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21,983 results for “UNITE”
Operating diagram of DR1/DR2 double riffle; it consists of two independent sections (DR1 and DR2), each containing 630 litres of water and measuring 2.5 x 0.6 m. Each section contains a filtration system separate from the fish, a cooling unit and an ultraviolet sterilizer. An 80 W UQL lamp completes the lighting of the module lit during the day. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of DR1/DR2 double riffle; it consists of two independent sections (DR1 and DR2), each containing 630 litres of water and measuring 2.5 x 0.6 m. Each section contains a filtration system separate from the fish, a cooling unit and an ultraviolet sterilizer. An 80 W UQL lamp completes the lighting of the module lit during the day.
An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Figure 1. Experimental units for feeding rates tests with terrestrial isopods and fecal pellets from different food sources. A in Coprophagy in detritivores: methodological design for feeding studies in terrestrial isopods (Crustacea, Isopoda, Oniscidea)
Figure 1. Experimental units for feeding rates tests with terrestrial isopods and fecal pellets from different food sources. A) Treatment access; coprophagy is allowed. B) Treatment removal; coprophagy and bacterial activity on feces are avoided. C) Treatment net; coprophagy is avoided and bacterial activity on feces allowed. D) Fecal pellet from carrot (left) and decomposing leaf (right) consumption.
UK01 - Post Graduate Centre - Edinburgh (United Kingdom)
<p>Data files for building: UK01 - Post Graduate Centre - Edinburgh (United Kingdom)</p> <p>Languages: English</p> <p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p> <p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p> <p>The files are organised in the following folders (note that not all files are always provided):</p> <ol> <li>Main data and Results, with: <ol> <li>Neutral data inventory.</li> <li>Neutral results report.</li> <li>Original EPC certificate.</li> </ol> </li> <li>Energy Consumption Data, with: <ol> <li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li> </ol> </li> <li>Drawings <ol> <li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li> </ol> </li> <li>Other Data <ol> <li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li> </ol> </li> <li>Dynamic Model <ol> <li>Data to run a dynamic model of the building, if available.</li> </ol> </li> </ol> <p>The files have been redacted to exclude confidential information. </p>
UK04 - Educational - Southampton (United Kingdom)
<p>Data files for building: UK04 - Educational - Southampton (United Kingdom)</p> <p>Languages: English</p> <p>These files are part of the public benchmark repository created as a part of the crossCert EU project. </p> <p>This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures.</p> <p>The files are organised in the following folders (note that not all files are always provided):</p> <ol> <li>Main data and Results, with: <ol> <li>Neutral data inventory.</li> <li>Neutral results report.</li> <li>Original EPC certificate.</li> </ol> </li> <li>Energy Consumption Data, with: <ol> <li>Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results.</li> </ol> </li> <li>Drawings <ol> <li>Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models.</li> </ol> </li> <li>Other Data <ol> <li>Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building.</li> </ol> </li> <li>Dynamic Model <ol> <li>Data to run a dynamic model of the building, if available.</li> </ol> </li> </ol> <p>The files have been redacted to exclude confidential information. </p>
The conservation burden of Intact Forest Landscapes (IFLs): A global database of management units and IFLs
<p><strong>Introduction</strong></p> <p>This dataset includes a global overview of publicly available forest Management Units (MUs), Intact Forest Landscapes (IFLs) and their overlap. This includes both the boreal forests of Canada and Russia, and the tropical forests in the Amazon, the Congo basin, South-East Asia. The dataset was developed for the paper "Feasibility and effectiveness of global Intact Forest Landscape protection through forest certification: The conservation burden of Intact Forest Landscapes" by Zwerts et al. (2024). A comprehensive list of MUs with % and absolute overlap with IFLs is presented in Table S1 of Zwerts et al. (2024).</p> <p><strong>Data collection</strong></p> <p>We collected and collated all publicly available MU and IFL data of Central Africa, Southeast Asia, the Amazon, and of the boreal forests in Canada and Russia. As such, we included MU data from Cameroon, Canada, the Central African Republic, the Democratic Republic of Congo, Equatorial Guinea, Gabon, Indonesia, Malaysia, the Republic of Congo and Russia. Together, these forests comprise the majority of all IFLs (Potapov et al., 2017). We utilized the 2020 intact forest landscape (IFL) dataset generated by Potapov et al. (2017). Both FSC-certified and non-FSC MUs were considered and FSC-certification status data was collected using the FSC public dashboard (FSC, 2023). All data was collected in March 2023. Our dataset is not exhaustive. To our knowledge, not all MU data is publicly available. For Southeast Asia no public MU data is available for Papua New Guinea and Peninsular Malaysia. For the Amazon, insufficient public MU data was available to create an accurate representation of the situation. This area was excluded from the main analysis in Zwerts et al., 2024. We included a distinction between FSC-certified and non-FSC MUs in Russia, even though the FSC has withdrawn all certificates in Russia in April 2023 following the invasion of Ukraine. We chose to retain the distinction between FSC and non-FSC MUs for the Russian data because of the uncertainty of the current situation and the significant influence of FSC-certification in the Russian management of IFLs.