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FIGURE 1 in Body size responses to land use in stream fish: the importance of different metrics and functional groups
FIGURE 1 | Description of the four body size metrics used to investigate body size patterns in overall stream fish communities and in distinct functional groups. A. Skewness describes the tendency of value distribution being biased towards the right (negative-skewed) or left (positive-skewed). B. Kurtosis describes if the distribution of values is more flatted (platykurtic) or biased towards the center (narrow). Mean values can be the same for distinct kurtosis. Coefficient of variation (CV) describes the variation in values standardized to the mean.
FIGURE 3 in Body size responses to land use in stream fish: the importance of different metrics and functional groups
FIGURE 3 | Clusters of fish species based on ecomorphological and trophic traits, resulting in 11 functional groups (FG). Full species names by FG are available in S2.
FIGURE 2 in Body size responses to land use in stream fish: the importance of different metrics and functional groups
FIGURE 2 | Location of the 40 stream sites where fish communities were sampled in South Brazilian grassland biome (Pampa).
Imputation of missing land carbon sequestration data in the AR6 Scenarios Database
<p>This repository is linked to the following research paper:</p> <ul> <li>Prütz, R., Fuss, S., and Rogelj, J.: Imputation of missing land carbon sequestration data in the AR6 Scenarios Database, Earth Syst. Sci. Data, 2025. <a href="https://doi.org/10.5194/essd-17-221-2025">https://doi.org/10.5194/essd-17-221-2025</a> </li> </ul> <p>This repository includes: </p> <ul> <li>An imputation dataset for missing land carbon sequestation data of the AR6 Scenarios Database for global scenarios and R10 scenario variants</li> <li>Code to test, compare and visualize the performance of regression models to predict missing land removal data</li> <li>Code to compare and visualize available AR6 land removal data and existing AR6 data reanalyses</li> </ul> <p>The following two datasets are required to replicate the analysis:</p> <ul> <li>Byers, E., Krey, V., Kriegler, E., Riahi, K., Schaeffer, R., Kikstra, J., Lamboll, R., Nicholls, Z., Sandstad, M., Smith, C., van der Wijst, K., Al -Khourdajie, A., Lecocq, F., Portugal-Pereira, J., Saheb, Y., Stromman, A., Winkler, H., Auer, C., Brutschin, E., … van Vuuren, D. (2022). AR6 Scenarios Database [Data set]. In Climate Change 2022: Mitigation of Climate Change (1.1). Intergovernmental Panel on Climate Change. <a href="https://doi.org/10.5281/zenodo.7197970">https://doi.org/10.5281/zenodo.7197970</a></li> <li>Gidden, M., Gasser, T., Grassi, G., Forsell, N., Janssens, I., Lamb, W. F., Minx, J., Nicholls, Z., Steinhauser, J., & Riahi, K. (2023). Dataset for Gidden et.al. 2023 Updated AR6 Mitigation Benchmarks using National Emissions Inventories (Version v2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10158920">https://doi.org/10.5281/zenodo.10158920</a></li> </ul> <p>The variable imputation is based on the dataset by Byers et al. (2022). The dataset by Gidden et al. (2023) is used for variable comparison. </p>
US Emission Facilities Land Cover Area Derived At Parcel Scale
<p>This dataset includes US industrial facilities from the Environmental Protection Agency's (EPA) 2017 National Emissions Inventory (NEI), combined with location data from the EPA's Facility Registry Service and land cover classes from the United States Geological Survey's (USGS) National Land Cover Data (NLCD). These land cover classes are measured in square meters at the parcel scale. The matching of facility parcels was done using a tiered approach to enhance spatial accuracy. The parcel data was provided by Homeland Infrastructure Foundation-Level Data (HIFLD) US Parcel Data. Because this parcel data is proprietary, the parcel geometries and fields were removed from the final dataset. However, centroid latitude and longitude coordinates were derived to allow spatial joins with publicly available parcel data.</p> <p>This dataset is organized by unique EPA NEI facilities data fields. Unique facility observations are identified by the field <code>cleaned_name</code> which represents the concatenated address and/or place name for the facility (dependent on data availability). Each row represents a facility matched to parcel scale land cover information and are associated with the unique identifier <code>MatchID</code>. NLCD land cover fields are described as the total area in meters squared of each facility parcel. Please see data README file for more information on individual data fields. </p> <h3>Known Limitations</h3> <ul> <li>Parcels are matched to the facility and in some cases multiple facilities are matched to the same parcel. Data users may want to omit these multiple match parcels and there is a data flag called <code>multi_match</code> that enables this.</li> <li>Approximately 15% of the dataset includes facilities where the latitude/longitude coordinates are over 200 meters from the matched parcel. Spot checking these instances revealed that, in many cases, the facility latitude and longitude locations did not accurately match the street address, city, or postal zip code associated with the facility. These instances are flagged as a potential source of inaccuracy and can be removed at the users discretion.</li> </ul> <p> </p>
