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635 results for “attributes”
Burn history and patch attributes
We present the recorded fire occurrences from 1967 to 2017 for 92 independent Florida Rosemary Scrub patches at Archbold Biological Station, Lake Placid, Florida. In addition, we include a series of habitat attributes, including patch area, patch isolation (described by the Hanski's index), and relative elevation, associated with those patches, as well as presence/absence data for Hypericum cumulicola, a Florida endemic plant.
Physical and chemical attributes of Quebrada Prieta, Bisley 3, Bisley 5, and Toronja related to shrimp populations measurements
Physical parameters, and sizes of two species of freshwater shrimps (Atya lanipes and Xiphocaris elongata) in four headwater streams (Prieta, Toronja, Bisley 3 and Bisley 5) have been censused 2 times yearly since 1998 to determine the effects of predatory fishes on shrimp size and spatial distributions of pools relative to locations of waterfalls. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
FIGURE 4 in Bite marks attributable to Tyrannosaurus rex: preliminary description and implications
FIGURE 4. Edmontosaurus proximal pedal phalanx (UCMP 140601) bearing tyrannosaur-like "puncture and pull" bite mark furrows. The specimen bears five such bite marks (three are visible). The furrows are deeper proximally suggesting the element was somewhat articulated at the time of feeding. Scale = 2.5 cm.
FIGURE 1 in Bite marks attributable to Tyrannosaurus rex: preliminary description and implications
FIGURE 1. Photographs of Tyrannosaurus rex bite marks on an adult Triceratops pelvis (MOR 799). Anterolateral view of the ventral surface of the sacrum and left ilium of the specimen. Arrows point to some of the more prominent tooth marks (over 58 definitive tooth marks are present on the pelvis). Furrows from "puncture and pull" biting and deep localized punctures are visible. Brackets encompass a region where the theropod(s) removed approximately one sixth of the anterior ilium via repetitive peripheral biting. Scale = 25 cm.
Characteristics of Marginalised Rural Areas in Europe and the Mediterranean Region: Shapeflie and associated attributes
<p>The H2020 project on Social Innovation in Marginalised Rural Areas (SIMRA) focused on understanding social innovation and innovative governance in agriculture, forestry and rural development, and how to boost them, particularly in marginalised rural areas across Europe, with a focus on the Mediterranean region (including non-EU). Its geographic focus was on Marginalised Rural Areas (MRAs), which had not previously been defined.</p> <p>The analysis of the rural areas of Europe and the Mediterranean area required data of consistent spatial and temporal resolutions for variables of three types: physical geography, infrastructure (spatial marginality), and socio-economic (societal marginality). There few datasets of relevance that exist for the entire area, creating a need to derive spatial datasets and produce associated maps of the characteristics that contribute to marginality or marginalization.</p> <p>The outputs comprise new spatial datasets at resolutions compatible with the underlying information (e.g. 1km2, NUTS 3, NUTS 2, and local authorities in North Africa and the eastern Mediterranean), enabling comparisons between such areas. The associated maps and a tabulation of the characteristics for the entire area of interest to SIMRA are reported in Price et al. (2017).</p> <p>This spatial dataset contains the characteristics of the Marginalised Rural Areas as attributes in a Shapefle for use in a Geographic Information System. Details of the attributes in the Shapefle, and their values, are provided in the MS Excel spreadsheet downloadable with this dataset.</p> <p>Reference:</p> <p>Price, M., Miller, D.R., McKeen, M., Slee, W. and Nijnik, M. 2017. Categorisation of marginalised rural areas (MRAs). Deliverable 3.1, Social Innovation in Marginalised Rural Areas (SIMRA). Report to the European Commission, pp. 57. 