</p> <p><strong>Overlap analysis</strong></p> <p>All area was transformed to geodesic distance. Furthermore, several MU names were altered because of duplicate names. The number of hectares of MUs that overlap with IFLs was calculated in ArcGIS Pro 3.0.0, using the WGS_1984_Web_Mercator_Auxiliary_Sphere coordinate system. Using the intersect and multipart to singlepart tools every overlap fragment was isolated. For the results in Zwerts et al. (2024) the total overlap and the percentage of overlap was calculated in R. </p> <p><strong>Abstract of the related article</strong></p> <p>Intact Forest Landscapes (IFLs) are defined as forested areas of at least 500 km2 that show no signs of remotely sensed human activity. They are considered to be of high conservation value due to their role in maintaining biodiversity and mitigating climate change. In 2014, the members of the Forest Stewardship Council (FSC), one of the major global certification schemes for responsible forest management, took a conservation stand by restricting logging in FSC-certified IFLs. However, this move raised concerns about the economic viability of FSC-certified logging in these areas. To address these challenges, in 2022, FSC proposed an integrated landscape approach, considering local conditions and stakeholders' needs to balance IFL protection, economic sustainability, and community interests. Here, we leverage publicly available management unit (MU) data, to provide a global quantitative overview of IFLs designated for timber production. We use the concept of 'conservation burden' for the extent that MUs overlap with IFLs, representing the impact that IFL protection has on forest management operations if logging is disallowed. Our data indicates that currently FSC-certified MUs affect 0.6% of global IFLs. Too restrictive policies for logging in IFLs may discourage FSC-certification in global IFLs. Considering the environmental and social benefits of FSC certification, it warrants careful examination whether the benefits of protecting a limited subset of FSC-certified IFLs outweighs the cost of potentially reduced growth of the total FSC-certified area. Our data can provide a basis to facilitate stakeholder engagement for landscape-level IFL management.</p>
BIO4AFRICA_Small-scale HTC batch unit_Dataset1_261124_v1
<p><span>Hydrochar Characterization Database: This database reflects the characterization of hydrochar produced at IHE Delft for Water Education within the framework of the Bio4Africa project. It includes the experimental operational conditions, the types of biomass used, the mass yield of the product, and the properties of the solid and liquid outputs of the process.</span></p> <p><span><span>Machine learning database: This is an analysis ready database to be used by user who are interested in predicting the properties of hydrochar using machine learning approaches. The database combines the experimental results from IHE and the experimental results from the literature. The data are standardized in the same manner, allowing the users to directly use it without any required edits.</span></span></p>
National Checklists: United Arab Emirates Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - United Kingdom in respect of Northern Ireland
<p>This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of United Kingdom in respect of Northern Ireland. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health.</p> <p>For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken.</p> <p>Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC.</p> <p>The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles.</p> <p>Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance.</p> <p>Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product.</p> <p>Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food.</p> <p>Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation.</p> <p>Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation.</p> <p>Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances.</p> <p>For 2021 and 2022 the only United Kingdom data that were reported to EFSA were from Northern Ireland. In accordance with the Agreement on the withdrawal of the United Kingdom of Great Britain and Northern Irelandfrom the European Union and the European Atomic Energy Community, and in particular Article 5(4) of the WindsorFramework (see Joint Declaration No 1/2023 of the Union and the United Kingdom in the Joint Committee establishedby the Agreement on the withdrawal of the United Kingdom of Great Britain and Northern Ireland from the EuropeanUnion and the European Atomic Energy Community of 24 March 2023, OJ L 102, 17.4.2023, p.87) in conjunction withsection 24 of Annex 2 to that Framework, for the purposes of this Regulation, references to Member States include theUnited Kingdom in respect of Northern Ireland.</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>VMPR_2023 – Department of Agriculture, Environment and Rural Affairs</p> <p>VMPR_2022 – Department of Agriculture, Environment and Rural Affairs</p> <p>VMPR_2021 – Department of Agriculture, Environment and Rural Affairs</p>
