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1,429 results for “Inventories”
Glacier inventory of the upper Huasco valley, Norte Chico, Chile
<p>Shapefile of the glacier inventory of the Upper Huasco catchment, Chile, that was generated for the following research article:</p> <p>Nicholson, L. I., Marín, J., Lopez, D., Rabatel, A., Bown, F. and Rivera, A.: Glacier inventory of the upper Huasco valley, Norte Chico, Chile: glacier characteristics, glacier change and comparison with central Chile, Ann. Glaciol., 50(53), 111–118, doi:10.3189/172756410790595787, 2010.</p> <p>Glaciers were mapped on the basis of ASTER imagery from 2004, and classifed for the study as clean ice glaciers (1), debris covered glaciers (2) and rock glaciers (3). Additionally, classification following the GLIMS gudeilines (https://www.glims.org/.../GLIMS_Glacier-Classification-Manual_V1_2005-02-10.pdf ) was applied where possible to glacier features on the basis of additional ASTER imagery from 2002 and 2003, and older air photographs.</p>
Inventory of criteria for prioritization of digitisation of collections focussed on scientific and societal needs
<p>Anno 2017 the task of mobilizing data from biocollections ahead of us is still enormous (data of 90% of the biocollections still needs to be mobilized). It is imperative for stakeholders, individual keepers of natural science collections, the community at large, and even for funding agencies, not only to tackle this backlog as quickly as possible, but do it in the best possible order. To establish the best possible order for digitizing biocollections a demand driven framework is required based among others on criteria used to digitize biocollections.</p>
Raw metagenomic data from sweep net samples collected in 2016 as part of the Slikok Creek Watershed Biotic Inventory
<p>We set out to inventory vascular plants, bryophytes, lichens, birds, arthropods, and earthworms on a grid of sites in the portion of Slikok Creek watershed that is on the Kenai National Wildlife Refuge, Kenai Peninsula, Alaska. Occurrence data, images, and field data sheets from this project are available via an <a href="https://arctosdb.org/">Arctos</a> project page at <a href="http://arctos.database.museum/project/10002227">http://arctos.database.museum/project/10002227</a>.</p> <p>This dataset includes the raw FASTQ files from metagenomic processing and associated collection data. Of the 160 sweep net samples collected, 125 were selected for High Throughput Sequencing and shipped to RTL Genomics (<a href="http://rtlgenomics.com">http://rtlgenomics.com</a>) for extraction and sequencing steps. Sequencing was performed on an Illumina MiSeq platform and reads were processed using RTL Genomics’ standard methods with the mlCOIlintF/HCO2198 primer set of Leray et al. (2013), yielding a 313 bp region of the COI gene.</p> <p>Collection data are included in the file <code>ArctosData_43C6167EB1.csv</code> downloaded from Arctos. Extraction methods and sequencing methods provided by RTL Genomics are included in the files <code>Bowser 4869.pdf</code> and <code>Illumina MiSeq Two-Step Method 454 profile only.docx</code>. Primers used are provided in the file <code>Bowser_4869M.txt</code>. The archive <code>FASTQ.zip</code> contains all of the resulting FASTQ files.</p> <p>These sequence data have also been been published to GenBank's Sequence Read Archive in accessions <a href="http://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR10454582">SRR10454582</a>–<a href="http://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR10454706">SRR10454706</a> under BioProject <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA427721">PRJNA427721</a>.</p>
Digital Inventory of the Wooden Neighborhood of Raksila in Oulu, FInland
