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3,118 results for “resources”
Ant Resource Detection Distance at the Caxiuana National Forest in Brazil 2017-2018
Environmental change scenarios of low precipitation forecast species loss in tropical regions. These losses can affect generalist species that provide important ecosystem services, such as controlling the rate at which nutrients become available for uptake by other organisms in tropical forests. Here, we use a long-term rainwater exclusion experiment in primary Amazonian rainforest to test whether induced water stress affects the detection distance of food resources (baits) in a generalist ant guild (number of colonies, richness, and composition) that remove resources on the ground. We found that (i) overall the distance of resource removal by generalist ants did not change with drought; (ii) however, comparing ant species that occurred in drought-induced and control environments, workers walked shorter distances in the drought habitat; (iii) the number of resources detected by ant colonies in the drought-induced habitat decreased by 50%. Although generalist ants are considered resilient to habitat disturbance, the effect of reduced rainfall can negatively affect the services mediated by them. The rate of removal and consumption of resources in tropical forests may be related to abundance of generalist ants; losses in both nest density and walking distances may cause cascading effects on ecosystem processes and the services they mediate.
Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/357/2. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/356/3. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/345/4, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/357/2. The abstract below was extracted from the Level 0 data package and is included for context: This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
FAO and SAGARPA. (2012). Baseline of the Natural Resources Sustainability Program. Sustainable Land Use Subindex - Calculation Methodology. Mexico City (53 pp.) (FAO y SAGARPA. (2012). Línea de Base del Programa de Sustentabilidad de los Recursos Naturales. Subíndice de Uso Sustentable del Suelo - Metodología de Cálculo (53 pág.). Ciudad de México)
The lack of soil data is a complication that most soil scientists will encounter throughout their career; this critical aspect is exacerbated due to the excessive cost of soil surveying. Consequently, it is essential to develop strategies that guarantee the permanent accessibility of past soil sampling efforts. The main objective of this contribution is to release an entire dataset of soil samples surveyed by the Secretary of Agriculture, Livestock, Rural Development, Fisheries, and Food (SAGARPA) in collaboration with the Food and Agriculture Organization of the United Nations (FAO) in the year 2012, the dataset consists of more that 4000 compound samples surveyed on managed cropland. SAGARPAS's main objective was to generate a new index to assess and monitor the current and future state of soil when sustainable soil practices take place. A complete set of physicochemical properties were determined via laboratory analysis for all record in the dataset, namely: pH, EC, OM, BD, P, Sand, Silt, Clay, Texture, N, K, Ca, Mg, Na, CEC, Sodium Adsorption Ratio (SAR), Exchangeable Sodium Percentage (ESP), including the calculation of the Sustainable Soil Land Use Subindex (SSLUS). We presented a reviewed dataset with the potential to contribute to a large variety of studies, ranging from: agricultural pinpointing of the best conditions for crop production to digital soil mapping and modeling and agronomical studies. We also provide the original report on the developement of the dataset, indicating the names of creators and colaborators, as well as the methods of analysis and interpretation of the results. The new information is appealing for a wide diversity of users interested in soil traits across agricultural systems of Mexico.
Altering pH changes competition dynamics between two crayfish species for both food and shelter resources
As climate change continues, alterations in abiotic variables within habitats will also change. One of the key variables for aquatic systems is pH. We were interested in how lowered pH altered the behavioral characteristics of two keystone species: Faxonius rusticus, a non-native crayfish in the upper midwest and Faxonius virilis, the native crayfish. We dosed size-matched individuals in separate tanks for four days and than placed these individuals in a flow through mesocosm to allow them to compete over food and shelter resources. Behavior was recorded from midnight to 4 am. Pairs of animals were exposed to pH levels varying from ambient (8.4) to 6.4. The experiment took place at the University of Michigan's Biological Station in northern Michigan in the United States.
