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665 results for “foundation”
Foundation Species Revisited: Citation Analysis of Ellison et al. 2005
Ecologists and environmental scientists often prioritize research efforts with conservation importance. Dominant, widespread, or locally abundant species at low risk of extinction receive relatively little attention unless they are invasive. Native foundation species create habitats and environmental conditions that support many associated species and modulate local-scale ecosystem processes, but the generally high local or regional abundance of foundation species may lead to less research about them. We used citation analysis (2005-2014) to examine research following from a suggestion to identify and study foundation species while they were still common and not threatened. We explored the use and expanding definition of the foundation species concept, as well as the trajectory and ecological focus of research on foundation species throughout the world in 378 papers published in this nine-year span. Contemporary authors who cite key papers defining a foundation species pay little attention to its actual definition and species studied in this context rarely were identified as foundation species. Although functions and roles of foundation species, such as creating unique microclimates or supporting dependent species, are being studied, less research is focused on identifying them before they are threatened or lost from the ecosystem that they otherwise define. Invasive species were identified as the most common threat to foundation species. Our citation analysis and synthesis provides a new conceptual framework linking identification of and research about foundation species with their functional roles and our ability to manage emerging threats to them.
Foundation Species in Forest Dynamics Plots of China 2004-2014
Foundation species structure forest communities and ecosystems but are difficult to identify without long-term observations or experiments. We used statistical criteria---outliers from size-frequency distributions and scale-dependent negative effects on alpha diversity and positive effects on beta diversity---to identify candidate foundation woody plant species in 12 large forest-dynamics plots spanning 26 degrees of latitude in China. We used these data to: [1] identify candidate foundation species in Chinese forests; [2] test the hypothesis---based on observations of a mid-latitude peak in functional trait diversity and high local species richness but few numerically dominant species in tropical forests---that foundation woody plant species are more frequent in temperate than tropical or boreal forests; and [3] compare these results with data from the Americas to suggest candidate foundation genera in Northern Hemisphere forests. Using the most stringent criteria, only two species of Acer, the canopy tree Acer ukurunduense and the shrubby treelet Acer barbinerve, were identified in temperate plots as candidate foundation species. Using more relaxed criteria, we identified four times more candidate foundation species in temperate plots (including species of Acer, Pinus, Juglans, Padus, Tilia, Fraxinus, Prunus, Taxus, Ulmus, and Corlyus) than in (sub)tropical plots (the shrubs or treelets Aporosa yunnanensis, Ficus hispida, Brassaiopsis glomerulata, and Orophea laui). Species diversity of co-occurring woody species was negatively associated with basal area of candidate foundation species more frequently at 5- and 10-m spatial grains (scale) than at a 20-m grain. Conversely, Bray-Curtis dissimilarity was positively associated with basal area of candidate foundation species more frequently at 5-m than at 10- or 20-m grains. Using either stringent or relaxed criteria supported the hypothesis that foundation species are more common in mid-latitude temperate forests. Com
Modeling Foundation Species in Food Webs
Foundation species are basal species that play an important role in determining community composition by physically structuring ecosystems and modulating ecosystem processes. Foundation species largely operate via non-trophic interactions, presenting a challenge to incorporating them into food-web models. Here, we used non-linear, bioenergetic predator-prey models to explore the role of foundation species and their non-trophic effects. We explored four types of models in which the foundation species reduced the metabolic rates of species in a specific trophic position. We examined the outcomes of each of these models for six metabolic rate “treatments” in which the foundation species altered the metabolic rates of associated species by one-tenth to ten times their allometric baseline metabolic rates. For each model simulation, we looked at how foundation species influenced food-web structure during community assembly and the subsequent change in food-web structure when the foundation species was removed. When a foundation species lowered the metabolic rate of only basal species the resultant webs were complex, species-rich, and robust to foundation species removals. On the other hand, when a foundation species lowered the metabolic rate of only consumer species, all species, or no species the resultant webs were species poor and the subsequent removal of the foundation species webs resulted in the further loss of species and complexity. This suggests that in nature we should look for foundation species to predominantly facilitate basal species.