<p>The material concerns the investigation made on the case study of Raksila, wooden historic neighborhood in Oulu, Finland.</p> <p>For each building it has been elaborated a census card by fixing the information on a sketch book, the analysis has included several specific aspects: dates of construction, function, materials used for the facades, structure, colours of the facades, typologies of frames and analysis of the open areas. For the analysis of Raksila two different types of card have been designed: one type for the residential buildings and another one (more simplified) for the description of the services located around the neighbourhood.</p> <p>The recognition on field represent a fundamental part in the elaboration of the census card, the operator needs to make an appreciation of the main aspects by producing lists of different typologies connected to a same category, i.e. elaborate different lists of values. Categories and list of values represent the main elements in the formulation of the census card. When the analysis is finished the entire categories analysed with the related lists need to be transfer to a digital card by using FileMaker software. This programme gives the possibility to create detailed digital databases by including specific data sets. After the recognition phase and the elaboration of the card in digital form, the operator can start the acquisition of the information on field. Each building is identified with a specific code that can be elaborated by the operator or, as in this case, by using the existing codification system (for the analysis of the buildings in Raksila it has been used the same codification elaborated by the Municipality of Oulu in order to deliver to them an updated material easily consulted).</p> <p>When the acquisition of the information is finished the result is a digital archive which can be navigated and used for different types of purposes. Moreover, the software gives the possibility to recover all those cards, which have the same information or group of information. In the final part, they have been then put in mutual dialogue through the support of a 3D model realized on the base of the laser scanner.</p> <p>This step produced thematic maps and interesting consideration for the understanding of the place with its intrinsic dynamics.</p>
Inventory of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) in May 2023
<p>The dataset contains 49103 landslides, that were manually mapped by visual inspection of pre- and post-event satellite images in an area of 8981 km2. Such images are acquired by PlanetScope satellites (<a href="https://www.planet.com/">https://www.planet.com/</a>) and are provided under an academic license; 3-m resolution multiband tiles are used.</p> <p>Pre-event imagery refers to the Monthly Global Basemap products provided by Planet, the April 2023 Basemap was used. Post-event images were acquired between 22 May and beginning of June 2023. The cloud-free image closer to the event was used and multi-temporal frames were checked in selected areas (e.g., due to the presence of shadows or unclear images). Images are accessed through the Planet QGIS Plugin.</p> <p>This dataset supersedes version 1, since it represents its update; major changes include:</p> <ul> <li>mapping over a wider area (8981 vs 5764 km2);</li> <li>check on the landslides mapped in version 1 located on flat slopes (lower than 5°); removal of polygons associated with river erosion and not due to gravity movements</li> </ul> <p> </p> <p>NOTES ON VERSION 1</p> <p>landslides were manually mapped at a scale of 1:5.000 by a single operator in a time interval of 5 weeks following the rainfall event; the inventory (version 1.0) was completed on 28 June 2023. Please note that data did not undergo any kind of validation.</p> <p>Data are provided in shapefile format (coordinate system WGS84 UTM 32N) and in kml format.</p> <p>The main dataset is the “Emilia landslides” shp/kml file; the “area” shapefile refers to the investigated area; the “riverbank and agricultural fields” files include polygons that were mapped but refer either to river courses having high discharge in the post-event images, or to color changes probably due to farming activities or the evolution of agricultural fields. The “riverbank and agricultural fields” elements should not refer to slope movements, and usage of these data is not recommended, unless a validation is made.</p>
The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress: Data supporting the publication