Gender and ethnic diversity of members of US university natural resource program external advisory board members, 2017-2022
This dataset contains deidentified demographics information for the members of external advisory boards that serve university natural resource programs. Data collected in 2017 and 2022 represents a sample of land-grant, National Association of University Forestry Program-affiliated, TIMES-ranked universities and colleges. Each row represents a member of an advisory board. Data collection was completed in two years: 2017 and 2022. Data was collected from department webpages and lists of advisory board members provided by department personnel. As needed, information was augmented through internet searches for public LinkedIn pages, organizational pages, local news stories, etc. Data include a unique respondent ID, a code for the university they are from, their employer affiliation (e.g., NGO, federal government, NR business, etc.) and their gender and ethnicity measured as binary variables.
Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Luke) and Geological Survey of Finland (GTK)
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland </strong></p><p><strong>Creators: </strong>Larmola T, Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M </p><p>The dataset consists of peat properties in a subset of 16 undrained peatland sites (32 peat samples) in Geological Survey of Finland (GTK) national peatland inventory. These sites were sampled between 2002 and 2017 and the subset selected from GTK peat sample archives. These 16 sites represented two pine-<i>Sphagnum-</i> dominated site types (IR, KR) and two treeless sedge fen types (VSN, RhSN) all in 4 replicates and sampled in 2 depths 20-40, 40-60cm). </p><p><strong>Peat analyses</strong> The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S).</p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola T, Anttila J, Alm J Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description </strong></p><p>ID - Site identifier</p><p>site - undrained peatland (UDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 20: 0-20 cm, 40: 20-40cm, 60: 40-60cm.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin - UDP site type. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p><strong>References</strong></p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p>
Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland, Natural Resources Institute Finland
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</strong></p><p><strong>Creators: Larmola T, Anttila J, Alm J </strong></p><p>The dataset consists of peat properties in a subsample of 30 drained peatland forests in Finland selected from the permanent sample plots of the 8th National Forest Inventory (systematic sample of plots on drained peatland forests, e.g., Hotanen et al. 2006). The subsample included equally different site types of forestry-drained peatlands of those parts of Finland where drainage for forestry is economically viable (Latitude 60-66 ºN, annual temperature sum > 750 dd). </p><p><strong>The site selection criteria</strong> were average peat layer thickness of over 20 cm, no clear-cut areas, site drained before 1995 and ditching had detectably altered hydrology or vegetation. <strong>Peat analyses</strong> Finnish Forest Research Institute (now Natural Resources Institute Finland) sampled peat cores with a box corer in 2002, samples were analysed for bulk density, archived and remaining samples at depths 20-30, 30-40 cm (total of 58) were analysed in 2021. The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. </p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola, T. Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Version 1) [Dataset]. Zenodo. doi.org/<strong>10.5281/zenodo.10068486</strong></p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description</strong></p><p>ID - Site identifier</p><p>site - Forestry-drained peatland (FDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 30: 20-30 cm, 40: 30-40cm, avg: average of both depths.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin – Origin of the FDP site type at undrained state. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p>n - Number of samples. 2 for averages from both depths, 1 for all other rows.</p><p> </p><p><strong>References</strong></p><p>Hotanen JP, Maltamo M, Reinikainen A (2006) Canopy stratification in peatland forests in Finland. Silva Fennica 40:53–82.</p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p><p> </p>
IPMWORKS Resource Toolbox - database extraction March 2024
<p><span>The IPMWORKS IPM Resource Toolbox (Toolbox) has been developed as an interactive, online repository of integrated pest management (IPM) resources. Populated with high priority resources for farmers and their advisors during the project, its structure enables additional resources added over time. The repository is a public interactive website, available to anyone looking to access, understand, and implement IPM. Built on an open-source content management system, the toolbox is designed to require minimal post-production site maintenance and support, while being easily expanded to integrate resources from future initiatives.<br>At the core of the Toolbox lies MongoDB, a powerful NoSQL database management system. The schema-less nature of MongoDB allows for flexible data modeling, crucial for accommodating the diverse array of materials within the IPMWORKS ecosystem. Additionally, the integration of GridFS, a feature of MongoDB, facilitates the storage and retrieval of large files like images, PDFs, and documents. This architectural choice ensures optimal performance and efficiency in handling a wide range of materials. <br>We here make available all content uploaded to the IPMWORKS Resource Toolbox up to 18 March 2024. Materials are available in two formats, first as MongoDB files which require users to open them as a Mongo database file, and second as JSON files. Note that the JSON format does not include access to any pdfs attached to Toolbox content, only the associated metadata. </span></p> <p> </p>