Data for: Terrestrial land use signals on groundwater fauna beyond current protection buffers (Ecological Applications, 2024)
<p>Original research article: Knüsel M., Alther R. & Altermatt F. (2024). Terrestrial land use signals on groundwater fauna beyond current protection buffers. <em>Ecological Applications.</em></p>
New advance in land controlled-source audio-frenquency magnetotellurics exploration: measurement at a shortened transmitter-receiver offset and three-dimensional inversion
<p><span>Ambient noise significantly affects the quality of land controlled-source audio-frequency magnetotellurics (CSAMT) data. Shortening the transmitter–receiver offset can enhance the raw data signal-to-noise ratios (S/N). However, most of CSAMT explorations still collect field data using a large separation. To enhance the S/N of the raw electric and magnetic field and the corresponding Cagniard apparent resistivity, we propose shortening offset to 2–4 km during CSAMT fieldwork and inverting apparent resistivity rather than only electric field. We tested this approach with three experimental models with varying offsets, simulating 3D responses. Result showed that longer offsets predominantly produce data anomalies in the far-field zone, whereas shorter offsets shift these anomalies toward the transition zone with some in the far-field or near-field zones. Shortened-offset responses exhibit stronger electric and magnetic fields. Synthetic datasets with different noise levels were generated for inversion analysis. The results indicate that shortening the offset, particularly in noisy environments, improves data S/N. Inversion results reveal that a shortened CSAMT transmitter–receiver offset allows for data collection with most anomalies in the transition-field zone, facilitating accurate prediction of the true model in the 3D inversion. In low-noise environments, inversion results from data with a shortened offset are comparable to those with a long offset. However, in high-noise environments, the shortened-offset approach yields more accurate results due to improved data S/N. Field data inversion from Yanqing, China, further confirms the effectiveness of our scheme. The shortened-offset approach achieves higher S/N datasets and inversion results that align with actual geological structures.</span></p> <p> </p>
LAND RESOURCES OF YAKUTIA'S AGRICULTURE IN THE LAST DECADE OF SOCIALISM: PECULIARITIES OF LAND ACCOUNTING OF STATE FARMS IN THE ARCTIC AND NORTHERN REGIONS
<p><span>The article shows the peculiarities of land resources utilization in the traditional economy of the indigenous population of Yakutia in the last decade of the Soviet period with a separate delineation of the state of the land balance and lands used in agriculture in 1990-1991. Including on the basis of archival data on land resources of state farms of the studied 15 arctic and northern regions, peculiarities of their accounting, preliminary results of statistical analysis of land resources of these large farms are obtained. The author introduces into scientific turnover new factual materials on land resources of separate state farms for the last Soviet 1991, in particular on their agricultural lands, reindeer and horse pastures.</span></p>
Data sets accompanying "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning"
<p>This data sets accompany the publication "Methodological and reporting inconsistencies in land-use requirements misguide future renewable energy planning" in One Earth.</p> <ul> <li>lur-db-output-zenodo.xlsx: contains all land use requirement estimates for renewable energies reviewed in the publication. Meta-data is reported in one sheet, the other sheet contains the original data.</li> <li>authorship-tree-zenodo.xlsx: contains the information necessary to derive the authorship tree shown in the supplementary information. Meta-data is reported in one sheet, the other sheet contains the original data.<br><br><br></li> </ul>
Fig. 5 in Oviposition of AedeS japoNiCUS japoNiCUS (Diptera: Culicidae) and associated native species in relation to season, temperature and land use in western Germany