Ungulate Browsing and Foundation Tree Regeneration in Central New England 2010
Large herbivores are important forest disturbances capable of altering community composition, biodiversity, tree density, successional pathways, and nutrient cycling. The extent to which browsers exert important impacts on forests, however, depends on the intensity and duration of browsing, the palatability and tolerance of the vegetation, available resources to plants, and the scale at which these factors are being investigated. Few studies have examined ungulate impacts at the landscape scale. Since the late-1980s moose have recolonized their pre-historical range in southern New England, joined white-tailed deer to create a potentially important new driver of forest dynamics in the region. Few, if any, studies have looked at combined deer and moose impacts on temperate forests in eastern North America. In the summer of 2010, we initiated a landscape-scale observational study on ungulate habitat use and browsing on foundation tree species (Quercus spp. and Tsuga canadensis) in unlogged forests. Seventy-two forest plots were sampled across several ecoregions in central and western Massachusetts, southern Vermont and New Hampshire and northern Connecticut. Tree seedlings, overstory characteristics, browsing, pellet piles, and shrub densities were sampled; and site attributes such as mean annual temperature and forest fragmentation were examined.
Supplementary Data to journal publication on 'The Foundations of the Patagonian Icefields'
<p>Partitioning and comparison of ice discharge estimates from the the Patagonian Icefields comprising associated uncertainties. For further details please refer to the notes in the individual files and/or consult the associated publication entitled 'The Foundations of the Patagonian Icefields' published in Communications Earth & Environment.</p>
Data for: A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks
<p>This dataset shows the results obtained for a case study at TRL4 for the research paper title <em><strong>A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks</strong></em>, with DOI: https://doi.org/10.1016/j.jobe.2023.107625</p> <p>This dataset is an enhanced IFC (Industry Foundation Classes) file with the creation of the BACN (Building Automation Control Network). This IFC file includes the devices created automatically by the BACN2BIM tool (developed by CARTIF Technology Centre) for the case study validated at TRL4. The original IFC was obtained from the Institute for Automation and Applied Informatics (IAI) / Karlsruhe Institute of Technology (KIT) https://www.ifcwiki.org/images/e/e3/AC20-FZK-Haus.ifc, under an unrestricted license, as served as one of the case studies for this research.</p> <p>*Depending on the IFC viewer used, the included sensors may not be represented correctly. In this case, it is recommended to try with another IFC viewer, for example xBIM explorer https://docs.xbim.net/downloads/xbimxplorer.html or BimCollab Zoom Free https://www.bimcollab.com/en/support/downloads/</p>
Omnibus Poll Ukraine - August 2023 (Ilko Kucheriv Democratic Initiatives Foundation + Razumkov Centre) – Random-sample questionnaire-based representative poll
This data collection offers a representative omnibus survey of the Ukrainian population, living in territories controlled by the Ukrainian government without ongoing armed hostilities. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation together with the sociological service of the Razumkov Center from 09 to 15 August 2023. The survey was conducted using a stratified multi-stage sample. The structure of the sample reflects the demographic structure of the adult population of the surveyed territories as of the beginning of 2022 (by age, gender, type of settlement). 2019 respondents aged 18 and older were interviewed. The theoretical sampling error does not exceed 2.3%. At the same time, additional systematic sample deviations may be caused by the consequences of Russian aggression, in particular, the forced evacuation of millions of citizens. The survey covers five thematic fields: assessment of the current situation in the country, the Russian war of aggression, energy sector, corruption, volunteering. This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation are also included in this data collection as separate pdf-file. Additionally, the data collection contains three files with "selected results" which document some major results of the survey in the form of analytical summaries and descriptive statistics: two in English, covering assessment of the current situation in the country + the Russian war of aggression as well as volunteering; one in Ukrainian covering corruption. New in version 1.1: The numbering of questions in the separate questionnaire (file "DIF_CR_0823-questionnaire-revised.pdf") has been adjusted to the numbering in the original data file ("DIF_CR_0823.sav"). A third file with "selected results" has been added. New in version 1.2: An English translation of the questionnaire has been added under "files".
Omnibus Poll Ukraine - July 2023 (Ilko Kucheriv Democratic Initiatives Foundation + Kyiv International Institute of Sociology) – Random-sample questionnaire-based representative poll
This data collection offers a representative omnibus survey of the Ukrainian population, living in territories controlled by the Ukrainian government without ongoing armed hostilities. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation together with the Kyiv International Institute of Sociology from 03 to 17 July 2023. A description of the methodology is given on p.2 of the "selected results" file, which is part of this data collection. The poll covers the following thematic fields: jobs + entrepreneurship, corruption, economic situation, healthcare sector, war, people under Russian occupation. This data collection contains the original survey data. The SPSS file (.sav) is the original file provided by the Ilko Kucheriv Democratic Initiatives Foundation. It has been exported into an Excel file. The content of the respective xlsx-file should be identical with the original sav-file. The sav-file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation are also included in this data collection as separate pdf-files. Additionally, the data collection contains one file with "selected results" which document some major results of the survey in the form of a analytical summaries and descriptive statistics and another file with a clarification concerning the interpretation of question 5.24 about the president's "personal responsibility" for corruption in the country. These files are in Ukrainian only. New in version 1.1: An English translation of the questionnaire has been added under "files".
