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22,710 results for “Plant”
Data from 'Tracability of Forest Reproductive Material with the quality label 'Plant van Hier': A DNA database with genetic profiles of native autochthonous tree and shrub species of Flanders, Belgium'
<h2>Background</h2> <p>Indigenous trees and shrubs play an important role in multifunctional forest management. They form a significant part of the biodiversity in our forests. Forest reproductive material (FRM) of autochthonous Flemish origin is sold under the quality label ‘Plant van Hier’, a certification mark of the Agency for Nature and Forests. To ensure the provenance of the seedlings, we developed a DNA-database of genetic profiles of potential parent trees, using species-specific genetic markers. This database enables the traceability of FRM of the ‘Plant van Hier’ label throughout the entire production chain; from seed harvesting and cultivation to planting by the end user.</p> <p>This database contains the genetic profiles of almost all possible parent trees present within 27 Flemish autochthonous seed orchards of eight ecologically important tree and shrub species: <em>Carpinus betulus</em>, <em>Corylus avellana</em>, <em>Frangula alnus</em>, <em>Populus tremula</em>, <em>Sorbus aucuparia</em>, <em>Tilia cordata</em>, <em>Tilia platyphyllos,</em> and <em>Ulmus laevis</em>. The profiles were established using microsatellite markers (11 to 24 markers per species). New genetic markers were developed for <em>Carpinus betulus</em> and <em>Ulmus laevis</em>. PCR products were run on an ABI 3500 Genetic Analyser (Thermo Fisher Scientific).</p> <h2>Files</h2> <p>The files will be updated when new genotypes are added to the seed orchards. The current data files contain data from genotypes collected in the period 2018-2023. </p> <h3>Species_genotypes</h3> <p>These files contain the genetic fingerprints of the parent trees of autochthonous Flemish seed orchards. Missing data is indicated as ‘MD’. For <em>Carpinus betulus</em>, an octoploid species, the allelic phenotype is given instead of the genotype as the number of times that an allele occurs on a specific locus is not known.</p> <p>The next metadata is additionally given:<br>- Species: the Latin name of the species<br>- Seed_orchard: the name of the seed orchard in which the genotypes are located<br>- Code_seed_orchard: the code of the seed orchard in which the genotypes are located as given in the Register of Flemish Forest Reproductive Material (‘Register bosbouwkundig uitgangsmateriaal’; inbo.be)<br>- Genotype: the fieldname given to the genotype<br>- Origin: the location where the genotype was collected in Flanders, Belgium. Genotypes were collected from natural stands which are assumed to have an autochthonous origin. When the specific location is unknown, the location ‘Flanders’ is given. <br>- Year_sampled: the year in which the genotypes were sampled in the respective seed orchard for genetic analysis.</p> <h3>Species_binsets</h3> <p>These files contain the binsets and allele names that are used to score the alleles of the genotypes in the programme Geneious Prime 2019.3.2 (<a href="https://www.geneious.com">https://www.geneious.com</a>). For <em>Tilia platyphyllos </em>and <em>Tilia cordata</em>, the same binsets were used.</p>
One-hectare fine-scale dataset of a fynbos plant community in the Cape Floristic Region
<p>Cape fynbos, which forms part of the Cape Floristic Region (CFR) of South Africa, a global biodiversity hotspot, is renowned for its high levels of plant species endemism and diversity. This extraordinary ecosystem, characterised by nutrient-poor soils and fire-adapted vegetation, is a treasure trove of endemic flora. However, this fragile system faces increasing threats from habitat loss, climate change, and invasive species. Pristine fynbos, naturally high in plant diversity and which forms a large part of the CFR, presents an ideal opportunity to gather fine-scale data on community assembly patterns. Most fynbos vegetation surveys use a plot size of about 100 m2, with no spatial structures within plots to demarcate individual subplots. Here, a groundbreaking dataset is presented that fully covers 1-hectare of pristine fynbos, systematically gridded into 50 × 50 subplots, each measuring 2 × 2 m, arranged evenly within a square-shaped survey site. Each plot was assigned a unique Y–X coordinate combination. For each plot, all plant species present were recorded, along with their total percentage covers and maximum height values. Total percentage covers were also recorded for bare soil, rock, and termite mounds. This dataset provides a valuable contribution to the field of fynbos ecology, as well as plant community ecology in general, and establishes a benchmark for future one-hectare surveys of similar fynbos vegetation types, delineating the fine-scale composition and structure of fynbos in the CFR. The dataset will be useful for a wide audience, including community and spatial ecologists, plant and environmental scientists, and biodiversity informaticians and statistical ecologists, offering ideal data for testing new metrics of diversity and compositional turnover. Data in Brief, Volume 59, April 2025, 111334: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2025.111334" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dib.2025.111334</span></span></a></p>
Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.
