deduplication/research-products">OpenAIRE article deduplication algorithm</a>. Each node in the citation network is now a deduplicated product having a distinct OpenAIRE id. <ul> <li>Corrected overcounting of citations caused by multiple versions of the same product.</li> <li>PID-level scores are now derived from deduplicated OpenAIRE nodes.</li> </ul> </li> <li>Added filtering rules described <a href="https://graph.openaire.eu/docs/graph-production-workflow/aggregation/non-compatible-sources/doiboost/#crossref-filtering">here</a> to remove from dataset PIDs with problematic metadata. </li> </ul> <p><strong>v9.0</strong></p> <ul> <li>[Major update] Introduced topic-specific impact classes for PID-identified products based on OpenAlex 2nd-level concepts.</li> </ul> <p><strong>v7.0</strong></p> <ul> <li>[Major update] Added impact class labels (C1-C5) for each procuct, indicating the percentile-bsaed impact levels. <ul> <li>Classes reflect relative position within the global score distribution.</li> </ul> </li> </ul> <p><strong>v5.1</strong></p> <ul> <li>[Major update] Introduced dual-level score computation: PID level and OpenAIRE ID level.</li> </ul>
Dataset for paper "Ejecta cloud distributions for the statistical analysis of impact cratering events onto asteroids' surfaces: a sensitivity analysis"
<p>Dataset for the paper "Ejecta cloud distributions for the statistical analysis of impact cratering events onto asteroids' surfaces: a sensitivity analysis" published in Icarus.</p>
The Impact of Gulf Stream Frontal Eddies on Ecology and Biogeochemistry near Cape Hatteras
<p>This data goes along with the manuscript "The Impact of Gulf Stream Frontal Eddies on Ecology and Biogeochemistry near Cape Hatteras" available as a preprint at https://doi.org/10.1101/2023.02.22.529409 and under review in the Journal of Geophysical Research: Oceans. The code to analyze this data and generate the figures from the paper is available at: https://github.com/patrickcgray/gs_front_analysis (https://doi.org/10.5281/zenodo.7685135).</p>
Stream Restoration and Flood Impacts in the Kickapoo River Watershed, Wisconsin, 2019
Data were collected from May to November 2019 at five sites on two stream reaches in the Kickapoo River Watershed. Sites include Billings Creek restoration site (BRES), Billings Creek reference site (BREF), Warner Creek upstream site (WUP), Warner Creek middle site (WMD), and Warner Creek downstream (WDN) site. Data were collected by Dr. Caroline Gottschalk Druschke as part of research into the impacts of stream restoration and flooding on Kickapoo River Watershed (WI, USA) streams. Data were collected on methane and carbon dioxide fluxes on all reaches, as well as cross sectional area and soft sediment depth on the restored reach on Billings Creek before and after restoration in July 2019.
Flume Experiment Testing the Impact of Artificial Streambank Roots on Velocity, Reynold's Shear Stress, and Turbulent Kinetic Energy using an Acoustic Doppler Profiler
The data published here is expected to accompany one publicly available dissertation (Chapter 4 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Impact of Flexible and Rigid Artificial Roots on Stream Hydrodynamics
Harmful algal bloom and aquatic weeds data from the Sacramento-San Joaquin Delta, collected to evaluate the impact of the 2021 Temporary Urgency Change Order and Emergency Drought Barrier
Condition 8 of the June 2021 Temporary Urgency Change Order for the Central Valley Project (CVP) and State Water Project (SWP) requires a special study of harmful algal blooms (HABs) in the Sacramento–San Joaquin Delta (Delta) and the spread of submersed aquatic vegetation (SAV), and floating aquatic vegetation (FAV), also referred to as “aquatic weeds”. A report on the study was submitted to the State Water Resources Control Board on June 1, 2022. This data package contains all publicly available data used in the report, including visual cyanobacteria reports, cyanotoxin data, water quality, nutrients, flow/hydrodynamics, chlorophyll-a concentrations, temperature, coverage of SAV and FAV, use of herbicides, and human populations. Many of these data were derived from other datasets, though some were collected specifically for this study
Impact of Snowmelt Timing and Tree Proximity on Dutchman's Breeches Phenology and Performance in Mont Megantic National Park (Quebec, Canada; 2018-2019)
Data herein were collected in 2018 and 2019 in Mont Megantic National Park, Quebec, Canada, in a sugar maple-dominated temperate deciduous forest. Individuals of Dutchman's breeches (Dicentra cucullaria), a common understory spring ephemeral plant that is only active in the spring, were transplanted into a fully factorial experiment of snowmelt timing (early vs. late) and tree proximity (near vs. far) to determine the role of thaw circle formation in the local clustering of this species near canopy tree trunks. Plant phenology (emergence, senescence, and growing season length) and performance (stem abundance and leaf area) were tracked during two years of snow manipulation. Additionally, microclimate temperature data were collected in a subset of plots in 2018.
Impacts of invasive species on food web energy pathways and quality, St. Lawrence River, 2018-2021.
This dataset contains field measurements collected between 2018 and 2021 from three fluvial lakes in the Upper St. Lawrence River (Canada), including both invaded systems (with dreissenid mussels and round goby) and uninvaded reference sites. Data include georeferenced sampling information (site, lake, latitude, longitude, month, year), water chemistry (total phosphorus, µg/L; conductivity, µS/cm), and habitat descriptors (substrate). Biological records encompass seston, macroinvertebrates, and fish. Fish data comprise species identity, sex, total length (mm), weight (g), relative weight index (Wr), and detailed fatty acid composition expressed as relative proportions (%) and concentrations (µg/mg), including essential LC-PUFAs (EPA, DHA), n-3 and n-6 polyunsaturated fatty acids. Stable isotope data are provided, including carbon (δ13C) and nitrogen (δ15N) ratios, C:N ratios, and isotopic baselines from pelagic (δ13Cpel, δ15Npel) and benthic (δ13Cben, δ15Nben) sources. Derived variables, such as pelagic diet proportion and trophic position, were calculated using the two-source mixing model described by Post (2002) (DOI: https://doi.org/10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2). These data provide a comprehensive resource for examining food web structure, energy pathways, and the ecological impacts of invasive species in large river ecosystems.