River Restoration Units
<p>Database on River Restoration Units (R2U) for European sea outlet basins that contain at least a segment of Strahler value 3 or more. The database covers all sea outlet basins occurring in current and former European Union Member States' territory. Contains three spatial layers; the European basins (Basins_EU.shp), the R2U drainage areas (R2U_ID_drainage_areas.shp) and the R2U watercourses (R2U_Watercourses.shp); and four tables: Member States coding (MS_coding), R2U characterization (R2U_Features), Connection between River Restoration Units ID and Member States ID (R2U_to_MS_ID), and the R2U typology (Unit_typology).</p> <p><strong>Funding:</strong><br>The development of this dataset was funded by MERLIN, a project funded under the European Commission’s Horizon 2020 programme under grant agreement No 101036337. Forest Research Centre (CEF) is a research unit funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), Portugal (UID/00239: Centro de Estudos Florestais). The Associate Laboratory Laboratory for Sustainable Land Use and Ecosystem Services – TERRA is funded by the FCT (LA/P/0092/ 2020). GD has been financed by FCT within the project project Dammed Fish (PTDC/CTA-AMB/4086/2021 – DOI: 10.54499/PTDC/CTA-AMB/4086/2021). AP is currently supported by a postdoctoral research scholarship under the project: mainstreaming ecological restoration of freshwater-related ecosystems in a landscape context. Innovation, upscaling, and transformation (101036337—MERLIN—H2020-LC-GD-2020). TL was supported by a PhD grant from the FLUVIO–River Restoration and Management program funded by Fundação para a Ciência e a Tecnologia I. P. (FCT), Portugal (UI/BD/15052/2021). PB is financed by national funds via FCT (LA/P/0092/2020). AF was funded by the Christian Doppler Research Association (CD Laboratory MERI) as well as FB who was also supported by the AQUAINFRA project (grant agreement No 101094434).</p>
How to render species comparable taxonomic units through deep time: A case study on intraspecific osteological variability in extant and extinct lacertid lizards
<p>Generally, the species is considered to be the only naturally occurring taxon. However, species recognized and defined using different species delimitation criteria cannot readily be compared, impacting studies of biodiversity through Deep Time. This comparability issue is particularly marked when comparing extant with extinct species because the only available data for species delimitation in fossils are derived from their preserved morphology, which is generally restricted to osteology in vertebrates. Here, we quantify intraspecific, intrageneric, and intergeneric osteological variability in extant species of lacertid lizards using pairwise dissimilarity scores based on a data set of 253 discrete osteological characters for 99 specimens referred to 24 species. Variability is always significantly lower intraspecifically than between individuals belonging to distinct species of a single genus, which is in turn significantly lower than intergeneric variability. Average values of intraspecific variability and associated standard deviations are consistent (with few exceptions), with an overall average within a species of 0.208 changes per character scored. Application of the same methods to six extinct lacertid species (represented by 40 fossil specimens) revealed that intraspecific osteological variability is inconsistent, which can at least in part be attributed to different researchers having unequal expectations of the skeletal dissimilarity within species units. Such a divergent interpretation of intraspecific and interspecific variability among extant and extinct species reinforces the incomparability of the species unit. Lacertidae is an example where extant species recognized and defined based on a number of delimitation criteria show comparable and consistent intraspecific osteological variability. Here, as well as in equivalent cases, application of those skeletal dissimilarity values to paleontological species delimitation potentially provides a way to ameliorate inconsistencies created by the use of morphology to define species.</p>
DATA SET USED IN THE PHYLOGENETIC ANALYSIS ?, condition not preserved. Coding for Paraortygoides based on BMNH PAL A 6217 (holotype of P. radagasti) and SMRME 1303 (holotype of P. messelensis) in The Fossil Galliform Bird Paraortygoides from the Lower Eocene of the United Kingdom
DATA SET USED IN THE PHYLOGENETIC ANALYSIS ?, condition not preserved. Coding for Paraortygoides based on BMNH PAL A 6217 (holotype of P. radagasti) and SMRME 1303 (holotype of P. messelensis)
Sci-Fi movies by inflation-adjusted gross revenue in the United States.
