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22,710 results for “Plant”
Annual Aboveground Net Primary Productivity by plant functional groups across grassland-shrubland ecotones at 3 sites in the Jornada Basin, 2006-ongoing
The objective of this ongoing study is to investigate how pulses of precipitation translate into pulses of plant aboveground net primary productivity (NPP) across grassland to shrubland ecotones in the northern Chihuahuan Desert. This dataset consists of annual aboveground net primary productivity estimates by plant functional groups in three habitat vegetation zones (grassland, ecotone, and shrubland) at three grassland-to-shrubland ecotone sites in the Jornada Basin, Dona Ana County, New Mexico, USA. The annual ANPP estimates are derived from plant cover measurements (see methods). Due to its growth form, Yucca elata (YUEL), in the leaf succulent functional group, has been found to produce large errors in interyear biomass estimates. This data package separates biomass estimates for YUEL and non-YUEL leaf succulents so that users can decide whether to combine them or keep them separate. In general, the authors recommend against using the YUEL estimates for most purposes. Data collection is ongoing with new observations in spring and fall of each year; data from both annual sampling times are required to estimate annual ANPP.
CGP01 Gall-insect densities on selected plant species in watersheds with different fire frequencies
Long-term monitoring of gall-insect densities on Solidago canadensis, Vernonia baldwinii, and Ceanothus herbaceous. Gall abundances are censused in watersheds burned at one- to twenty- year intervals to asses the role of fire frequency and time since fire on gall-insect population dynamics. The data sets contain the following: Watershed fire frequency, number of growing seasons since last fire, plant species, number of galled stems, and number of censused stems. Censuses conducted for the 1989-1996 growing seasons except 1992 and 1994, next scheduled census is fall 1997.
PAB01 Aboveground net primary productivity of tallgrass prairie based on accumulated plant biomass on core LTER watersheds (001d, 004b, 020b)
Data set contains estimates of end-of-season standing crop biomass (grams per square meter) of live graminoids, forbs, woody plants, and previous year's dead vegetation for 2 soil types (shallow and deep) on three core LTER watersheds representing three fire frequency treatments. Twenty quadrats (0.1 square meters) are harvested for each soil/treatment type. NOTE: Early (April) and mid-season (July) biomass was collected from 1983-1988, and these data are available by request.
North Temperate Lakes LTER Regional Survey Macrophytes Plant Index 2015 - current
The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry. The purpose of the macrophyte survey is to identify, and quantify the types of aquatic plants within the various 28 regional survey lakes. The macrophyte survey consists of sampling macrophyte plants using a metal rake attached to a 15ft pole at approximately 140 spatially resolved points on a lake that are spread out in a grid like fashion, equally spaced from each other. Sampling locations were chosen such that the maximum depth at which macrophytes were surveyed was equal to or less than 15ft of water. Macrophyte sampling occurs in the latter part of the summer (after July 10) to ensure that macrophytes have had adequate time to grow and our sampling efforts capture the typical summer macrophyte community in each lake. Macrophyte sampling in these 28 lakes is ongoing and will be repeated approximately once every six years.
Individual plant growth and reproduction in the black sand extended growing season experiment for East Knoll, Audubon, Lefty, and Trough sites, 2018 - 2020.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites, each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows and a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot at each site by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to control plots after snow had naturally melted. We used open top warming chambers (OTCs) to increase summer temperature in three subplots within each of the 10 x 40 m plots. This dataset includes measurements of growth and reproduction for Geum plants as well as counts of buds and flowers were counted and recorded for each tagged study plant, within all snow and warming treatments at all sites except Soddie.
Plant species composition for sensor network array, 2017 - ongoing.
Above-ground plant species cover was recorded for vegetation plots in the sensor network, starting in 2017. Cover was measured annually at peak biomass using a 100 point-intercept method.
Plant phenology observations in the black sand extended growing season experiment for East Knoll, Audubon, Lefty, and Trough sites, 2018 - 2020.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites, each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows and a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot at each site by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to control plots after snow had naturally melted. We used open top warming chambers (OTCs) to increase summer temperature in three subplots within each of the 10 x 40 m plots. This dataset includes measurements of plant species phenology within all snow and warming treatments at all sites except Soddie.