This is the dataset on which the following publication is based: • Michel G, Baenziger J, Brodbeck J, Mader L, Kuehni CE, Roser K (2024). The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress. PLOS One. 19(7), e0305192. Doi: 10.1371/journal.pone.0305192, https://doi.org/10.1371/journal.pone.0305192 A description of the sample and the data collection procedure is available in the publication. The dataset contains the following variables: • Socio-demographic characteristics of the sample - Weights according to representative general population sample - Sex from Swiss Federal Statistical Office (SFSO) - Age at study (rounded to integer) - Age categories (10-year age groups) - Language questionnaire (German/Rumantsch, French, Italian) - Nationality from SFSO - Migration background - Education - Employment status • Original and prepared data on the Brief Symptom Inventory A detailed data dictionary is available in a separate excel file. Version • 1.0 (15 August 2024)
Shrub inventory data such as shrub identity, height and biomass in a 20x5m core plot at Mt. Kilimanjaro
<p>This dataset describes position and sizes of all shrubs above 130 cm high in all plots, also fruiting and flowering events in KiLi project. -999999 represents NA in numeric variables. </p> <p>The shrub inventory was carried out within a 5 × 20 m subplot in the centre of each plot. Within this subplot, the shrub layer was defined as consisting of all woody stems exceeding 1.3 m in height, but below 10 cm dbh and thus not included in the tree inventory. We measured dbh at 1.3 m with a diameter tape (Forestry Suppliers; for dbh's above 3 cm) or a caliper (for dbh's below 3 cm) and the height of each shrub with a hypsometer.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
Tree inventory data such as tree identity, position in the plot, height, architecture and biomass on Mt. Kilimanjaro
<p>This dataset describes position and sizes of all trees above 10 cm diameter at breast height in all plots, also fruiting and flowering events and if it is a canopy tree or not in KiLi project. -999999 represents NA in numeric variables. </p> <p>Within each plot, all trees wider than 10 cm diameter at breast height (dbh) were marked with aluminium tags and their dbh and height were measured. The dbh was measured with a diameter tape (Forestry Suppliers, USA) at 1.3 m for normally shaped trees and 20 cm below or above when branches or irregular shapes impeded measurement at that height. The 1.3 m height was measured from the highest ground level around the stem to standardize measurements taken on slopes. For trees which were strongly buttressed or too big to measure by hand, a laser dendrometer (Criterion RD 1000 with TruPulse 200/200, Centennial, USA) was used to measure the tree above the buttresses and at 1.3 m. Lianas above 10 cm in diameter were also marked and their dbh was measured. Tree height was measured using an ultra-sonic hypsometer (Vertex IV Hypsometer, Haglöf, Langsele, Sweden) or a laser rangefinder (TruPulse 200/200). The tree inventories were carried out between December 2010 and March 2013.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
The Metagenome-Assembled Genome Inventory for Children (MAGIC)
<div> <div>Existing microbiota databases are biased towards adult samples, hampering accurate profiling of the infant gut microbiome. Here, we generated a **M**etagenome-**A**ssembled **G**enome **I**nventory for **C**hildren (**MAGIC**) from a large collection of bulk and viral-like particle-enriched metagenomes from 0-7 years of age, encompassing `3,299` prokaryotic and `139,624` viral species-level genomes, `8.5%` and `63.9%` of which are unique to MAGIC. MAGIC improves early-life microbiome profiling, with the greatest improvement in read mapping observed in Africans. We then identified `54` candidate keystone species, including several *Bifidobacterium spp.* and four phages, forming guilds that fluctuated in abundance with time. Their abundances were reduced in preterm infants and were associated with childhood allergies. By analyzing the *B. longum* pangenome, we found evidence of phage-mediated evolution and quorum sensing-related ecological adaptation. Together, the MAGIC database recovers genomes that enable characterization of dynamics of early-life microbiomes, identification of candidate keystone species, and strain-level study of target species.</div> </div>
Gridded ammonia emission inventory in mainland China
<p>We produce and provide an improved ammonia emission inventory in mainland China in 2016. The emission inventory have been developed with 1/12 by 1/12 degree spatial resolution. The unit of the emission inventory is t/grid/year. </p>
A global inventory of solar photovoltaic generating units - dataset