<p>Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.</p> <p>Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.</p>
2019-2020 woody plants pollen dataset from automatic particle detector in Šiauliai
<p>Dataset acquired from Rapid-E device by testing it with anemophilous woody plants pollen collected in Lithuania. Data is sorted by pollen type.</p> <p>The sampling methodology can be found in publication "Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps", Šaulienė Ingrida, et al. Atmospheric Measurement Techniques, 2019, 12.6: 3435-3452. <a href="https://doi.org/10.5194/amt-12-3435-2019">https://doi.org/10.5194/amt-12-3435-2019</a></p> <p>The authors would like to hear from you at realtime@sa.vu.lt if you use this dataset.</p>
2019-2020 herbaceous plants pollen dataset from automatic particle detector in Šiauliai
<p>Dataset acquired from Rapid-E device by testing it with anemophilous herbaceous plant pollen collected in Lithuania. Data is sorted by pollen type.</p> <p>The sampling methodology can be found in publication "Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps", Šaulienė Ingrida, et al. Atmospheric Measurement Techniques, 2019, 12.6: 3435-3452. <a href="https://doi.org/10.5194/amt-12-3435-2019">https://doi.org/10.5194/amt-12-3435-2019</a></p> <p>The authors would like to hear from you at realtime@sa.vu.lt if you use this dataset.</p>
Data for: Increasing plant group productivity through latent genetic variation for cooperation
<p>Historic yield advances in the major crops have to a large extent been achieved by selection for improved productivity of groups of plant individuals such as high-density stands. Research suggests that such improved group productivity depends on “cooperative” traits (e.g., erect leaves, short stems) that – while beneficial to the group – decrease individual fitness under competition. This poses a problem for some traditional breeding approaches, especially when selection occurs at the level of individuals, because “selfish” traits will be selected for and reduce yield in high-density monocultures. One approach, therefore, has been to select individuals based on ideotypes with traits expected to promote group productivity. However, this approach is limited to architectural and physiological traits whose effects on growth and competition are relatively easy to anticipate.</p> <p>Here, we developed a general and simple method for the discovery of alleles promoting cooperation in plant stands. Our method is based on the game-theoretical premise that alleles increasing cooperation benefit the monoculture group but are disadvantageous to the individual when facing non-cooperative neighbors. Testing the approach using the model plant <em>Arabidopsis thaliana</em><em>, </em>we found a major effect locus where the rarer allele was associated with increased cooperation and productivity in high-density stands. The allele likely affects a pleiotropic gene, since we find that it is also associated with reduced root competition but higher resistance against disease. Thus, even though cooperation is considered evolutionarily unstable except under special circumstances, conflicting selective forces acting on a pleiotropic gene might maintain latent genetic variation for cooperation in nature. Such variation, once identified in a crop, could rapidly be leveraged in modern breeding programs and provide efficient routes to increase yields.</p>
Data for nocturnal plant respiration is under strong non-temperature control
<p>Data set contains</p> <p>1- Annual output (2000-2018) of simulated plant respiration and net primary productivity from JULES with standard (Q10=2) and temperature dependent Q10 (TDQ<sub>10</sub>) with and without incorporation of nocturnal non-temperature control of respiration. Related readme file is included (Readme JULES output.txt).</p> <p>2-Python code (manuscript-code.zip) and data sets (Source Data.zip) to produce all manuscript figures including supplementary material. Readme file is included within mansucript-code.zip.</p> <p> </p>
Arctic specimens in the NHMO DNA bank Vascular plants collection 2022
<p>All Arctic specimens in the NHMO DNA bank Vascular plants collection as of August 2022. See Bjorå et al. 2023 "Collections of Arctic<br> plants, lichens and fungi in the Natural History Museum, University of Oslo, Norway" for further details.</p>
Distribution and habitat suitability maps for Central European steppe plants