Fig. 5 Occurrence of mosquito taxa in relation to trap location. The number of analysable traps is given in brackets. For abbreviations, see Fig. 2
Fig. 4 in Oviposition of AedeS japoNiCUS japoNiCUS (Diptera: Culicidae) and associated native species in relation to season, temperature and land use in western Germany
Fig. 4 Canonical correspondence analysis of land use types and mosquito taxa. Ae. j. japonicus occupied significantly more positive ovitraps in the settlement–forest transition zone than in other trap locations (Fisher's exact test: P = 0.0045 tested against arable land– forest, P <0.0001 tested against arable land–settlement)
Fig. 3 in Oviposition of AedeS japoNiCUS japoNiCUS (Diptera: Culicidae) and associated native species in relation to season, temperature and land use in western Germany
Fig. 3 Percentages of mosquito-positive ovitraps (Traps positive), and air temperatures. Air temperature was calculated in the week before sampling in 2018. Sampling dates and the number of analysable ovitraps from a total of 270 ovitraps (in brackets) are shown on the x-axis. There was no statistically significant difference between the numbers of Aedes japonicus japonicus-positive ovitraps sampled in spring (April–May) and in autumn (October–November) (identical lowercase letters). By contrast, the numbers of Ae. japonicus japonicus-positive ovitraps in the summer (12 June to 25 September) were statistically significantly higher than in spring and autumn (different lowercase letters) (Fisher's exact test: P <0.0001. For all P-values, see Additional file 1: Table S2. For total numbers of emerged adults, see Additional file 2: dataset S1
Fig. 6 in Oviposition of AedeS japoNiCUS japoNiCUS (Diptera: Culicidae) and associated native species in relation to season, temperature and land use in western Germany
Fig. 6 Logistic regression of probability proportion of adults hatched from larvae of Cx. pipiens s.l. vs. Ae. japonicus japonicus collected at the specified temperatures. Data from 2017 and 2018 (23 May to 25 September) for ovitraps located along the forest–settlement transect. Function jitter was used for taxa proportion data to improve visibility [85]
The LUCKINet land use ontology
<h1>1. Rationale</h1> <p>The LUCKINet land use ontology expands <a href="https://agrovoc.fao.org/browse/agrovoc/en">AGROVOC</a>, developed by the Food and Agriculture Organization of the United Nations (FAO). Like AGROVOC, the LUCKINet land use ontology uses semantic web technology powered by the Simple Knowledge Organization System (<a href="https://www.w3.org/TR/skos-primer/">SKOS</a>).</p> <p>The ontology has been primarily developed to support the LUCKINet effort to standardize approaches that develop gridded information on land use dynamics. As standards develop from the needs of various stakeholders, we have designed this ontology in a way that it can grow organically with emerging use cases. The focal unit is the crop or livestock species, which makes it easy to align our ontology with other vocabularies and typologies. The ontology is distinguished into two main sections: landcover and land-use concepts.</p> <p>We built the ontology with a strong focus on mapping land-use concepts to FAO/AGROVOC concepts to foster interoperability with the wealth of other projects building on AGROVOC. Moreover, concepts from different state-of-the-art datasets, such as land use from statistical agencies worldwide and the (grey) literature, are considered.</p> <p>You will find landcover concepts in this ontology in an unfamiliar way. In many projects, landcover is an amalgamation of concepts across various functional types of concepts. For example, 'cropland' is often regarded as a landcover, but because it also includes human activities managing that cover, it is not a pure <em>cover</em> but also a <em>use</em> type. We, therefore, revised the former version of this ontology, which was heavily centred on the <a href="https://land.copernicus.eu/pan-european/corine-land-cover">CORINE Land Cover</a> classification, to be more consistent from a semantic point of view. Any level now distinguishes only one conceptual dimension. The ontology is now distinguished into land cover, dynamic and use. <em>Cover</em> is characterized purely by the material or entities covering the surface, <em>dynamics</em> are characterized by the most dominant process (such as management or productiveness), and <em>use</em> is characterized by how the sphere covering the surface is managed and what is used.</p> <h1>2. Method</h1> <p>The ontology was built with the R-package <a href="https://cran.r-project.org/web/packages/ontologics/index.html">ontologics v0.7.3</a>, which enables reproducibility and transparency in creating an ontology. A script documenting this process is part of this publication.