First Street Foundation Property Level Flood Risk Statistics V1.3
<p>The property level flood risk statistics generated by the First Street Foundation Flood Model Version 1.3 come in CSV format. The data that is included in the CSV includes:</p> <ul> <li> <p>An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.</p> </li> <li> <p>The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.</p> </li> <li> <p>The property’s Flood Factor as well as data on economic loss.</p> </li> <li> <p>The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms in 2021, 2036, and 2051.</p> </li> <li> <p>Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for years 2021, 2036, and 2051.</p> </li> <li> <p>Information on historical events and flood adaptation, such as ID and name.</p> </li> </ul> <p>You can download a sample of the property level flood risk statistics generated by First Street's Flood Model on this page. You can purchase the property level data for areas within the contiguous United States on the First Street website <a href="https://firststreet.org/data-access/paid-access/?utm_source=Property_Statistics&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component">here</a>. You can find the data dictionary which breaks down the data that is available with each property-level data purchase <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/data-dictionary/?utm_source=Property_Statistics&utm_medium=Data_Dictionary&utm_campaign=Zenodo">here</a>. If you are also interested in the hazard layers, you can find more information <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Property_Statistics&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo">here</a>.</p>
First Street Foundation Property Level Flood Risk Statistics V2.0
<p>The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format. </p> <p>The data that is included in the CSV includes:</p> <ul> <li> <p>An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.</p> </li> <li> <p>The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.</p> </li> <li> <p>The property’s Flood Factor as well as data on economic loss.</p> </li> <li> <p>The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.</p> </li> <li> <p>Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.</p> </li> <li> <p>Information on historical events and flood adaptation, such as ID and name.</p> </li> </ul> <p> </p> <p>This dataset includes <a href="https://firststreet.org/">First Street</a>'s aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various <a href="https://floodfactor.com/">Flood Factor</a> risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.</p> <p>If you are interested in acquiring First Street flood data, you can request to access the data <a href="https://firststreet.org/data-access/paid-access/?utm_source=Summary_Statistics_v1.3&utm_medium=Purchase_Data&utm_campaign=Zenodo#pricing-component">here</a>. More information on First Street's flood risk statistics can be found <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-data-dictionaryv2/">here</a> and information on First Street's hazards can be found <a href="https://firststreet.org/data-access/getting-started-with-first-street-data/documentation-hazard-dictionary/?utm_source=Summary_Statistics_v1.3&utm_medium=Hazard_Dictionary&utm_campaign=Zenodo">here</a>.</p> <p>The data dictionary for the parcel-level data is below.</p> <table> <tbody> <tr> <td> <p><strong>Field Name</strong></p> </td> <td> <p><strong>Type</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>fsid</p> </td> <td> <p>int</p> </td> <td> <p>First Street ID (FSID) is a unique identifier assigned to each location</p> </td> </tr> <tr> <td> <p>long</p> </td> <td> <p>float</p> </td> <td> <p>Longitude</p> </td> </tr> <tr> <td> <p>lat</p> </td> <td> <p>float</p> </td> <td> <p>Latitude</p> </td> </tr> <tr> <td> <p>zcta</p> </td> <td> <p>int</p> </td> <td> <p>ZIP code tabulation area as provided by the US Census Bureau</p> </td> </tr> <tr> <td> <p>blkgrp_fips</p> </td> <td> <p>int</p> </td> <td> <p>US Census Block Group FIPS Code</p> </td> </tr> <tr> <td> <p>tract_fips</p> </td> <td> <p>int</p> </td> <td> <p>US Census Tract FIPS Code</p> </td> </tr> <tr> <td> <p>county_fips</p> </td> <td> <p>int</p> </td> <td> <p>County FIPS Code</p> </td> </tr> <tr> <td> <p>cd_fips</p> </td> <td> <p>int</p> </td> <td> <p>Congressional District FIPS Code