<p>This is the data repository accompanying Kruitwagen, L., Story, K., Friedrich, J., Byers, L., Skillman, S., & Hepburn, C. (2021) A global inventory of photovoltaic solar generating units, <strong>Nature, </strong><em>forthcoming</em>. This repository contains the training, cross-validation, test, and predicted data set as described in the publication. The contents of this repository are briefly summarised here, see the publication for further details.</p> <p><strong>Repository contents:</strong></p> <p><em>trn_tiles.geojson: </em>18,570 rectangular areas-of-interest used for sampling training patch data.</p> <p><em>trn_polygons.geojson: </em>36,882 polygons obtained from OSM in 2017 used to label training patches.</p> <p><em>cv_tiles.geojson: </em>560 rectangular areas-of-interest used for sampling cross-validation data seeded from <a href="https://www.wri.org/research/global-database-power-plants">WRI GPPDB</a></p> <p><em>cv_polygons.geojson: </em>6,281 polygons corresponding to all PV solar generating units present in cv_tiles.geojson at the end of 2018.</p> <p><em>test_tiles.geojson: </em>122 rectangular regions-of-interest used for building the test set.</p> <p><em>test_polygons.geojson: </em>7,263 polygons corresponding to all utility-scale (>10kW) solar generating units present in test_tiles.geojson at the end of 2018.</p> <p><em>predicted_polygons.geojson: </em>68,661 polygons corresponding to predicted polygons in global deployment, capturing the status of deployed photovoltaic solar energy generating capacity at the end of 2018.</p>
Normalisation of Early Modern Science: Inventory of 17th- and 18th-Century Sources
<p>This dataset contains a corpus of early modern natural philosophy works that underlie the European Research Commission-funded Starting Grant “The Normalisation of Natural Philosophy: How Teaching Practices Shaped the Evolution of Early Modern Science,” (grant agreement No. 801653 NaturalPhilosophy), led by Dr. Andrea Sangiacomo at the Faculty of Philosophy at the University of Groningen.</p> <p> </p> <p>The methodology behind the retrieval, cleaning, and annotation of this repository is described in the paper:</p> <p> </p> <ul> <li>Sangiacomo, Andrea; Tanasescu, Raluca; Donker, Silvia; Hogenbirk, Hugo. 2021. “Mapping the Evolution of Early Modern Natural Philosophy: Corpus Collection and Authority Acknowledgement,” published in the <em>Annals of Science</em> (DOI: 10.1080/00033790.2021.1992502; permanent link: <a href="https://doi.org/10.1080/00033790.2021.1992502">https://doi.org/10.1080/00033790.2021.1992502</a> [forthcoming on the date of the upload]</li> </ul> <p> </p> <p>The difference between the total number of entries in this data set and the number of titles we ran our analyses on comes from listing multiple volumes of the same work as one single entry. The work on this corpus continues and further versions will be gradually uploaded, with supplementary information about publishers and publication places.</p> <p> </p> <p>The dictionaries from which we selected the data in worksheets 2-5 are the following:</p> <p> </p> <ul> <li>Wiep van Bunge, Henri Krop, Bart Leeuwenburgh, Paul Schuurman, Han van Ruler and Michiel Wielema, <em>Dictionary of Seventeenth- and Eighteenth-Century Dutch Philosophers</em> (London: Bloomsbury, 2003);</li> <li>John Yolton, Valdimir Price and John Stephens. <em>Dictionary of Eighteenth-Century British Philosophers</em> (London: Bloomsbury, 1999);</li> <li>Andrew Pyle. <em>Dictionary of Seventeenth-Century British Philosophers</em> (London: Bloomsbury, 2000);</li> <li>Luc Foisneau. <em>Dictionary of Seventeenth-Century French Philosophers</em> (London: Bloomsbury, 2008);</li> <li>Heiner F. Klemme and Manfred Kuehn. <em>Dictionary of Eighteenth-Century Philosophers</em> (London: Bloomsbury, 2011).</li> </ul> <p> </p> <p>University of Groningen Team:</p> <ul> <li>Andrea Sangiacomo (principal investigator)</li> <li>Raluca Tanasescu (postdoctoral researcher)</li> <li>Silvia Donker and Hugo Hogenbirk (PhD students)</li> <li>Cristian A. Marocico (scientific programmer, Center for Information Technology)</li> <li>Wim Breakman (bibliographer, University of Groningen Library)</li> </ul> <p> </p> <p>Special thanks to</p>
High-resolution oil and gas methane emission inventory for the Permian Basin