</p> <p>Concepts were taken from the external datasets harmonized during the LUCKINet and Global Pasture Watch project (the harmonized data are published on zenodo).</p> <h2>2.1 Sources</h2> <p>The ontology is an amalgamation of various datasets:</p> <ol> <li>The <a href="https://datalab.review.fao.org/datalab/caliper/web/classification-page/43">FAO Indicative Crop Classification</a> (ICC) v1.1 provides a hierarchical structure of crop and livestock concepts.</li> <li>The <a href="https://www.fao.org/faostat/en/#data/QCL">FAO Crops and livestock products</a> (QCL) dataset contains a range of concepts used by the FAO and frequently by statistical offices of nations that make their census data publicly available.</li> <li>The <a href="https://www.fao.org/faostat/en/#data/RL">FAO Land Use</a> (RL) dataset contains a hierarchical set of land-use concepts that represent the most harmonized representation of land-use concepts to date.</li> <li>The <a href="https://www.fao.org/3/I8661EN/i8661en.pdf">FAO Forest Resource Assessment</a> (FRA) contains a detailed set of forest classes that fit well with the FAO Land Use vocabulary and represent relevant classes at a coarse scale (Sections 1b, 1e, 1f).</li> <li><a href="https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/">CORINE Land Cover</a> (CLC) classes are used as the basis to define the (hierarchical) organization of land cover and land use classes (into which the crop and livestock commodities are nested).</li> </ol> <h2>2.2 Classes</h2> <p>The ontology is hierarchically structured into the following classes:</p> <ul> <li>domain <ul> <li>landcover <ul> <li>land dynamic <ul> <li>land use</li> </ul> </li> </ul> </li> <li>production <ul> <li>group <ul> <li>class <ul> <li>crop</li> <li>animal</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> <li> domain: the overarching dimensions into which all other concepts are nested. These represent conceptually different kinds of classification systems (landcover and production).</li> <li>landcover: surface area described by the predominant landcover in that location.</li> <li>production: surface area described by the crops and livestock produced in that location.</li> <li>land dynamic: surface area described by the predominant attribute determining dynamics within the landcover type.</li> <li>land use: surface area described by mutually exclusive types of how land covered by a particular type and dynamic is used.</li> <li>group: broad groups of concepts that describe crops and livestock.</li> <li>class: mutually exclusive types of concepts that describe crops and livestock.</li> <li>crop/animal: individual commodities (typically at species level).</li> </ul> <h2>2.3 Concepts</h2> <p>The bulk of the ontology consists of a collection of concepts, each an instance of a specific class. The concepts also bear hierarchical relations determined by their class affiliation. For example, the concept of <em>Cultivated grazingland</em> (class: use) is semantically narrower than <em>Managed vegetation</em> (class: dynamic), which in turn is narrower than the concept of <em>VEGETATED LAND</em> (class: lancover), which is narrower than <em>landcover</em> (class: domain).</p> <h2>2.4 Mappings</h2> <p>As far as possible, concepts are mapped to all corresponding wikidata and GBIF concepts. For example, the concept of <em>maize</em> is mapped to wikidata concepts <a href="https://www.wikidata.org/wiki/Q11575">Q11575</a> and <a href="https://www.wikidata.org/wiki/Q25618328">Q25618328</a> and GBIF concept <a href="https://www.gbif.org/species/5290052">5290052</a>. Moreover, concepts are mapped to the</p> <ul> <li>life-form concepts <em>graminoid</em>, <em>forb</em>, <em>herb</em>, <em>shrub</em>, <em>tree</em></li> <li>purpose concepts <em>energy</em>, <em>fibre</em>, <em>food</em>, <em>wood</em>, <em>forage</em>, <em>silage</em>, <em>fodder</em>, <em>industrial</em>, <em>recreation</em>, <em>medicinal </em>and <em>labour </em>and <em>reproduction</em></li> <li>used-part concepts <em>bark</em>, <em>bast</em>, <em>biomass</em>, <em>buds</em>, <em>bulb</em>, <em>eggs</em>, <em>flower</em>, <em>fruit</em>, <em>hair</em>, <em>honey</em>, <em>husk</em>, <em>leaves</em>, <em>lint</em>, <em>meat</em>, <em>dairy</em>, <em>resin</em>, <em>root</em>, <em>sap</em>, <em>seed</em>, <em>shoot</em>, <em>stalk</em>, <em>time</em>, <em>tuber</em>, <em>fur </em>and <em>silk</em></li> <li>agronomic attributes such as the <em>years-before-harvest</em> and <em>years-of-harvest</em> (i.e., for how many years a crop, once mature, can be harvested)</li> </ul> <h1>3. Files</h1> <p>- <code>make_ontology.R</code>: The script used to build this ontology<br>- <code>luckiOnto.rds</code>: the ontology to access and manipulate with R and the ontologics R-package</p> <h1>4. Version changes</h1> <ul> <li>uses a new version of <code>ontologics</code></li> <li>revised hierarchy of landcover concepts</li> <li>includes livestock concepts</li> <li>no mapping to 'persistence' concepts anymore; instead, years-before-harvest (YBH) and years-of-harvest (YOH) are introduced.