10.5281/zenodo.3625493</p> <p> </p> <p>The boundaries in the spatial dataset are complied from: Nomenclature of Territorial Units for Statistics (NUTS) 2013 European Commission, © EuroGeographics, © FAO (UN), © TurkStat Source: European Commission – Eurostat/GISCO© for administrative boundaries. All other boundary data were extracted from the GADM database (www.gadm.org), version 2.8, November 2015. They can be used for non-commercial purposes only. It is not allowed to redistribute these data, or use them for commercial purposes, without prior consent. See the website for more information.<br> </p>
Multi-Attributed Structured Text-to-face Dataset
<p>A new data consolidation called Multi-Attributed and Structured Text-to-face (MAST) dataset. The motivation is to have a large corpus of high-quality face images with fine-grained and attribute-focussed annotations. This has the benefits of the attribute oriented approach as well as the semantics in a textual description.</p>
source_data_flood_attribution
<p>The dataset includes source data for the journal article:</p> <p>Inga Sauer, Ronja Reese, Christian Otto, Tobias Geiger, Sven Willner, Benoit Guillod, David Bresch, and Katja Frieler. “Climate Signals in River Flood Damages Emerge under Sound Regional Disaggregation,” <a href="https://doi.org/10.21203/rs.3.rs-37259/v1">10.21203/rs.3.rs-37259/v1</a></p> <p>It provides data data required for hazard and exposure modeling required for damage modeling provided in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).</p> <p>Hazard modeling: provided are spatially explicit flooded areas and flood depth on a 150arcsec resolution and discharge on a 0.25 degree resolution the file names follow the structure: variable_resolution_ghm_climateforcing_protectionstandard.nc (for flood depth (flddph) and flooded fraction (fldfrc)) and variable_ghm_climateforcing.nc</p> <p>Exposure modeling:</p> <p>The file gdp_1850_2100_150arcsec.nc contains yearly gridded-GDP on a 150 arcsec resolution converted to PPP 2005 USD. Between 2000 and 2010 there is a transition between observed GDP and the future socio-economic development scenario SSP2.</p> <p>The application of the datasets for damage modeling and for the reproduction of the article data is described here:</p> <p>https://github.com/ingajsa/flood_attribution_paper</p> <p>More information on flood modeling can be found at: 10.5281/zenodo.1241051</p> <p>For the gridded_GDP, see also: https://doi.org/10.5880/pik.2017.003</p>
LabelGit: A dataset for software repositories classification using attributed dependency graphs
<p>A dataset for software repositories classification using attributed dependency graphs</p>
Transformation attributes from GTZAN audio database
<p>This data set was used in my master thesis "Reproducibility of Machine Learning results". On one hand, this dataset was used as an input in creating Support Vector Machine , Naive Bayes and Random Forest models in R, Python, RapidMiner and Weka in order to experiment the influence that different development environments have on reproducibility of machine learning results. Furthermore all the models implemented in all of previously mentioned environments were tested in different operating systems such as Windows, Linux and Mac OS. </p>
CAMELS-DE: hydrometeorological time series and attributes for 1582 catchments in Germany
<h2>Description</h2> <p>CAMELS-DE provides a comprehensive collection of hydro-meteorological timeseries data (e.g. discharge, water level, precipitation, air temperature) and catchment attributes for 1582 streamflow gauges across Germany. The time series data is in daily resolution and spans up to 70 years, from January 1951 to December 2020. The static catchment attributes include information on topography, soils, land cover, hydrogeology and human influences. Additionally, the dataset includes discharge simulations from a regional Long-Short Term Memory (LSTM) network and a conceptual hydrological model (HBV), providing benchmark data for future hydrological modelling studies in Germany.