Plant species composition in ITEX subplots in black sand extended growing season experiment, 2018 - 2023.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites, each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows and a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot at each site by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to control plots after snow had naturally melted. We used open top warming chambers (OTCs) to increase summer temperature in three subplots within each of the 10 x 40 m plots. This dataset includes measurements of plant species composition within all snow and warming treatments.
Plant species composition data for Saddle grid, 1989 - ongoing.
Permanent 1 m^2 vegetation plots were established near each of the 88 Saddle grid stakes in 1989 by Marilyn Walker, who led the sampling effort until 1997. To estimate plant canopy cover, point quadrat measurements have been made at irregular intervals from 1989 to the present (1989, 1990, 1995, 1997, 2006, 2008 and yearly from 2010 onward). The point-quadrat technique used for sampling was described in Spasojevic et al. (2013) and Auerbach (1992). Auerbach, N. 1992. Effects of road and dust disturbance in minerotrophic and acidic tundra ecosystems, northern Alaska. University of Colorado, Boulder, Colorado, USA. Spasojevic, Marko J, William D Bowman, Hope C Humphries, Timothy R Seastedt, and Katharine N Suding. Changes in alpine vegetation over 21 years: Are patterns across a heterogeneous landscape consistent with predictions?” Ecosphere 4, no. 9 (2013): 1–18. https://doi.org/10.1890/es13-00133.1.
Plant species cover and biomass for Sevilleta dominant species removal experiment.
The purpose of this research project was to connect the removal of dominant grass species in grasslands at the Sevilleta National Wildlife Refuge to changes in plant community composition and subsequent changes in aboveground biomass. We used species cover data for 23 years of a dominant species removal experiment (https://doi.org/10.6073/pasta/fd3c777524231ae245bf1916715c9140) and converted percent cover values to aboveground standing biomass using methods from Rudgers et al. 2019 (https://doi.org/10.1111/1365-2435.13463). For this project, only two sites from the original study were used blue grama (site 1) and black grama (site 3) as they are referred to in the original study.
Barrier Island Plant and Soil Properties on Hog and Metompkin Islands, Virginia, 2021-2022
Dune building has the potential to impact the entire barrier island ecosystem, and these grasses therefore serve as ecosystem engineers. Protection offered by dune ridges directly impacts the adjacent swale habitat, modifying both biotic and abiotic factors. In order to better understand how dune building impacts the island ecosystem as a whole, we quantified sediment accretion, plant percent cover, stem numbers, and soil characteristics (chlorides, bulk density, %OM, %C, %N). These characteristics were assessed on two islands with varied disturbance intensities. Hog island is infrequently disturbed, and resists change driven by storms and overwash. Metompkin island is frequently disturbed and undergoes high rates of overwash and island migration.
Plant richness of the terrestrial ecoregions of the world with a mean aridity index lower than 0.65
<p>Data used to compose the <strong>Figure 1</strong> and the <strong>Table S1</strong> of the paper <strong>Biogeography of Global Drylands</strong>, by Maestre <em>et al</em>. (2021).</p>
Silica Nanoparticles Enhance Disease Resistance in Arabidopsis Plants - RAW DATA
<p>These datasets are used to produce the figures/graphs published in our article</p> <p><strong>Silica Nanoparticles Enhance Disease Resistance in <em>Arabidopsis</em> Plants</strong></p> <p>in <em>Nat. Nanotechnol.</em> (2020). <a href="https://doi.org/10.1038/s41565-020-00812-0">https://doi.org/10.1038/s41565-020-00812-0</a></p> <p> </p><p><strong>Correspondence: </strong></p> <p></p> <p>fabienne.schwab@alumni.ethz.ch, Tel: +41 78 736 00 19;</p> <p>m.shetehy@uky.edu, Tel. +41 76 455 56 02</p> <p>Further raw data related to qPCR and microbiology are available upon reasonable request from M.H. El‑Shetehy.</p> <p>Further raw data related to the nanoparticles and plant microscopy are available upon reasonable request by F. Schwab.