<p>This dataset contains distribution maps for Central European steppe plants and coordinates of species occurrence points used by Divíšek et al. (2022) to calibrate habitat suitability models. These models were projected onto past climates and the resulting habitat suitability maps for 10 periods since the Last Glacial Maximum (LGM) are also included. These maps were further used as input data for simulations of species migration from climatically suitable areas in the LGM to identify those that may have served as a source for colonisation of the species' current ranges. For each species, we present maps of climatically suitable areas during the LGM and mid-Holocene (for the latter period, only areas accessible from the LGM are shown), as well as maps of the "source areas" from which the species may have colonised the regions occupied today.</p>
Data from: Effects of plastic fragments on plant performance are mediated by soil properties and drought
<p>In recent years, the effects of plastic contamination on soil and plants have received growing attention. Plastic can affect soil water content and thus may interact with the effects of drought on soil and plants. However, the effects of plastic on soil are highly context-dependent, and interactions with drought have been hardly tested. We conducted two greenhouse experiments to test the combined effects of plastic fragments (of varying size and concentration), water availability and soil texture, on soil water content and performance of the plant <em>Arabidopsis thaliana</em>. Plastic fragments had stronger negative effects on soil water content in low water availability, and the shape of this response (linear <em>vs.</em> unimodal) was mediated by soil texture. Conversely, increasing concentration of plastic had positive effects on plant growth. We suggest that plastic fragments introduce fracture points within soil aggregates. This increases number and size of soil pores favoring water loss but also facilitating root growth. Our results suggest complex interactive effects of plastic and drought, that may lead to a decoupling of plant and soil response. These processes should be taken into account in ecological studies and agricultural practices.</p>
Plant Atlas 2020 — British and Irish phenological data (flowering and leafing ranges)
<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 phenological diagrams (flowering and leafing) presented on the Plant Atlas 2020 website (<a href="http://www.plantatlas2020.org"><span>www.plantatlas2020.org</span></a><span>) and in the <em>Plant Atlas 2020</em> book. </span><span>Note that for non-flowering plants included in the atlas (e.g. ferns, horsetails etc.), the “flowering” fields in the phenology file included here are equivalent to the months when spore-bearing structures are visible.</span></p>
Plant Atlas 2020 — Plant native statuses for Britain, Ireland and the Channel Islands
<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 statements concerning species’ native statuses, for various geographical levels and areas, presented in the Plant Atlas 2020 website (<a href="http://www.plantatlas2020.org"><span>www.plantatlas2020.org</span></a><span>) and book (Stroh et al., 2023).</span></p>
Plant Atlas 2020 — British altitude-by-latitude diagrams
<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 British altitude-by-latitude diagrams presented in the Plant Atlas book (Stroh et al., 2023) and on the website (www.plantatlas2020.org).</p>
Plant Atlas 2020 — British and Irish species weekly apparency (including by-latitude breakdown for Britain), 2000–2019
<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 contains the species weekly “apparency” metrics presented within the Plant Atlas book and website, including a breakdown of species apparency by latitude for Britain. Apparency at the scale presented here (2 x 2 km spatially, smoothed over the period 2000–2019) combines aspects of recorder activity and detectability, with the latter being primarily influenced by phenology at this spatio-temporal scale.</p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km distribution trends for 1930–2019 (long-term) and 1987–2019 (short-term), including country-level breakdowns
<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 for the long- (1930–2019) and short- term (1987–2019) 10 x 10 km (“hectad”) distribution trends, presented in both the <em>Plant Atlas 2020</em> book (Stroh et al., 2023) and website (www.plantatlas2020.org).</p>
Raw Data of Pilot Plant Runs for CONSENS Project (Case Study 1)