for the 116th Congress</p> </td> </tr> <tr> <td> <p>state_fips</p> </td> <td> <p>int</p> </td> <td> <p>State FIPS Code</p> </td> </tr> <tr> <td> <p>floodfactor</p> </td> <td> <p>int</p> </td> <td> <p>The property's Flood Factor, a numeric integer from 1-10 (where 1 = minimal and 10 = extreme) based on flooding risk to the building footprint. Flood risk is defined as a combination of cumulative risk over 30 years and flood depth. Flood depth is calculated at the lowest elevation of the building footprint (largest if more than 1 exists, or property centroid where footprint does not exist)</p> </td> </tr> <tr> <td> <p>CS_depth_RP_YY</p> </td> <td> <p>int</p> </td> <td> <p>Climate Scenario (low, medium or high) by Flood depth (in cm) for the Return Period (2, 5, 20, 100 or 500) and Year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_depth_002_year00</p> </td> </tr> <tr> <td> <p>CS_chance_flood_YY</p> </td> <td> <p>float</p> </td> <td> <p>Climate Scenario (low, medium or high) by Cumulative probability (percent) of at least one flooding event that exceeds the threshold at a threshold flooding depth in cm (0, 15, 30) for the year (today or 30 years in the future). Today as year00 and 30 years as year30. ex: low_chance_00_year00</p> </td> </tr> <tr> <td> <p>aal_YY_CS</p> </td> <td> <p>int</p> </td> <td> <p>The annualized economic damage estimate to the building structure from flooding by Year (today or 30 years in the future) by Climate Scenario (low, medium, high). Today as year00 and 30 years as year30. ex: aal_year00_low</p> </td> </tr> <tr> <td> <p>hist1_id</p> </td> <td> <p>int</p> </td> <td> <p>A unique First Street identifier assigned to a historic storm event modeled by First Street</p> </td> </tr> <tr> <td> <p>hist1_event</p> </td> <td> <p>string</p> </td> <td> <p>Short name of the modeled historic event</p> </td> </tr> <tr> <td> <p>hist1_year</p> </td> <td> <p>int</p> </td> <td> <p>Year the modeled historic event occurred</p> </td> </tr> <tr> <td> <p>hist1_depth</p> </td> <td> <p>int</p> </td> <td> <p>Depth (in cm) of flooding to the building from this historic event</p> </td> </tr> <tr> <td> <p>hist2_id</p> </td> <td> <p>int</p> </td> <td> <p>A unique First Street identifier assigned to a historic storm event modeled by First Street</p> </td> </tr> <tr> <td> <p>hist2_event</p> </td> <td> <p>string</p> </td> <td> <p>Short name of the modeled historic event</p> </td> </tr> <tr> <td> <p>hist2_year</p> </td> <td> <p>int</p> </td> <td> <p>Year the modeled historic event occurred</p> </td> </tr> <tr> <td> <p>hist2_depth</p> </td> <td> <p>int</p> </td> <td> <p>Depth (in cm) of flooding to the building from this historic event</p> </td> </tr> <tr> <td> <p>adapt_id</p> </td> <td> <p>int</p> </td> <td> <p>A unique First Street identifier assigned to each adaptation project</p> </td> </tr> <tr> <td> <p>adapt_name</p> </td> <td> <p>string</p> </td> <td> <p>Name of adaptation project</p> </td> </tr> <tr> <td> <p>adapt_rp</p> </td> <td> <p>int</p> </td> <td> <p>Return period of flood event structure provides protection for when applicable</p> </td> </tr> <tr> <td> <p>adapt_type</p> </td> <td> <p>string</p> </td> <td> <p>Specific flood adaptation structure type (can be one of many structures associated with a project)</p> </td> </tr> <tr> <td> <p>fema_zone</p> </td> <td> <p>string</p> </td> <td> <p>Specific FEMA zone categorization of the property ex: A, AE, V. Zones beginning with "A" or "V" are inside the Special Flood Hazard Area which indicates high risk and flood insurance is required for structures with mortgages from federally regulated or insured lenders</p> </td> </tr> <tr> <td> <p>footprint_flag</p> </td> <td> <p>int</p> </td> <td> <p>Statistics for the property are calculated at the centroid of the building footprint (1) or at the centroid of the parcel (0)</p> </td> </tr> </tbody> </table> <p> </p>
The use of Foundational Ontologies in Bioinformatics - Supplementary Material
<p>Supplementary material for the paper "The use of Foundational Ontologies in Bioinformatics".</p>
IN01004 Podagadh Foundation of Visnu Footprint of Skandavarman. Sanskrit XML file
<p>IN01004 Poḍāgaḍh Foundation of Viṣṇu Footprint of Skandavarman. Sanskrit XML file (without metadata).</p>
Related Works for the National Science Foundation of Sri Lanka and the National Sleep Foundation retrieved from the DataCite Commons.