</p> <p> </p> <p><strong>Abstract</strong></p> <p>In plants, pathogen attack can induce an immune response known as systemic acquired resistance (SAR) that protects against a broad spectrum of pathogens. In the search for safer agrochemicals, silica nanoparticles (SiO<sub>2</sub>‑NPs, food additive E551) have recently been proposed as a new tool. However, initial results are controversial, and the molecular mechanisms of SiO<sub>2</sub>‑NP-induced disease resistance are unknown. Here, we show that SiO<sub>2</sub>‑NPs, as well as soluble orthosilicic acid (Si(OH)<sub>4</sub>), can induce SAR in a dose-dependent manner, that involves the defence hormone salicylic acid. Nanoparticle uptake and action occurred exclusively through stomata (leaf pores facilitating gas exchange) and involved extracellular adsorption in leaf air spaces of the spongy mesophyll. In contrast to treatment with SiO<sub>2</sub>‑NPs, induction of SAR by Si(OH)<sub>4 </sub>was problematic, since high concentrations caused stress. We conclude that SiO<sub>2</sub>‑NPs have the potential to serve as an inexpensive, highly efficient, safe, and sustainable alternative for plant disease protection.</p>
Canterbury Museum (CMNZ) collection insect specimen-plant flower interactions
<p>This dataset compromises insect-plant flower interactions recorded from the entomology collections of Canterbury Museum, New Zealand (CMNZ). All invertebrate records were extracted from the Museum Vernon database. This included field collection metadata indicating if an insect and plant flower interaction had occurred. A large proportion of records are specimens collected as part of research by Richard Primack in the 1970s, from insects collected from flowering inflorences. Data was cleaned using OpenRefine v.3.6.2. Insect and plant names were reconciled using the GlobalNames extension in OpenRefine. Data was prepared for submission to the Global Biotic Interaction (GloBI) network.</p> <p>This data makes part of a paper submission to the Journal of Applied Entolomolgy Call for Papers on neglected insects pollinators. If accepted this publication will be linked to this dataset.</p> <p>This version included name updates for insect species, spreadsheet data used to produce summary statistics for the manuscript submission and the README file.</p>
AusTraits Plant Dictionary (APD)
<p>The Austraits Plant Dictionary (APD) offers detailed descriptions for more than 500 plant trait concepts.</p><p>APD includes trait focused on plant morphology, plant nutrient concentrations, plant physiology, plant life history, and plant fire response. The definitions will be useful to researchers from diverse disciplines, including plant functional ecology, plant taxonomy, and conservation biology. All trait concepts are supported by comprehensive metadata including trait descriptions, allowable trait values, allowable ranges, preferred units, keywords, references, and links to matches in a selection of trait databases. The traits describe here also fully support the AusTraits plant trait database, doi.org/10.5281/zenodo.3568417.</p><p>The APD can be viewed online at:</p><ul><li>https://w3id.org/APD</li><li>https://vocabs.ardc.edu.au/viewById/649 (Research Vocabularies Australia)</li></ul><p>The project GitHub repository is at:</p><ul><li>https://github.com/traitecoevo/APD</li></ul>
AusTraits: a curated plant trait database for the Australian flora
<p>AusTraits is a transformative database, containing measurements on the traits of Australia's plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 300 distinct sources, describing > 500 plant traits and > 34,000 taxa.</p> <p>To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project's GitHub repository, <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Further information on the project is available at the project website <a href="https://austraits.org">austraits.org</a> and in the associated publication (see below).</p> <p><strong>CONTRIBUTORS</strong></p> <p>The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University.</p> <p>We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment Land Water and Planning Victoria and the Royal Botanic Gardens Victoria.</p> <p>AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their "Transformative data collections" (https://doi.org/10.47486/TD044) and "Data Partnerships" (https://doi.org/10.47486/DP720, https://doi.org/10.47486/DP720A) programs; and grants from the Australian Research Council (FT160100113, DE170100208, FT100100910) and Macquarie University, The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS).</p> <p><strong>ACCESSING AND USE OF DATA</strong></p> <p>The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community.</p> <p>A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors:</p> <blockquote> <p>Falster, Gallagher et al (2021) <em>AusTraits, a curated plant trait database for the Australian flora</em>. Scientific Data 8: 254, <a href="https://doi.org/10.1038/s41597-021-01006-6">https://doi.org/10.1038/s41597-021-01006-6</a></p> </blockquote> <p>In addition, we encourage users you to cite the original data sources, wherever possible.</p> <p>Note that under the license data may be redistributed, provided the attribution is maintained.</p> <p>The downloads below provide the data in two formats:</p> <ul> <li>austraits-X.X.X.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python.</li> <li>austraits-X.X.X.rds: data as compressed R object. Suitable for users of R (see below).</li> <li> <div>austraits-X.X.X-flattened.rds: contains a flattened version of the dataset for direct loading in R; all data tables are joined into a wider format</div> </li> <li> <div>austraits-X.X.X-flattened.parquet: contains a flattened version of the dataset in parquet format; all data tables are joined into a wider format </div> </li> </ul> <p>For R users, access and manipulation of data is assisted with the <a href="http://github.com/traitecoevo/austraits">austraits R package</a>. The package can both download data and provides examples and functions for running queries.<br><br><strong>STRUCTURE OF AUSTRAITS</strong></p> <p>The compiled AusTraits database contains a series of relational tables and files. These elements include all the data, contextual information submitted with each contributed datasets, database schema, and trait definitions. The file dictionary.html provides the same information in textual format. Similar information is available at <a href="https://traitecoevo.github.io/traits.build-book/">https://traitecoevo.github.io/traits.build-book/</a>.</p> <p><strong>CONTRIBUTING</strong></p> <p>We envision AusTraits as an on-going collaborative community resource that:</p> <ol> <li>Increases our collective understanding the Australian flora;</li> <li>Facilitates accumulation and sharing of trait data;</li> <li>Builds a sense of community among contributors and users; and</li> <li>Aspires to fully transparent and reproducible research of the highest standard.</li> </ol> <p>As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Here are some of the ways you can contribute:</p> <p><strong>Reporting Errors</strong>: If you notice a possible error in AusTraits, please <a href="https://github.com/traitecoevo/austraits.build/issues">post an issue on GitHub</a>.</p> <p><strong>Refining documentation:</strong> We welcome additions and edits that make using the existing data or adding new data easier for the community.</p> <p><strong>Contributing new data</strong>: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p>
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
Occurrence Record Dataset from "Annotated checklist of the bees of Bonaire, with a focus on host plants"
<p>This is the occurrence dataset created for the publication "Annotated checklist of the bees of Bonaire, with a focus on host plants" (<a href="https://natuurtijdschriften.nl/pub/1026875" target="_blank" rel="noopener">https://natuurtijdschriften.nl/pub/1026875</a>).</p> <p>Observation and specimen data were assembled for this dataset, with the majority of records obtained during the Bonaire Estafette Expeditie (BEE). All citizen science records from Observation.org and iNaturalist.org up to December 2023 have been critically reviewed.<br>A project was created (<a href="https://www.inaturalist.org/projects/flower-visitors-and-pollinators-of-the-caribbean" target="_blank" rel="noopener">Flower visitors and pollinators of the Caribbean</a>) to improve standardized data collecting of plant-pollinator interactions and on <a href="https://observation.org/">observation.org</a> the standardized fields for interactions were used.<br>Records from passive trapping methods are not included. All bees were either observed or collected by hand or insect net. The majority of specimens will be accessible in the collection of Naturalis Biodiversity Center (RMNH), Leiden (the Netherlands). A synoptic collection is retained at the University of Tartu Zoological Collections in Tartu, Estonia (TUZ).