<p>This dataset consists of a high-resolution (0.01<sup>o</sup> × 0.01<sup>o</sup>) oil and gas methane emission inventory for the Permian Basin, developed at Environmental Defense Fund (<a href="http://www.edf.org">www.edf.org</a>). The Permian Basin in western Texas and southern New Mexico is the largest oil producing basin in the U.S., accounting for more than 40% of national oil production in 2021. It is also the nation's largest methane emitting basin, with recent measurement-based estimates of more than three million metric tons per year. Here, we develop an improved inventory of oil and gas methane emissions for the Permian Basin, based on recent facility-scale measurements and updated oil and gas activity data for the year 2021.</p> <p>Full details for the oil and gas methane emission inventory development and key results can be found in the following journal paper, which is under review at Earth System Science Data journal.</p> <p>Please cite the paper when using the methane inventory dataset:</p> <p>Omara, M., Gautam, R., O'Brien, M.A., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D.R., Chulakadaba, A., Miller, C.C., Franklin, J., Wofsy, S., and Hamburg, S.P. Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution. <em>In review</em>, Earth System Science Data journal (2023).</p> <p>Points of Contact at Environmental Defense Fund: Mark Omara (momara@edf.org) and Ritesh Gautam (rgautam@edf.org).</p>
Inventories of 17th-century Dutch painters and art dealers: the Künstler-Inventare database
<p>Abraham Bredius’ seminal work <em><a href="https://digi.ub.uni-heidelberg.de/diglit/bredius1915ga">Künstler-Inventare</a> </em>contains over 150 inventories of artists’ possessions in the Dutch Republic. However, this rich source has never been fully transformed into datasets, partially because Bredius was selective in his transcriptions of artists’ inventories with a focus on paintings. To fill this gap and to overcome Bredius’ biases, the <a href="https://www.virtualinteriorsproject.nl/">Virtual Interior project</a> assembled and transcribed 46 inventories of artists’ and art dealers’ homes in 17<sup>th</sup>-century Amsterdam. The selection of the inventories was based on three criteria: 1) The artists’ and art dealers’ inventories in my sample were drawn up during an artist’s lifetime or shortly after his death; 2) the inventories listed goods arranged by room; 3) the original documents of the inventories can still be traced in the Amsterdam City Archives. Forty-six inventories were selected and transcribed from the original sources or copied from existing publications. The majority of my samples can be found in the <a href="https://archief.amsterdam/inventarissen/details/5075/keywords/notarissen/withscans/0/start/0/limit/10/flimit/5">notarial archives</a> (Archive nr. 5075) and the rest in the <a href="https://archief.amsterdam/inventarissen/details/5072/withscans/0/findingaid/5072/start/0/limit/10/flimit/5">Archive of the Chamber of Insolvency</a> (<em>Desolate Boedel kamer, </em>Archive nr. 5072), both preserved in the Amsterdam City Archives<em>. </em>Starting with the published inventories, Bart Reuvekamp from our project team traced them back to the original sources in the archive, transcribed centuries-old handwritten pages, and compiled the listed objects in digital form. In this way, our database is able to encompass all belongings present in painters’ workshops, filling in the blanks that Bredius and other authors neglected or chose to leave out – the missing information that once hindered our comprehension of how artists organized their studios.</p> <p>The dataset is organized as follows:</p> <ul> <li><strong>1_Intro_and_data_explanation.xlsx</strong> introduces the dataset and explains the columns in the following files</li> <li><strong>2_Inventory_list.csv</strong> hosts the details of the inventories in this dataset</li> <li><strong>3_Inventory_items.csv</strong> contains the full transcriptions and categorical data of the objects in the inventories</li> <li><strong>4_Inventory_relationships.csv</strong> captures all the people mentioned in the inventories in the pre- or postscript and/or as debtors or creditors</li> <li><strong>5_Inventory_category_reference.csv</strong> provides a reference table for the ‘object_type’ and ‘object_category’ columns in 3_Inventory_items.csv</li> <li><strong>6_Inventory_subjectmatter_reference.csv</strong> offers a reference table for the ‘painting_subject’ and ‘painting_genre’ columns in 3_Inventory_items.csv</li> </ul> <p><strong>NB: This <em>Künstler-Inventare database </em>is a provisional version. The correction and final data process has not been fully finished in Version 1. The transcriptions might contain errors and need to be treated with caution. </strong></p> <p><strong> </strong></p>