</p> <p>The accompanying data description gives information on data sources, the structure of the data set and contains extensive information on time series and catchment attribute variables. In addition, up-to-date benchmark results of the LSTM and HBV are provided.</p> <blockquote> <p><strong><strong>Important: As CAMELS-DE is continuously developed and updated, please ensure that you cite the correct version of the dataset that you are using.<br><br></strong></strong><strong>The CAMELS-DE data description paper is available here: <a href="https://doi.org/10.5194/essd-16-5625-2024">https://doi.org/10.5194/essd-16-5625-2024</a>.</strong></p> </blockquote> <p>Information about the code and methods for generating CAMELS-DE can be found here: <a title="CAMELS-DE Processing Pipeline" href="https://doi.org/10.5281/zenodo.12760336" target="_blank" rel="noopener">CAMELS-DE Processing Pipeline</a>.</p> <p>CAMELS-DE is also part of the Caravan project, a global hydrological dataset. Due to the use of data products that are available beyond the Germany national boundaries, Caravan-DE includes 305 additional streamflow gauges, resulting in a total of 1887 streamflow gauges: <a href="https://doi.org/10.5281/zenodo.13320514">https://doi.org/10.5281/zenodo.13320514</a>.</p> <h3>Disclaimer for discharge and water level data provided by the German federal state agencies:</h3> <p>english:<em><br>The state agencies do not guarantee the accuracy or completeness of the discharge or water level data provided. In addition, all hydrological data may be subject to future revisions, including adjustments to the rating curves or corrections of errors. Therefore, it is necessary to obtain the most recent discharge time series directly from the federal state authorities for projects that require water law permits. Additionally, the regulations of the respective federal state apply and specific enquiries should be made as needed. It is also important to note that the state agencies explicitly disclaim any warranty as to the accuracy or completeness of the data and therefore any liability claims against any of the federal states are also excluded.</em></p> <p>german:<em><br>Die Ländesämter gewährleisten nicht die Genauigkeit oder Vollständigkeit der bereitgestellten Abfluss oder Wasserstandsdaten. Zudem können alle hydrologischen Daten zukünftigen Überarbeitungen unterliegen, einschließlich Anpassungen der Wasserstands-Abflussbeziehung oder der Korrektur von Fehlern. Daher ist es notwendig, die aktuellsten Abflusszeitreihen direkt bei den Landesbehörden zu beziehen, falls Wasserrechtsgenehmigungen erforderlich sind. Zusätzlich gelten die Vorschriften des jeweiligen Bundeslandes, und spezifische Anfragen sollten bei Bedarf gestellt werden. Es ist ebenfalls wichtig zu beachten, dass die staatlichen Behörden ausdrücklich jegliche Gewährleistung hinsichtlich der Genauigkeit oder Vollständigkeit der Daten ausschließen und somit auch jegliche Haftungsansprüche gegenüber einem der Bundesländer ausgeschlossen sind.</em></p> <h3>Changelog</h3> <ul> <li><strong>v1.1.0</strong> <ul> <li>LSTM benchmark results are now based on a <strong>LSTM with 10 ensemble members</strong>, changing the median NSE in the testing period from 0.83 to 0.85 <div> <ul> <li>The columns <em>discharge_spec_sim_lstm</em> and <em>discharge_vol_sim_lstm</em> in <em>timeseries_simulated</em> are now based on the median values of the 10 ensemble members.