</p> <p>The occurrence dataset (Version 1.4 and later) is:</p> <ul> <li>conform Darwin Core (DwC): <a href="https://dwc.tdwg.org/terms/">https://dwc.tdwg.org/terms</a></li> <li>in the data format CSV (tab delimited values) and UTF-8 encoded</li> </ul> <p> </p> <p><strong>DwC terms (Column labels) used in the dataset with their description:</strong></p> <table> <tbody> <tr> <td><strong>Column label</strong></td> <td><strong>Column description</strong></td> </tr> <tr> <td>occurrenceID</td> <td>Unique identifier or URI (GUID) for each record, mainly unique URLs generated by the web-based data holder.</td> </tr> <tr> <td>catalogNumber</td> <td>Unique code derived from URI in occurrenceID. Each specimen bears a label with this identifier and multimedia are tagged with this identifier.</td> </tr> <tr> <td>recordNumber</td> <td>Sample field ID used to manage data of preserved specimen occurrence records.</td> </tr> <tr> <td>otherCatalogNumbers</td> <td>Other unique identifiers used on specimen labels, but not derived from an URI.</td> </tr> <tr> <td>scientificName</td> <td>The scientific name of the lowest taxonomic rank to which the individual(s) was identified.</td> </tr> <tr> <td>scientificNameAuthorship</td> <td>The author name and year of publication in accordance with ICZN rules.</td> </tr> <tr> <td>verbatimIdentification</td> <td>The original identification, including qualifiers if needed.</td> </tr> <tr> <td>individualCount</td> <td>The number of individuals present at the time of the occurrence.</td> </tr> <tr> <td>sex</td> <td>The sex of the individual(s). The values female, male or unknown are used, if a mixed group is observed multiple values are listed.</td> </tr> <tr> <td>lifeStage</td> <td>The life stage of the individual(s).</td> </tr> <tr> <td>basisOfRecord</td> <td>The specific nature of the data record at the time of the identification (e.g. PreservedSpecimen).</td> </tr> <tr> <td>identifiedBy</td> <td>The name of the person who made the identification in the field or based on collected evidence (e.g. specimen or photo).</td> </tr> <tr> <td>identificationQualifier</td> <td>In case the identification could be given only to a species group 'cf.' is recorded.</td> </tr> <tr> <td>dateIdentified</td> <td>The year when the identification was made.</td> </tr> <tr> <td>previousIdentifications</td> <td>The scientific name originally given to the observed or collected individual(s).</td> </tr> <tr> <td>order</td> <td>The name of the order (e.g. Hymenoptera).</td> </tr> <tr> <td>family</td> <td>The name of the family (e.g. Apidae).</td> </tr> <tr> <td>genus</td> <td>The name of the genus (e.g. Apis).</td> </tr> <tr> <td>subgenus</td> <td>The name of the subgenus (e.g. Apis).</td> </tr> <tr> <td>specificEpithet</td> <td>The name of the species, epithet as given in dwc:scientificName.</td> </tr> <tr> <td>taxonRank</td> <td>The taxonomic rank of the most specific name in dwc:scientificName.</td> </tr> <tr> <td>eventDate</td> <td>The date-time when the event was observed and recorded. The event date uses the ISO 8601-1:2019 standard, with the following formatting being used: format YYYY-MM-DD, or YYYY if only the year is known. If time of capture is known, then format is YYYY-MM-DDTHH:MM, with HH:MM the local time.</td> </tr> <tr> <td>year</td> <td>The year in which the event was observed and recorded.</td> </tr> <tr> <td>month</td> <td>The month in which the event was observed and recorded.</td> </tr> <tr> <td>day</td> <td>The day in which the event was observed and recorded.</td> </tr> <tr> <td>eventTime</td> <td>The time or interval during which the event occurred.</td> </tr> <tr> <td>samplingProtocol</td> <td>The name or description of the collecting or recording method used.</td> </tr> <tr> <td>behavior</td> <td>A description of the behavior shown by the individual(s) recorded in this occurrence.</td> </tr> <tr> <td>decimalLatitude</td> <td>The geographic latitude in decimal degrees recorded by a GPS device (WGS84) when observing and recording the occurrence.</td> </tr> <tr> <td>decimalLongitude</td> <td>The geographic longitude in decimal degrees recorded by a GPS device (WGS84) when observing and recording the occurrence.</td> </tr> <tr> <td>geodeticDatum</td> <td>The ellipsoid, geodetic datum, or spatial reference system (SRS) upon which the geographic coordinates given in dwc:decimalLatitude and dwc:decimalLongitude is based.