<p>Sci-Fi movies by inflation-adjusted gross revenue in the United States.</p> <p>The dataset is a .csv file with < ; >used as separator.</p> <p>The dataset has the next columns:</p> <ul> <li>Title: movie title.</li> <li>Release_Year: year of the movie's release.</li> <li>Watchtime: movie duration in minutes.</li> <li>Genre: genre.</li> <li>Movie_Rating: IMDB movie rating.</li> <li>Metascore: metacritic movie rating.</li> <li>Votes: number of ratings.</li> <li>Gross_collection: incomes generated by the movie.</li> <li>Sinopsis.</li> <li>Director.</li> <li>Star: main actors.</li> <li>Gross_equivalent: income generated by movies with dollar devaluation correction.</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
TSE results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - the United Kingdom
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: TSE_2020_UK: Animal and Plant Health Agency (APHA)</p>
FIG. 1 in New occurrences of crabs (Decapoda, Brachyura, Eubrachyura) in the Pliocene of Florida (United States)
FIG. 1. — Location map of the two fossiliferous localities of the Pliocene Tamiami Formation in Sarasota County, Florida (United States). Locality A: SMR Aggregates quarry (27°20'05"N, 82°19'58"W). Locality B: Old APAC Shell quarry (27°14'18"N, 82°27'11"W). Maps modified after different sources and ViaMichelin.
FIG. 2 in New occurrences of crabs (Decapoda, Brachyura, Eubrachyura) in the Pliocene of Florida (United States)
FIG. 2. — Persephona cf. P. subovata (Rathbun, 1893), USNM PAL 720104 from the Pinecrest Member of Tamiami Formation (middle Piacenzian, Pliocene) of locality A in Sarasota County, Florida (United States): A, dorsal view; B, frontal view; C, left lateral view; D, posterior view. Scale bar: 10 mm. Photographs by À. Ossó.
FIG. 3 in New occurrences of crabs (Decapoda, Brachyura, Eubrachyura) in the Pliocene of Florida (United States)
FIG. 3. — Paractaea cf. P. nodosa (Stimpson, 1860), USNM PAL 720105 from the Pinecrest Member of Tamiami Formation (middle Piacenzian, Pliocene) of locality B in Sarasota County, Florida (United States): A, dorsal view; B, frontal view; C, posterior view. Scale bar: 10 mm. Photographs by À. Ossó.
Figures 1–3 in A new species of Buprestis (s. str.) Linnaeus, 1758 (Coleoptera: Buprestidae) from the southwestern United States
Figures 1–3. Buprestis (s. str.) pinyoni male holotype. 1) After being relaxed. 2) Prior to being relaxed. 3) Aedeagus, dorsal view.
Figure 2 in The eriophyid mite Aculops ailanthi Lin, Jin, & Kuang, 1997 (Acariformes: Prostigmata: Eriophyidae) from tree-of-heaven in the United States - new state records and morphological observations
Figure 2 Stereomicrographs (2a–b) and SEMs (2c–f) ofAculops ailanthi: General aspect of colonies on leaves and morphological traits:** 2a–b – prodorsal shield; 3c – mite infestation of a leaf; 2d – mite feeding on a trichome; 2e – nymph next to an egg, note the lack of reticulate cells on the prodorsal shield; 2f – dorsum.
Figure 1 in The eriophyid mite Aculops ailanthi Lin, Jin, & Kuang, 1997 (Acariformes: Prostigmata: Eriophyidae) from tree-of-heaven in the United States - new state records and morphological observations
Figure 1 Treeofheaven damaged byAculops ailanthi Lin, Jin, & Kuang feeding: 1a – healthy leaf without mite infestation; 1b – leaf showing severe stippling; 1c – leaf showing yellowing, stippling, and necrosis; 1d – leaf necrosis; 1e – mites on leaf surface after an application of a 2% solution of MPede insecticidal soap.
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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.