<p>In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and <em>o</em>-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum. </p> <p>The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17.</p> <p>In the following the contents of the files are explained.</p> <p><strong>NIR_data_AQ15_raw.zip: </strong>Contains Bruker binary files (0-Files) of NIR spectrometer at AQ15 (Location is located subsequently to NMR spectrometer)</p> <p><strong>NMR_spectra_raw.zip: </strong>Contains Spinsolve files (Binarys of FID and Spectrum, DX-Files) of NMR spectrometer. The use of DX-files files is not recommended.</p> <p><strong>PCS_data_csv.zip</strong>: Contains csv-files of the process control system (PCS) including data of mass flow controlers (*_Bilanz.csv), filling level (*_FillLe.csv), pressures (*_pres), temperatures (*_Temp), position of valves (*_Valves). Relevant labels are: BP13 = LiHMDS storage tank, BP12 = aniline storage tank, BP12 = <em>o</em>-FNB storage tank, CM003 and CM004 = tubular reactors, T0041 and T005 = Temperature at reactor exits, P009 and P003 = Pressure at reactor inlets, P006 = Pressure at reactor exits.</p> <p><strong>housing_data_NMR.csv</strong>: Contrains data of NMR enclosure of all four days. Each columns from left to right represent timestamps, bypass pressure (bar), bypass temperature (°C), Gasalarm (logical), bypass actual flowrate (g min<sup>-1</sup>), bypass flowrate setpoint (g min<sup>-1</sup>), bypass density (kg m<sup>3</sup>)</p> <p><strong>matlab_variables_explanation.xlsx</strong>: Explanation of variables used in matlab structure "data_validation_run".</p> <p><strong>data_validation_run.mat</strong>: Matlab structure containing most relevant process data including NMR results, NIR results, housing data of NMR, and process control system data.</p>
MIRCA-BC-USMX: Irrigated and planted fractions over the continental United States and Mexico for years 1992, 2002, and 2012
<p>The MIRCA-BC-USMX project contains a spatially explicit mean annual cycle of monthly planted and irrigated fractions at 0.0625 degree (6 km) spatial resolution over the continental United States and Mexico for years 1992, 2002, and 2012.</p> <p>These fractions were generated by (1) reconciling the MIRCA2000 Global Monthly Irrigated and Rainfed Crop Areas dataset (Portmann et al., 2010) with the cropland and pasture classes of year 2001 of the harmonized NLCD_INEGI land cover dataset (Bohn and Vivoni, 2019b); (2) bias-correcting the irrigated and planted fractions to match state-by-state total irrigated and planted areas from government records in the United States (USDA, 2016) and Mexico (SADER, 2014; SAGARPA, 2016).</p> <p>These fractions have been added to land surface parameter files for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994) version 5.1 (Hamman et al., 2018), extended to include the irrigation module of Haddeland et al. (2006), available on <a href="https://github.com/tbohn/VIC/tree/feature/irrig.imperv.deep_esoil">GitHub</a>. The parameter files were taken from the MOD-LSP project, available on <a href="https://zenodo.org/record/2612560">Zenodo</a> (Bohn and Vivoni, 2019a). The VIC 5 image driver requires a "domain" file to accompany the parameter file. This domain file is also necessary for disaggregating the daily gridded meteorological forcings to hourly for input to VIC via the disaggregating tool <a href="https://github.com/UW-Hydro/MetSim">MetSim</a> (Bennett et al., 2018). We have provided a domain file compatible with the meteorological forcings of Livneh et al (2015) and the MIRCA-BC-USMX parameters, on <a href="https://zenodo.org/record/2564019">Zenodo</a> (Bohn et al., 2019a,b).</p> <p>Contents:</p> <ul> <li>Input Files <ul> <li>county_codes.csv - table mapping the numerical codes for counties with the county names used by the US Census Bureau and USDA. This was created by parsing this information from US Census tables from years 1990, 2000, and 2010 and USDA tables from years 1992, 2002, and 2012 and manually reconciling discrepancies across years. Thus the names may not match county names in the original files exactly from year to year, but rather represent my own naming convention. However these discrepancies were rare.</li> <li>mun_us.0.01_deg.asc.tgz and mun_mx.0.0.01_deg.asc.tgz - gzipped tar archives containing mun_us.0.01_deg.asc and mun_mx.0.01_deg.asc, which are ascii-format ESRI grid files created by rasterizing publicly available shapefiles of US and Mexican counties/municipios. These have 0.01 degree (1 km) spatial resolution and pixels have numerical values equal to the codes in county_codes.csv.</li> </ul> </li> <li>Output Files <ul> <li>fplant_firr_bc.$LCYEAR.nc, where $LCYEAR is one of ("s1992","2001", or "2011") - NetCDF-format files at 0.0625 degree (6 km) resolution containing 12 monthly maps each of bias-corrected "fplant" (planted area fraction) and "firr" (irrigated area fraction) for a specific historical year.The value of $LCYEAR indicates the snapshot of the NLCD_INEGI harmonized land cover classification with which fplant and firr were reconciled (so that these area fractions would not exceed the total agricultural/pastoral area given by NLCD_INEGI). Values of fplant and firr were bias corrected so that state-wide total areas matched government records from USDA (USDA, 2014) and SAGARPA (SADER, 2014; SAGARPA, 2016). For $LCYEAR = ("s1992", "2001", "2011"), the agricultural census year used in the bias correction was (1992, 2002, 2012).