An Ice Age JWST inventory of dense molecular cloud ices
<p>This dataset is the first observational data release for the JWST Early Release Science Ice Age program (#1309). More information about this program can be found at our team website (<a href="http://jwst-iceage.org/">http://jwst-iceage.org/</a>) and the STScI website (<a href="https://www.stsci.edu/jwst/science-execution/approved-programs/dd-ers/program-1309">https://www.stsci.edu/jwst/science-execution/approved-programs/dd-ers/program-1309</a>). The data is analyzed in the article by McClure et al. (2023), to be published on January 24th, 2023 by Nature Astronomy.</p> <p>The dataset consists of 5 files, each containing a spectrum of one of two background stars analyzed in that publication. The spectra are given as wavelength in microns (column 1), flux in milli-Janskys (column 2), and uncertainty in the flux (column 3). Some basic information about the JWST pipeline version is given in the header of each text file, but users should refer to the Methods section of McClure et al. (2023) for the full description of how the data were processed and extracted, as it extends beyond the basic pipelines. Information about how the data were observed is given in the APT file, accessible through a query in STScI's APT application (search by PID 1309) or here at STScI (<a href="https://www.stsci.edu/jwst/science-execution/program-information.html?id=1309">https://www.stsci.edu/jwst/science-execution/program-information.html?id=1309</a>).</p> <p>Three of the files correspond to the background star NIR38. These represent spectra of this star taken separately by JWST with the NIRCam WFSS, NIRSpec FS, and MIRI LRS FS instrument modes on JWST. The other two files correspond to the background star J110621 and are spectra taken separately by JWST using the NIRSpec FS and MIRI LRS FS instrument modes.</p> <p>It is necessary to cite the McClure et al. (2023) Nature Astronomy publication when making use of these data, to fully describe the data processing, as well as this Zenodo DOI.</p>
ASTER 8 Aug 2014 Proglacial Lake Inventory
<p>An Inventory (shapefiles) of proglacial Lakes in Arctic Sweden that has been manually delineated from ASTER satellite imagery taken on 8/8/2014</p>
Migration Data Inventory Records
<p>This inventory includes metadata on various quantitative sources of information on migration that can be used for modelling purposes.</p> <p>Documentation: http://www.quantmig.eu/res/QuantMig_documentation_website.pdf</p> <p>Instructional video: https://www.youtube.com/watch?v=Lqm36f0lXho</p> <p>How to cite: <strong>Soto Nishimura A</strong> and <strong>Mooyaart J</strong> (2021) Migration Data Inventory Records: Data Inventory. Online resource, available at <a href="http://www.quantmig.eu/data_inventory/">http://www.quantmig.eu/data_inventory/.</a></p>
Life cycle inventories for on-road vehicles
<p>Life cycle inventory datasets for current on-road vehicles in Switzerland and Europe. These datasets can be consumed by brightway2 (https://brightway.dev/) and Simapro 9.x (https://simapro.com/), and link to either ecoinvent 3.6 (cut-off), ecoinvent 3.7.1 (cut-off), ecoinvent 3.8 (cut-off), ecoinvent 3.9 (cut-off) or UVEK:2018.</p> <p>These datasets can be cited as:</p> <p>Sacchi, R., Bauer, C. (2023) Life cycle inventories for on-road vehicles. Paul Scherrer Institut, Villigen, Switzerland.</p>
Fire INventory from NCAR (FINN) v2.5(MODIS), MOZART VOC speciation
<p>The Fire INventory from NCAR (FINN) provides daily global fire emissions at high spatial resolution. The FINN model uses satellite detection of active fires (thermal anomalies) and the land cover type to determine the emission estimates. These emission estimates are based on MODIS active fire detection. Other versions of FINNv2.5 use MODIS+VIIRS fire detections. Please find additional VOC speciations and gridded emissions files at: https://doi.org//10.5065/XNPA-AF09</p>
Global Earth Mineral Inventory Data Dump
<p>Contains data dumps of various versions of the Global Earth Mineral Inventory (GEMI) - One of the Deep carbon observatory data legacies. </p>
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.