</td> </tr> <tr> <td>verbatimLocality</td> <td>The original textual description of the place.</td> </tr> <tr> <td>island</td> <td>The name of the island.</td> </tr> <tr> <td>countryCode</td> <td>The standard ISO 3166-1 alpha-2 country code for the country.</td> </tr> <tr> <td>coordinateUncertaintyInMeters</td> <td> <p>The horizontal distance (in meters) from the given dwc:decimalLatitude and dwc:decimalLongitude describing the smallest circle containing the actual location, usually the EPE (Estimated Position Error) from the GPS device. The EPE is here measured as the horizontal position error in meters.</p> </td> </tr> <tr> <td>recordedBy</td> <td>A person, group, or organization observing and recording the occurrence.</td> </tr> <tr> <td>associatedTaxa</td> <td>The type of association and the scientific name of the host taxon is recorded that is associated/has relationship with the taxon in dwc:scientificName. The association/relationship is recorded using the format as in the following example: "floral host":"Lantana sp."</td> </tr> <tr> <td>occurrenceRemarks</td> <td>Comments or notes about the dwc:Occurrence.</td> </tr> <tr> <td>associatedSequences</td> <td>A list (concatenated and separated) of identifiers (publication, global unique identifier, URI) of genetic sequence information.</td> </tr> <tr> <td>typeStatus</td> <td>A list (concatenated and separated) of nomenclatural types (type status, typified scientific name, publication) applied to the subject.</td> </tr> <tr> <td>collectionCode</td> <td>The name, acronym, coden, or initialism identifying the collection or data set from which the record was derived.</td> </tr> <tr> <td>identificationRemarks</td> <td>Comments or notes about the identification.</td> </tr> <tr> <td>identificationReferences</td> <td>A reference or list of references (publication, global unique identifier, URI) used for the identification.</td> </tr> <tr> <td>nameAccordingTo</td> <td>A reference to the checklist or publication that was followed to record the name in dwc:scientificName.</td> </tr> <tr> <td>samplingEffort</td> <td>The amount of effort, expressed in minutes or hours, to obtain and record the occurrences.</td> </tr> <tr> <td>occurrenceStatus</td> <td>A statement about the presence or absence of a taxon during the time of an event.</td> </tr> <tr> <td>disposition</td> <td>The current state of a specimen with respect to a collection.</td> </tr> <tr> <td>language</td> <td>The language of the record using ISO 639-1 codes, e.g. en</td> </tr> </tbody> </table>
Plant Atlas 2020 — British and Irish plant conservation statuses
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data behind the conservation status tables presented on the Conservation tabs of species’ pages of the Plant Atlas 2020 website (<a href="http://www.plantatlas2020.org"><span>www.plantatlas2020.org</span></a><span>).</span></p>
Steady-state operation dataset of an experimental Wet Cooling Tower pilot plant located at Plataforma Solar de Almería
<p>Repository that contains experimental data obtained from a Wet Cooling Tower (WCT) plant located at <a href="https://www.psa.es/es/index.php">Plataforma Solar de Almería</a>.</p> <p>For the article "Wet cooling tower performance prediction in CSP plants: A comparison between artificial neural networks and Poppe’s model", three experimental campaigns were used, quoting from the article:</p> <blockquote> <p>A total of 132 steady-state experimental points have been obtained thanks to the thorough experimentation conducted. These data cover a large variety of ambient conditions (different seasons, days and nights) and thermal loads (from 27 kW to 207 kW). </p> </blockquote> <p> </p> <p>See <code>README.md</code> for a more detailed description and instructions on how to use the data.</p> <h2><br>License</h2> <p><a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0. Attribution 4.0 International</a></p> <p>If the data is used as part of a scientific publication, please cite the source publication:</p> <div> <div>Serrano, Juan Miguel, Pedro Navarro, Javier Ruiz, Patricia Palenzuela, Manuel Lucas, and Lidia Roca. “Wet Cooling Tower Performance Prediction in CSP Plants: A Comparison between Artificial Neural Networks and Poppe’s Model.” <em>Energy</em> 303 (September 15, 2024): 131844. <a href="https://doi.org/10.1016/j.energy.2024.131844">https://doi.org/10.1016/j.energy.2024.131844</a>.</div> </div>
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.