<p>These data were retrieved in order to help understand the use of funder identifiers associated with common acronyms like NSF. They include the following fields: doi, registrationAgency, type, publisher, publicationYear, funderName, funderIdentifier, and awardNumber retrieved from DataCite Commons using the query</p> <div> <div>{organization(id: "' + ror + '") {id name works(first:2000) { totalCount pageInfo {endCursor hasNextPage} nodes {doi type registrationAgency {name} publisher {name} publicationYear fundingReferences {funderName funderIdentifier awardNumber}}}}}'</div> <div> </div> <div>The files are identifier with RORs:</div> <div><a href="../api/records/11116776/draft/files/00zc1hf95_relatedWorks_20240505_10.csv/content" target="_blank" rel="noopener noreferrer">00zc1hf95_relatedWorks_20240505_10.csv</a> are data for the National Sleep Foundation</div> <div><a href="../api/records/11116776/draft/files/010xaa060_relatedWorks_20240505_10.csv/content" target="_blank" rel="noopener noreferrer">010xaa060_relatedWorks_20240505_10.csv</a> are data for the National Science Foundation of Sri Lanka</div> <div> </div> <div>A blog post describing this work is at https://metadatagamechangers.com/blog/2024/4/12/funder-acronyms-are-still-not-enough</div> <div> </div> </div>
From social categorization to implicit citizenship theories: Advancing the socio-cognitive foundations of state–citizen interactions
<p>Data for the following article: Vogel, R., Vogel, D., Liegat, M. C., & Hensel, D. (2024). From social categorization to implicit citizenship theories: Advancing the socio‐cognitive foundations of state–citizen interactions. Public Administration Review, Article puar.13844. Advance online publication. https://doi.org/10.1111/puar.13844</p>
Data Potential Bias in Peer Review of Grant Applications at the Swiss National Science Foundation
<p>Potential biases in the peer review of grant applications at the Swiss National Science Foundation.</p> <p> </p>
Official GO FAIR Foundation icons for the Three-Point FAIRification Framework
<p>The official icons for the Three-Point FAIRification Framework (3PFF): Metadata for Machines Workshops, FAIR Implementation Profiles and FAIR Orchestration, created by the GO FAIR Foundation.</p>
A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys
<p>A multi-type geobody dataset for training SAG model, including channel, paloekarst, salt body, and so on.</p> <p>A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys (<a href="https://arxiv.org/abs/2409.04962">[2409.04962] A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys (arxiv.org)</a>)</p> <p> </p>
Macroevolutionary foundations of a recently-evolved innate immune defense
<p>There are two datasets along with accompanying statistical code used to generate the results of our article, the first is a database of published articles that from the basis of the literature review and then the other file contains data from an experimental study of phylogenetic conservation of peritoneal fibrosis in 17 species of ray-finned fish. Detailed description of the methodology can be found in the article (https://doi.org/10.1101/2020.07.08.191601).</p>
Genetic diversity of a marine foundation species, Laminaria hyperborea (Phaeophyceae Laminariales), along the coast of Ireland
<p><span><span><span><span><span><span><span><span><span><span><span>Worldwide, kelp populations are stressed by warming, increased storms and other man-driven disturbances<i>. </i>Marine population distributions are projected to retreat poleward with climate change if they cannot adapt to changing conditions, which would potentially lead to a regime shift in subtidal habitats. In Northern Europe, <i>Laminaria hyperborea</i>is a subtidal ecosystem engineer whose distribution has shifted over millennia, leaving predicted areas of high genetic diversity from the last glacial maximum (LGM) near its southern distribution limit in the Iberian Peninsula. In Ireland, <i>L. hyperborea </i>structures communities by supporting diverse faunal assemblages and producing large quantities of organic carbon throughout the year. We investigated the genetic diversity of eight populations ranging from the southern coast to the northwest of Ireland using nine microsatellite loci. Diversity was found to be highest in Lough Hyne, a Special Area of Conservation (SAC), near the predicted climate refugium. We found evidence of isolation by distance, with high connectivity between populations that were geographically close, likely driven by short range dispersal of <i>L. hyperborea</i>propagules. Genetic diversity (measured as expected heterozygosity and allelic richness) was highest at Lough Hyne, and decreased northwards, as predicted from past range shifts. Expected heterozygosity was highest at Lough Hyne (0.706) and decreased northward, with the lowest value at Bridges of Ross (0.283). Based on these patterns, further fine-scale investigation into population diversity, dispersal and potential resilience in Irish kelp forests are necessary as warming and non-native species are observed more and more frequently.</span></span></span></span></span></span></span></span></span></span></span></p>
Elements of Karl Popper's Philosophy as the Foundation of a Sociology of the Internet
<p>Talk at the <a href="https://www.digital-philosophy.org/" target="_blank" rel="noopener">Philosophy [in:of:for:and] Digital Knowledge Infrastructures</a> online workshop 2023 (28/09/2023).</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.