Microbial and soil moisture impacts of compost amendments and rainfall pulses in a degraded dryland soil, Arizona, 2021-2023
Compost, an organic soil amendment, has been proposed to increase soil carbon storage and water-holding capacity in drylands, and this management strategy may be particularly impactful in degraded drylands with low soil organic content. Compost additions and rainfall variability may interact to affect soil moisture, which is an important catalyst for soil microbial activity. This dataset is from a study that investigated how variable compost application amounts and simulated rainfall pulses affect soil moisture, microbial activity, and carbon content in a laboratory incubation study. Soils were amended with different amounts of compost (0, 0.35, and 0.70 g cm -2) and water pulses (5, 10, and 15 mm) in a full-factorial design. Each treatment received the same cumulative amount of water throughout the incubation, but pulses occurred at different frequencies (every 5, 10, and 15 days). Soil moisture content and microbial respiration were measured daily. Soil carbon content was measured at the end of the experiment.
Indirect impacts of a novel wildfire on a well-studied desert stream: connectivity, carbon, and communities
In 2020 the Bush Fire burned approximately half of the Sycamore Creek watershed in central Arizona. Sycamore Creek has been subject to >40 years of research and the stream has been monitored by NEON since 2017. We studied the effects of fire on biogeochemistry of the stream and its watershed. We deployed autosamplers to monitor stream chemistry during storms on the mainstem and in ephemeral tributaries draining burned and unburned watersheds. The storm sampling program commenced nearly a year following the fire because absence of summer monsoon or winter storms in 2020-21 resulted in no flow in tributaries and intermittent flow in the mainstem. Water chemistry was measured during 14 monsoon storms of 2021 and winter frontal storms of 2021-22 with samples of baseflow collected in the mainstem during intervening periods. Water samples were analyzed for dissolved organic carbon, nitrogen, phosphorus, and major anions and cations. We also measured nutrient content of ash and chemistry of ash leachate as a potential source of solutes to stream biota.
Global Climate Change Impacts on the Vegetation and Fauna of Mangrove Forested Ecosystems in Florida (FCE): Nekton Portion from March 2000 to April 2004
Depth is measured at 3 random locations within each net at time of set. All other variables (salinity, temperature, dissolved oxygen) are measured at the river bank adjacent to each net also at the time of set. Minimum and maximum values for sites were found to be: Salinity(ppt) = SRSMc-S2: 0.3-14.7, SRSMc-S3: 15.6-34.4, SRSMc-S4: 2.4-34; Water temp(degrees C)= SRSMc-S2: 22.2-31.5, SRSMc-S3: 16.6-31.1, SRSMc-S4: 21.1-30.6; DO(mg/l)= SRSMc-S2: 2.55-5.27, SRSMc-S3: 2.08-5.3, SRSMc-S4: 1.25-4.2; Mean depth(cm)= SRSMc-S2: 0.0-24.6, SRSMc-S3: 5.7-41.5, SRSMc-S4: 0.0-21.4
Global Climate Change Impacts on the Vegetation and Fauna of Mangrove Forested Ecosystems in Florida (FCE): Nekton Mass from March 2000 to April 2004
Bottomless lift nets are buried within the mangrove forest floor and raised remotely on slack high spring tides to enclose a 6m2 area. As the tide ebbs, fishes retreat into a subtidal refuge cleared when the tide has fallen. Three replicate nets have been sampled at 3 locations along a salinity gradient on Shark River for 4 years. Small resident forage fish and grass shrimp dominate the collections. Exotic species and estuarine transient species that use the estuary as a nursery are rare within the assemblage of fishes that routinely use the flooded forest.
Quantifying the magnitude of storm events that have impacted the Virginia Coast Reserve (2009-2024) using the Cumulative Storm Impact Index (CSII)
This dataset contains a record of storm events along with quantified magnitudes that have impacted the Virginia Coast Reserve between 2009- 2024, minus 2010. We retrieved hourly water level data and monthly datums from the NOAA Tides and Currents database (tidesandcurrents.noaa.gov) for the tide station located in Wachapreague, VA (Station 8631044) to quantify the magnitude of storms using 1) the Storm Erosion Potential Index (SEPI; Zhang et al. 2001), and 2) the Cumulative Storm Impact Index (CSII; Fenster and Dominguez 2022). CSII incorporates the timing and magnitude of previous storms as a measure of cumulative impact, or "storminess". We identified storm events based on storm surge that exceeded two standard deviations (> 2SD) of the average surge and storm tide that exceeded the annual average Mean High Water (MHW) of a semi-diurnal tide (12 hours; SEPI). We then calculated the CSII for each storm as the sum of the SEPI and an exponentially decaying weighting factor (delta) from the previous storm's CSII that accounts for beach recovery that may have occurred between storm events. Here we use delta = 0.3 to best capture storm clustering during the 15 year period (Fenster and Dominguez 2022). Years missing >10% of data were excluded. For detailed methods on the data retrieval process, identifying storms, and quantifying storm magnitude, see Fenster and Dominguez (2022) and Dominguez et al. (2024). We identified a total of 208 storm events with an average of 14.3 events per year +/- 2.3 (SD) and an average annual CSII of 428.1 (m2hr) +/- 196.1 (SD).
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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.