</li> <li>The column <em>NSE_lstm</em> in <em>CAMELS_DE_simulation_benchmark.csv</em> is now calculated from the median simulations of the 10 ensemble members</li> <li><em>model_parameters/LSTM/CAMELS_DE_epochs_training_lstm.zip</em> now contains the epochs of the 10 ensemble members</li> </ul> </div> </li> <li>The columns <em>NSE_lstm</em>, <em>NSE_hbv</em> and <em>training_perc_complete</em> in <em>CAMELS_DE_simulation_benchmark.csv</em> were calculated from 2001 - 2020, now corrected to 2000 - 2020</li> <li>Fixed a bug in the calculation of <em>high_prec_dur</em> and <em>low_prec_dur</em> in <em>CAMELS_DE_climatic_attributes.csv</em> calculation (thank you to Bastian Klein from BfG for reporting this issue)</li> <li>Bayern: removed blank space after gauge and water body name and removed water body name from some gauge names, where it was included as "[gauge_name]_[water_body_name]", e.g. "Würzburg_Main" -> "Würzburg", water body name is now only included in the `water_body_name` column in<code> </code><em>CAMELS_DE_topographic_attributes.csv</em></li> <li>Nordrhein-Westfalen: corrected some wrong river names in <em>CAMELS_DE_topographic_attributes.csv</em></li> <li>Sachsen: added `gauge_elevation_metadata` information to <em>CAMELS_DE_topographic_attributes.csv</em></li> </ul> </li> </ul> <ul> <li><strong>v1.0.0</strong> <ul> <li>CAMELS-DE v1.0.0 is the version of the dataset that is described by the <a href="https://doi.org/10.5194/essd-2024-318">CAMELS-DE data description paper</a>.</li> <li>Addition of the federal state of Saarland, resulting in 27 additional catchments and coverage of all federal states except the city states of Berlin, Bremen and Hamburg. This also leads to a change in the title of the dataset from 1555 catchments to 1582 catchments.</li> <li>Addition of HBV model parameters and LSTM model training period epochs.</li> <li>Catchment DE911970: Removal of erroneous zero discharge values at the beginning of the measurement period.</li> <li>Minor fixes such as the elimination of discrepancies between the variable names in the dataset and in the data description.</li> <li>We were able to identify and fix some of these problems based on the feedback from the community, thank you very much!</li> </ul> </li> </ul>
Fig. 6. Misgolas trangae n in Trapdoor Spiders of the Genus Misgolas (Mygalomorphae: Idiopidae) in the Sydney Region, Australia, With Notes on Synonymies Attributed to M. rapax
Fig. 6. Misgolas trangae n.sp. (A–D) Ƌ, holotype AM KS49026. (A), right palp retrolateral. (B,C), right bulb: (B),
Fig. 12 in Trapdoor Spiders of the Genus Misgolas (Mygalomorphae: Idiopidae) in the Sydney Region, Australia, With Notes on Synonymies Attributed to M. rapax
Fig. 12. Species distribution of Misgolas species in the Sydney region (eastern Australia) based on material examined. Key to symbols for maps (A) and (B): O Misgolas gracilis; A M. melancholicus; Z M. villosus. Map (C): • M. beni; Z M. cliffi; Δ M. lynabra; M. maculosus; O M. michaeli; * M. rodi; A M. trangi; * M. wayorum.
Fig. 9. Misgolas rodi n in Trapdoor Spiders of the Genus Misgolas (Mygalomorphae: Idiopidae) in the Sydney Region, Australia, With Notes on Synonymies Attributed to M. rapax
Fig. 9. Misgolas rodi n.sp. (A–D) Ƌ, holotype AM KS50083. (A), right palp retrolateral. (B,C), right bulb: (B),
Fig. 3 in Trapdoor Spiders of the Genus Misgolas (Mygalomorphae: Idiopidae) in the Sydney Region, Australia, With Notes on Synonymies Attributed to M. rapax
Fig. 3. Misgolas gracilis. (A–D) Ƌ, AM KS22910. (A), right palp retrolateral. (B,C), right bulb: (B), dorsal; (C), prolateral. (D), venter. (E) Ƌ, AM KS34720, venter. (F,G) ♀, AM KS44339; (F), tarsus and metatarsus IV retrodorsal; (G), venter. (H,I) Ƌ, AM KS86211; (H), ventral aspect, palpal tibia excavation; (I), tibial excavation texture.
Fig. 4. Misgolas cliffi n in Trapdoor Spiders of the Genus Misgolas (Mygalomorphae: Idiopidae) in the Sydney Region, Australia, With Notes on Synonymies Attributed to M. rapax
Fig. 4. Misgolas cliffi n.sp. (A–D) Ƌ, holotype AM KS36559. (A), right palp retrolateral. (B,C), right bulb: (B), dorsal; (C), prolateral. (D), venter. (E) ♀, allotype AM KS7472, tarsus and metatarsus IV retrodorsal.
Fig. 10. Misgolas beni n in Trapdoor Spiders of the Genus Misgolas (Mygalomorphae: Idiopidae) in the Sydney Region, Australia, With Notes on Synonymies Attributed to M. rapax
Fig. 10. Misgolas beni n.sp. (A–D) Ƌ, holotype AM KS38550. (A), right palp retrolateral. (B,C), right bulb: (B), dorsal; (C), prolateral. (D), venter.