FuTRES (Functional Trait Resource for Environmental Studies) data store archival copy - 5/21/2022
<p> </p> <p>The Functional Trait Resource for Environmental Studies (FuTRES) project is a collaborative project among four universities (University of Oregon, University of Arizona, University of Florida, and Howard University). The key deliverables of FuTRES are a workflow for assembling functional trait data measured at the specimen level, a database to serve that data, and scientific publications demonstrating the utility of the assembled data. This dataset represents the FuTRES datastore as of 5/21/2022, providing an archive that is timestamped and providing all data that is not currently embargoed by providers. The column headers for FuTRES data are: basisOfRecord,catalogNumber,class,collectionCode,country,decimalLatitude,decimalLongitude,diagnosticID,eventID,family,genus,individualID,institutionCode,lifeStage,locality,mapped_project,materialSampleID,maximumChronometricAge,maximumChronometricAgeReferenceSystem,maximumElevationInMeters,measurementMethod,measurementSide,measurementType,measurementUnit,measurementValue,minimumChronometricAge,minimumChronometricAgeReferenceSystem,minimumElevationInMeters,observationID,occurrenceID,occurrenceRemarks,order,reproductiveCondition,samplingProtocol,scientificName,sex,specificEpithet,stateProvince,verbatimElevation,verbatimEventDate,verbatimLatitude,verbatimLocality,verbatimLongitude,verbatimMeasurementUnit,yearCollected,projectID,inferred_traits. The traits available and number of records for each trait: </p> <ul> <li><a href="https://futres-data-interface.netlify.app/">length (1,790,883)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tail length (520,281)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length (456,165)</a></li> <li><a href="https://futres-data-interface.netlify.app/">pes length (413,668)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ear length to notch (397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">external ear length (397,073)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body mass (373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">weight (373,949)</a></li> <li><a href="https://futres-data-interface.netlify.app/">width (7,429)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus width (1,705)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 proximal articular breadth (783)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface width (782)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 breadth (716)</a></li> <li><a href="https://futres-data-interface.netlify.app/">metacarpal bone of digit 3 depth (706)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus length (649)</a></li> <li><a href="https://futres-data-interface.netlify.app/">long bone length (637)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface width (605)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus trochlea breadth (596)</a></li> <li><a href="https://futres-data-interface.netlify.app/">epiphysis width (581)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus breadth (560)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus medial depth (549)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tooth row length (498)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower tooth row length (425)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal width (413)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 1 occlusal surface length (402)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 4 occlusal surface width (361)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface length (343)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur width (301)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length (297)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus width (293)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface width (261)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 2 occlusal surface length (260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis width (260)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface length (242)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia length (228)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal breadth (208)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia distal depth (201)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 2 occlusal surface width (197)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface length (187)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 4 occlusal surface width (185)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 1 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">premolar tooth 3 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary canine tooth to premolar tooth 3 length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface length (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 3 occlusal surface width (184)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 1 occlusal surface length (180)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface length (177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 1 occlusal surface width (177)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface length (176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 4 occlusal surface width (176)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal width (162)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 3 occlusal surface length (159)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface length (155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 2 occlusal surface width (155)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis breadth (145)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface length (120)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 2 occlusal surface width (119)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia diaphysis depth (111)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface width (106)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 