</li> <li>various rare species are now included</li> </ul> <h1>5. Acknowledgements</h1> <p>This work was supported by funding to Carsten Meyer through the Flexpool mechanism of the German Centre for Integrative Biodiversity Research (iDiv) (FZT-118, DFG). Several developments in this software stem from work on the GeoKur research data infrastructure. GeoKur is granted by the BMBF (Federal Ministry of Education and Research) under number 16QK04A. Revisions of the ontology pertaining to the landcover hierarchy and livestock concepts were made during the initial funding period of the Global Pasture Watch Project.</p>
KARI drone vertical takeoff and landing navigation dataset
<p>Dataset for testing visual-inertial navigation under constant velocity, and obstacle detection with LiDAR.</p> <p>Data was collected in the field by a custom-built hexacopter mounting the following sensors.<br>Rostopics are given in parentheses:</p> <ul> <li>Nadir-pointing RealSense d455 payload (visual-inertial odometry): 10 Hz RGB camera frames (/camera/color/image_raw), 200 Hz IMU data (/camera/imu)</li> <li>RTK GNSS (groundtruth): position, velocity, attitude quaternions (/mavros/global_position/local)</li> <li>Nadir-pointing Livox Avia scanning LiDAR payload (safe landing site detection, only available during landing): pointcloud (/livox/lidar)</li> <li>Nadir-pointing doppler radar altimeter: altitude (/sensor_data)</li> </ul> <p><a href="https://github.com/dirkpitt2050/open_vins">https://github.com/dirkpitt2050/open_vins</a> contains an example config, kari_vtol, that shows how to use the dataset.</p>
Linked collectors and determiners for: Notice of remains of extinct reptiles and fishes, discovered by Dr. FV Hayden in the Bad Lands of the Judith River, Nebraska Territory..
Natural history specimen data linked to collectors and determiners held within, "Notice of remains of extinct reptiles and fishes, discovered by Dr. FV Hayden in the Bad Lands of the Judith River, Nebraska Territory.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5adaed8a-c1bb-48b3-bd0e-fd1022bac4fa">https://bionomia.net/dataset/5adaed8a-c1bb-48b3-bd0e-fd1022bac4fa</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5adaed8a-c1bb-48b3-bd0e-fd1022bac4fa">https://gbif.org/dataset/5adaed8a-c1bb-48b3-bd0e-fd1022bac4fa</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: BioGIS - Land snails - HUJ collections.
Natural history specimen data linked to collectors and determiners held within, "BioGIS - Land snails - HUJ collections". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/b6834d2b-77de-49ac-9b23-c93ebf54e245">https://bionomia.net/dataset/b6834d2b-77de-49ac-9b23-c93ebf54e245</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/b6834d2b-77de-49ac-9b23-c93ebf54e245">https://gbif.org/dataset/b6834d2b-77de-49ac-9b23-c93ebf54e245</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Auckland Museum Land Vertebrates Collection.
Natural history specimen data linked to collectors and determiners held within, "Auckland Museum Land Vertebrates Collection". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/aad97542-a068-449d-adb3-a8e937f64cb4">https://bionomia.net/dataset/aad97542-a068-449d-adb3-a8e937f64cb4</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/aad97542-a068-449d-adb3-a8e937f64cb4">https://gbif.org/dataset/aad97542-a068-449d-adb3-a8e937f64cb4</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: ICEL (Land - Invertebrates).
Natural history specimen data linked to collectors and determiners held within, "ICEL (Land - Invertebrates)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/902dd5f6-d3a0-4ef0-b2f8-8e3f96229ec9">https://bionomia.net/dataset/902dd5f6-d3a0-4ef0-b2f8-8e3f96229ec9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/902dd5f6-d3a0-4ef0-b2f8-8e3f96229ec9">https://gbif.org/dataset/902dd5f6-d3a0-4ef0-b2f8-8e3f96229ec9</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Additions to the knowledge of the land snails of Sabah (Malaysia, Borneo), including 48 new species.
Natural history specimen data linked to collectors and determiners held within, "Additions to the knowledge of the land snails of Sabah (Malaysia, Borneo), including 48 new species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/70702787-a6a8-4af7-9702-738f344a5cbe">https://bionomia.net/dataset/70702787-a6a8-4af7-9702-738f344a5cbe</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/70702787-a6a8-4af7-9702-738f344a5cbe">https://gbif.org/dataset/70702787-a6a8-4af7-9702-738f344a5cbe</a>. Formatted as a Frictionless Data package.
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.