Data from: The mechanism of promoting rhizosphere nutrient turnover for arbuscular mycorrhizal fungi attribute to recruited functional bacterial assembly
<p>Symbiosis with arbuscular mycorrhizal (AM) fungi improves plant nutrient capture from the soil, yet there is limited knowledge about the diversity, structure, functioning, and assembly processes of AM fungi-related microbial communities. Here, 16S rRNA gene sequencing and metagenomic sequencing were used to detect bacteria in the rhizosphere of <em>Lotus japonicus</em> inoculated with and without AM fungi, and the <em>L. japonicus</em> mutant <em>ljcbx</em> (defective in symbiosis) inoculated with AM fungi in southern grassland soil. Our results show that AM symbiosis significantly increased bacterial diversity and promoted deterministic processes of bacterial community construction, suggesting that mycorrhizal symbiosis resulted in the directional enrichment of bacterial communities and established a stable rhizosphere bacterial community. AM fungi promoted the enrichment of nine bacteria, including <em>Ohtaekwangia</em>, <em>Niastella</em>, <em>Gemmatimonas</em>, <em>Devosia</em>, <em>Sphingomonas</em>, <em>Novosphingobium</em>, <em>Opitutus</em>, <em>Lysobacter</em>, <em>Brevundimonas</em>, which are positively correlated with NPK-related parameters. Through a functional identification experiment, we found that six of these genera, including <em>Brevundimonas</em>, <em>Lysobacter</em>, <em>Ohtaekwangia</em>, <em>Sphingomonas</em>, <em>Devosia</em>, and <em>Gemmatimonas</em>, demonstrated the ability to mineralize organophosphate and dissolve inorganic phosphorus, nitrogen, and potassium. Our study revealed that AM fungi can regulate rhizosphere bacterial community assembly and attract specific rhizosphere bacteria to promote soil nutrient turnover in southern grasslands.</p>
A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1
<p>A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies. GSHA covers 21,568 watersheds from 13 agencies for as long as 43 years based on the discharge observations scraped from the web. GSHA includes yearly streamflow characteristics derived from daily discharge observations, daily meteorological variables (including precipitation, 2-m air temperature, long- and shortwave radiation, wind speed, actual and potential evapotranspiration (AET and PET)), daily or weekly water storage terms (4 layers of soil moisture, groundwater, and snow depth water equivalence), daily vegetation index (leaf area index (LAI)), yearly LULC characteristics (urban, cropland, and forest fraction), and yearly reservoir information (degree of regulation (DOR) and reservoir capacity). For each meteorological variable, multiple independent data sources are incorporated to provide uncertainty estimates. Static attributes like land physiography, soils, and geology are not additionally extracted, as similar efforts have been made by other researchers, so we directly matched our gauge locations to the HydroATLAS dataset by providing the river ID match table.</p> <p>For more details of GSHA, please refer to a companion research article submitted to ESSD.</p> <p>Please access the variables in version 1.0. Monthly streamflow indices files do not include Chinese basins.</p> <p>Citation: <strong> </strong>Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., and Cai, S.: A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-256, in review, 2023.</p> <p> </p>
Survey: coopetition attributes, coopetition life cycle, and coopetition performance_UMO-2020/39/B/HS4/00935
<p>The data set covers Likert-type data on coopetition attributes, coopetition life cycle, and coopetition performance.</p> <p><span>Data collection: December 2022 and March 2023 using a mixed-mode (CATI, CAWI, and CAWI supported by phone). </span></p> <p><span>Sample: 1231 (909 low-tech firms and 322 high-tech firms).</span></p> <p><span>Finacned by a research grant by National Science Centre in Poland under agreement UMO-2020/39/B/HS4/00935.</span></p> <p> </p> <p> </p>
CAMELS-AUS v2: updated hydrometeorological timeseries and landscape attributes for an enlarged set of catchments in Australia
<p>Version 2 of the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. Since publication in 2021, CAMELS-AUS (Australia) has served as a resource for the study of hydrological change, arid-zone hydrology, and hydrological model improvement. In this update, the dataset has been significantly enhanced both temporally and spatially. The new dataset comprises information for over twice as many catchments (561 compared to 222). The streamflow and climatic information are updated a further eight years (2022 compared to 2014). Lastly, the attribute information is improved, particularly with respect to hydrological statistics (signatures) and uncertainty in streamflow. Together, these updates make CAMELS-AUS Version 2 a more comprehensive and current resource for hydrological research and applications. </p>
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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.