</li> <li>fplant_firr_bc.2011.mun_mx.nc - same as fplant_firr_bc.2011.nc, but bias-corrected at the municipio level in Mexico. County-level bias correction was not possible in the US due to lack of sufficient resolution USDA records. Similarly, municipio-level records were not available in Mexico prior to year 2003.</li> <li>params.USMX.NLCD_INEGI.$LCYEAR.$YEAR1_$YEAR2.with_irrig.nc - VIC 5 image driver-compliant input parameter files into which fplant and firr of the given $LCYEAR have been inserted. $YEAR1 and $YEAR2 indicate the first and last years of MODIS data used to estimate the annual cycle of monthly LAI, fcanopy, and albedo (independent of the values of fplant and firr).</li> <li>params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.mun_mx.nc - same as params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.nc but with fplant and firr bias-corrected at the municipio level in Mexico.</li> </ul> </li> </ul> <p>These parameters were created with scripts archived on <a href="https://github.com/tbohn/MIRCA-BC-USMX/releases/tag/v1.1">GitHub</a> (Bohn, 2019).</p>
Voltage and current data for IEC 62600-30 power quality monitoring from the Mutriku Wave Power Plant and Lir National Ocean Test Facility electrical laboratory
<p>This Technical Note describes the electrical data collected from the Mutriku Wave Power Plant (MWPP) and the Lir National Ocean Test Facility (NOTF) electrical laboratory at the MaREI Centre in the Environmental Research Institute, at University College Cork.</p> <p>In summary, the electrical data collect is for the purpose of analysing the power quality output of a Wave Energy Converter (WEC). The data includes voltage and current signals from the output of a WEC sampled at 15 kHz from the MWPP and a WEC emulator sampled at 20 kHz from the Lir NOTF electrical laboratory. There are 24 datasets from the MWPP taken at various sea state conditions, and there are 56 datasets from the Lir NOTF which are taken with at various sea state conditions, with different control laws, and grid connections.</p> <p>This data is published for purpose of power quality analysis and comparison for future tests. For OPERA, power quality analysis was performed as part of WP5 T5.2 and T5.5, and presented in depth in Deliverables D5.2 and D5.4.</p> <p>See accompanying technical note for more Information.</p>
Data from: The thermal limits of native plant species in California Coastal Sage Scrub
<p>Field and laboratory data for Goldsmith et al. (<em>In Review</em>) entitled, "The thermal limits of native plant species in California Coastal Sage Scrub." Four data files are included: </p> <p><strong><em>Goldsmithetal_PlantFunctionalTraitMetaData-18July24.xlsx </em></strong>-Provides metadata (header, description, units, measurement type, and expample) for each column of the file entitled "<em>Goldsmithetal_PlantFunctionalTraitData-18July24.csv." </em></p> <p><em><strong>Goldsmithetal_PlantFunctionalTraitData-18July24.csv </strong>- </em>Provides raw data for field and lab observations of plant functional traits as described in the methods section of this data record. </p> <p><em><strong>Goldsmithetal_PlantFvFmLabData-29March24.csv </strong>- </em>Provides raw data for experimental lab observations of leaf fv/fm following experimental heat treatments as described in the methods section of this data record. <em><br></em></p> <p><em><strong>Goldsmithetal_PlantFvFmLabMetaData-2Aug23.xlsx</strong> - </em>Provides metadata (header, description, units, measurement type, and expample) for each column of the file entitled "Goldsmithetal_PlantFvFmLabData-29March24.csv." </p> <p> </p> <p>Contact Greg Goldsmith (goldsmith at chapman dot edu) for additional information. </p>
Extensive Checklist to cGMP Inspections in Pharmaceutical Manufacturing Plants
<p><span>cGMP inspections are an essential part of ensuring that pharmaceutical companies produce high-quality, safe, and effective drugs. These inspections help maintain the integrity of the pharmaceutical supply chain and protect public health. Pharmaceutical companies must prioritize continuous cGMP compliance, prepare thoroughly for inspections, and respond promptly to any observations made by inspectors to remain in good standing with regulatory authorities.</span></p> <p><span>Here's a comprehensive cGMP inspection checklist for a pharmaceutical company, incorporating requirements from USFDA, EMA, WHO, UKMHRA, TGA, and ANVISA. This checklist includes a rating system to evaluate compliance with each requirement.</span></p>
ScienceDex guides
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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.