2 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper premolar tooth 1 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface length (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary premolar tooth 1 occlusal surface width (105)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal breadth (85)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface length (81)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface length (79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary premolar tooth 1 occlusal surface width (79)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface length (78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper secondary molar tooth 2 occlusal surface width (78)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlea breadth (76)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus medial trochlear height (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear height at sagittal crest (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus trochlear sulcus height (74)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia proximal depth (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1-2 length (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper tooth row length (73)</a></li> <li><a href="https://futres-data-interface.netlify.app/">anterior tibial tuberosity length (70)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus distal depth (69)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length (67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna width (67)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia medial length (63)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis breadth (57)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus diaphysis depth (54)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower secondary molar tooth 3 occlusal surface length (51)</a></li> <li><a href="https://futres-data-interface.netlify.app/">trochlea tali length (49)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis breadth (45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">forelimb zeugopod bone length (45)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal breadth (44)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna length (42)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal breadth (40)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to caput (38)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus length (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur caput depth (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus length from trochlea to ventral tubercle (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">humerus proximal breadth (37)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur diaphysis depth (36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur distal depth (36)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur trochlea breadth (32)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur proximal depth (31)</a></li> <li><a href="https://futres-data-interface.netlify.app/">molar tooth 3 occlusal surface width (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">talus lateral length (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface length (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 3 occlusal surface width (30)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to lateral condyle (28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from greater trochanter to medial condyle (28)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body length with tail (25)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 1 occlusal surface length (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">lower molar tooth 2 occlusal surface length (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna depth across the process anaconaeus (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">ulna proximal articular breadth (23)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 1 occlusal surface length (22)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon depth (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">olecranon length (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">upper molar tooth 2 occlusal surface length (21)</a></li> <li><a href="https://futres-data-interface.netlify.app/">breadth of calcaneal body (18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">calcaneus width (18)</a></li> <li><a href="https://futres-data-interface.netlify.app/">tibia lateral length (14)</a></li> <li><a href="https://futres-data-interface.netlify.app/">femur length from caput to medial condyle (5)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius distal width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius length (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal articular width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius proximal width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">radius width (3)</a></li> <li><a href="https://futres-data-interface.netlify.app/">body height (1)</a></li> <li><a href="https://futres-data-interface.netlify.app/">height (1)</a></li> </ul>
Resources to compute TF-IDF weightings on press articles and tweets
<p>These two datasets of features are used in order to compute TF-IDF weightings of documents. It is meant to be used with the <a href="https://pypi.org/project/compute-tf-idf-vectors/">compute-tf-idf-vectors</a> program written in Python and available on Pypi.org.</p> <p>- features_tweets.csv contains features (tokens, lemmas and entities) extracted from Tweets published by press agencies in french, german, spanish and english.</p> <p>- features_news.csv contains features (tokens, lemmas and entities) extracted from articles published by Deutsche Welle in the same languages.</p>
Resources from: Disparate patterns of genetic divergence in three widespread corals across a pan-pacific environmental gradient highlights species-specific adaptation trajectories
<p>The following files are contained in this repository:</p> <p><br> README.Hume_et_al_2022.zenodov4.txt - This document.</p> <p>scripts.Hume_et_al_2022.zenodov4.pdf - Contains the scripts, or locations of the scripts, used to conduct the data analyses detailed in the associated manuscript.</p> <p>acknowledgements_local_authorities.Hume_et_al_2022.zenodov1.pdf - Acknowledgements of local authorities for the collection of samples used in the associated study.</p> <p>TaraPacific_SST_timeseries_mean_productsV2mai2021.Hume_et_al_2022.zenodov1.csv - The historical temperature data set used for the RDA, Mantel tests and gradient Forest analysis.</p> <p>Pocillopora_meandrina_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip - The Pocillopora SNPs referred to as 'raw' in the Methods of the associated manuscript. Compressed using genozip (https://genozip.readthedocs.io/index.html).</p> <p>Pocillopora_meandrina_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip.md5 - md5 of the the Pocillopora raw SNPs.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'linked' in the Methods of the associated manuscript.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora linked SNPs.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'unlinked' in the Methods of the associated manuscript.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora unlinked SNPs.</p> <p>Porites_lobata_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip - The Pocillopora SNPs referred to as 'raw' in the Methods of the associated manuscript. Compressed using genozip (https://genozip.readthedocs.io/index.html).</p> <p>Porites_lobata_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip.md5 - md5 of the the Pocillopora raw SNPs.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'linked' in the Methods of the associated manuscript.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora linked SNPs.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'unlinked' in the Methods of the associated manuscript.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora unlinked SNPs.</p> <p>PANAMA2021.raw.Hume_et_al_2022.zenodov2.vcf.gz - The Millepora SNPs referred to as 'raw' in the Methods of the associated manuscript.</p> <p>PANAMA2021.raw.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Millepora raw SNPs.</p> <p>Millepora_REF_orthologue_genes.Hume_et_al_2022.zenodov2.csv - The Millepora gene list referred to as 'target genes' in the Methods of the associated manuscript.</p> <p>Mil_transcriptom.Hume_et_al_2022.zenodov2.fa.gz - The Millepora de novo assembled transcriptome.</p> <p>Mil_transcriptom.Hume_et_al_2022.zenodov2.fa.gz.md5 - md5 of the Millepora de novo assembled transcriptome.</p> <p> </p> <p>mtORF Phylogeny</p> <p>TP-Johnston_mtORF-Pocillo.fa = all sequences</p> <p>TP-Johnston_mtORF-Pocillo.mafft.fa = mafft alignment</p> <p>TP-Johnston_mtORF-Pocillo.mafft.ML.nwk = ML tree newick</p> <p> </p> <p>Hellberg genotype network Porites</p> <p>TP-Hellberg_MM32-Porites.nex = all aligned sequences for this locus with indels encoded</p> <p>TP-Hellberg_MM100-Porites.nex = all aligned sequences for this locus with indels encoded</p> <p>TP-Hellberg_ATPaseB.nex = all aligned sequences for this locus with indels encoded,</p> <p>TP-Hellberg_POFAD.nex = POFAD multilocus genotypic distance,</p> <p>TP-Hellberg_Splitstree.nex= Multilocus genotype network in nexus format</p> <p><br> Gradient Forest Analysis</p> <p>Poc_abund.csv - Pocillopora SSH Occurrences per Site er Island</p> <p>Por_abund.csv - Porites SSH Occurrences per Site er Island</p> <p>mean_depth_por.csv - per site per island mean depth among Porites colonies</p> <p>mean_depth_poc.csv - per site per island mean depth among Pocillopora colonies</p>
Minnesota Department of Natural Resources Yellow Perch and Bluegill Diet Study, Lower Pool 4 Mississippi River, 2018-2019
To assess potential dietary overlap and predation between bluegill and yellow perch, we examined stomach content of both species in three backwater contiguous lakes of Lower Pool 4 in the Mississippi River from May 2018 through January 2019. In this area, bluegill have been common for decades, but yellow perch only became abundant following an ecological shift to a clear-water, macrophyte dominated state that occurred from 2007 - 2007. We used daytime electrofishing to collect fish during open-water sampling (spring, summer, and fall) and ice angling during ice-covered sampling (winter). The dataset documents the diet content of one hundred and eighty-nine yellow perch and sixty-one bluegill. Stomach contents were extracted via gastric lavage in the spring, summer, and fall and via stomach removal in the winter. All prey items were categorized to lowest identifiable taxonomic level and quantified via volumetric displacement.
Forage Resources in Warming and Removal Plots, Almont, CO, 2019
This is data collected to explore the impacts of warming and dominant species removal on the quantity and quality of plants for cattle foraging. The data were collected from the Colorado low elevation site (Almont) of the Warming and Removal in Mountains experiment which examines the direct and indirect impacts of climate change on plant and soil communities. Treatments include a control, warming (+1.5C), removal of the dominant species (Wyethia Amplexicalus), and both warming and dominant species removal. The dataset includes data that were collected in 2019 as well as historical data from the site. From 2019 we have in situ air temperature contained in and soil temperature data and an assessment of plant cover from every plot. We then have a compiled set of plant traits for each of the nine most common species including the leaf nitrogen, crude protein content, and forage quality class which are used for analysis on forage quality. The dataset also includes the annual plant cover data collected at peak season from 2013 to 2021 which was compared to daily temperature and precipitation data from the same date range collected by the National Oceanic and Atmospheric Administration. All reported figures and statistics published can be created from this data package.
Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012
This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Long-term fish size data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012
This dataset describes long-term (1944-2012) variations in individual fish total lengths from Wisconsin lakes. The dataset includes information on 1.9 million individual fish, representing 19 species. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Pollinator visitation and floral resource production in Black Sand plots, 2019.
Anthropogenic climate change is altering interactions among numerous species, including plants and pollinators. Plant-pollinator interactions, crucial for the persistence of most plant and many insect species, are threatened by climate change-driven phenological shifts. Phenological mismatches between plants and their pollinators may affect pollination services, and simulations indicated that these mismatches may reduce floral resources available to up to 50 percent of insect pollinator species. Although alpine plants rely heavily on vegetative reproduction, seedling recruitment and seed dispersal are likely to be important drivers of alpine community structure. Similarly, advanced flowering may expose plants to increased risk of frost damage and shifted soil moisture regimes; phenologically advanced plants will experience these environmental factors differently, which may alter their floral resource production. Some species of alpine plants on the Niwot Ridge have displayed advanced phenology under treatments of advanced snowmelt (Forrester, unpublished data). However, little is understood about how these differences in distribution and phenology affect floral resources, pollinator community composition, and plant fecundity. Here we strive to examine how changes in the timing of flowering and number of flowers produced by plants, driven by experimental changes to climatic conditions at individual sites impact pollinator communities. In summer 2019, we found that plots with advanced phenology experienced peaks in pollinator visitation rates and pollinator diversity earlier than plots with unmanipulated snowmelt. We expect this to be because of the advanced floral phenology of certain key species in these plots. We did not find evidence that plants with advanced phenology produce fewer floral resources.
Effects of Multiple Resource Additions on Community and Ecosystem Processes: NutNet NPP Quadrat Sampling at the Sevilleta National Wildlife Refuge, New Mexico
Two of the most pervasive human impacts on ecosystems are alteration of global nutrient budgets and changes in the abundance and identity of consumers. Fossil fuel combustion and agricultural fertilization have doubled and quintupled, respectively, global pools of nitrogen and phosphorus relative to pre-industrial levels. In spite of the global impacts of these human activities, there have been no globally coordinated experiments to quantify the general impacts on ecological systems. This experiment seeks to determine how nutrient availability controls plant biomass, diversity, and species composition in a desert grassland. This has important implications for understanding how future atmospheric deposition of nutrients (N, S, Ca, K) might affect community and ecosystem-level responses. This study is part of a larger coordinated research network that includes more than 40 grassland sites around the world. By using a standardized experimental setup that is consistent across all study sites, we are addressing the questions of whether diversity and productivity are co-limited by multiple nutrients and if so, whether these trends are predictable on a global scale.
Effects of Multiple Resource Additions on Community and Ecosystem Processes: NutNet Seasonal Biomass and Seasonal and Annual NPP Data at the Sevilleta National Wildlife Refuge, New Mexico
Two of the most pervasive human impacts on ecosystems are alteration of global nutrient budgets and changes in the abundance and identity of consumers. Fossil fuel combustion and agricultural fertilization have doubled and quintupled, respectively, global pools of nitrogen and phosphorus relative to pre-industrial levels. In spite of the global impacts of these human activities, there have been no globally coordinated experiments to quantify the general impacts on ecological systems. This experiment seeks to determine how nutrient availability controls plant biomass, diversity, and species composition in a desert grassland. This has important implications for understanding how future atmospheric deposition of nutrients (N, S, Ca, K) might affect community and ecosystem-level responses. This study is part of a larger coordinated research network that includes more than 40 grassland sites around the world. By using a standardized experimental setup that is consistent across all study sites, we are addressing the questions of whether diversity and productivity are co-limited by multiple nutrients and if so, whether these trends are predictable on a global scale. Above-ground net primary production is the change in plant biomass, represented by stems, flowers, fruit and and foliage, over time and incoporates growth as well as loss to death and decomposition. To measure this change the vegetation variables, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots within each site. Volumetric measurements are made using vegetation data from permanent plots (SEV231, "Effects of Multiple Resource Additions on Community and Ecosystem Processes: NutNet NPP Quadrat Sampling") and regressions correlating species biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
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.