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5,061 results for “access”
Gene expression ATLAS of Arabidopsis thaliana (accession Columbia) across its lifecycle
<p><strong>Abstract: </strong>Arabidopsis thaliana (accession- Columbia) is an important model plant. RNA-Seq based study of 36 gene expression libraries was carried out to explore transcriptional programs operating in different plant parts (seedling, rosette, root, inflorescence, flower, fruit silique, and seed) and developmental stages (2-leaf stage, 6-leaf stage, 12-leaf stage, senescence stage, dry mature and imbibed seed stage). For each tissue type and developmental stage, three individual plants were used as biological replicates.</p> <div><strong><span>Organism part: </span></strong><span>inflorescence, whole plant, seed, root, silique fruit, flower, rosette</span></div> <div> </div> <div><span><strong>Developmental stage:</strong> </span><span>LP.02 two leaves visible stage, IL.00 inflorescence just visible stage, fruit size 30 to 50% stage, LP.12 twelve leaves visible stage, root development stage, fruit size 70% to final stage, LP.06 six leaves visible stage, dry seed stage, flowering stage, seed imbibition stage, sporophyte senescent stage, inflorescence development stage</span></div> <div> </div> <div> <div><strong><span>Organism: </span></strong><span>Arabidopsis thaliana</span></div> <div> </div> <div><span><strong>Ecotype:</strong> </span><span>Col-0</span></div> <div> </div> <div><strong><span>Genotype: </span></strong><span>wild type genotype</span></div> <div> </div> <div><span><strong>Age:</strong> Samples are from </span><span>20-day, 49-day, 39-day, 15-day, 21-day, 9-day, 22-day, 55-day, 26-day, 45-day</span></div> <div> </div> <div><span><strong><span>Experimental Designs: </span></strong><span>growth chamber study<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://purl.obolibrary.org/obo/EO_0007269" target="_blank" rel="noopener"> EFO</a></span>, <span>development or differentiation design<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0001746" target="_blank" rel="noopener"> EFO</a></span>, <span>organism part comparison design<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0001750" target="_blank" rel="noopener"> EFO</a></span></span></div> <div> </div> <div><span>For more description of the data and sample types see the file <a href="../api/records/11133989/draft/files/PRJEB24664_Sample_descriptors.xlsx/content" target="_blank" rel="noopener noreferrer">PRJEB24664_Sample_descriptors.xlsx or visit </a> or visit <a href="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422/sdrf">https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422/sdrf</a></span></div> <div> </div> <div><span>Original data was submitted from </span></div> <div> <ul> <li><span>EMBL-EBI ArraExpress: <a href="https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422">https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6422</a></span></li> <li><span>NCBI SRA: <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJEB24664">https://www.ncbi.nlm.nih.gov/bioproject/PRJEB24664</a></span></li> </ul> <p><strong><span>Protocol description:</span></strong></p> <table> <tbody><tr> <th>Name</th> <th>Type</th> <th>Description</th> <th>Hardware</th> </tr> </tbody><tbody> <tr> <td>P-MTAB-71349</td> <td><span>growth protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0003789" target="_blank" rel="noopener"> EFO</a></span></td> <td>Seeds were planted in pots containing commercial potting mix with fertilizers. Pots were covered with clear perforated plastic wrap and kept at 4 degrees celsius for 3 days to break the dormancy. After 3 days plants were transferred to the Intellus Ultra growth chamber (Percival Scientific, IA, USA) which was set to temperature 22-23 degrees celsius, light intensity 120-150 micromol/m2sec under the cycle of 16h light and 8h dark. Soil was kept moist by gently spraying with water every 72 hours to maintain humidity to 50-60%. Sampling time point is given in days after germination.</td> <td> </td> </tr> <tr> <td>P-MTAB-71350</td> <td><span>nucleic acid extraction protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0002944" target="_blank" rel="noopener"> EFO</a></span></td> <td>Total RNA from frozen samples was extracted as a method described in Filichkin et al., 2010. Total RNA was used to isolate large RNA as per manufacturer's protocol for miRNeasy Mini kits (Qiagen Inc., USA), and RNase-free DNase (Life Technologies Inc., USA).</td> <td> </td> </tr> <tr> <td>P-MTAB-71351</td> <td><span>nucleic acid library construction protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0004184" target="_blank" rel="noopener"> EFO</a></span></td> <td>True-Seq kit (Illumina Inc.) was used to prepare RNA-seq libraries, according to the manufacturer’s protocol.</td> <td> </td> </tr> <tr> <td>P-MTAB-71352</td> <td><span>nucleic acid sequencing protocol<a title="" href="https://www.ebi.ac.uk/ols4/ontologies/efo/terms?iri=http://www.ebi.ac.uk/efo/EFO_0004170" target="_blank" rel="noopener"> EFO</a></span></td> <td>101bp paired-end sequencing of mRNA was performed by using the standard protocols on Illumina HiSeq 3000.</td> <td>Illumina HiSeq 3000</td> </tr> </tbody> </table> </div> </div>
Figures in Scientific Open Access Publications - Underlying Data
<p>This publication contains data for a statistical analysis of an OA article corpus. The underlying dataset consists of over 1 million open access articles from different publishers (Copernicus: 9592; Springer:78418; Hindawi: 147848; Frontiers: 57621; PMC (aggregator): 747839)</p>
Actinidia eriantha Accession EA01_01 low coverage genome assembly
Draft assembly scaffolds of kiwifruit <i>Actinidia eriantha</i> 'EA01_01'. This is a female vine derived from seed collected on Qi-Yuan Mt., Min-Qing, Fukien on 15/11/75 by Li Lai-Yung, Professor of Subtropical Pomology, University of Fukien, Peoples Republic of China and provided to the New Zealand DSIR in 1975
Datensatz zu: Fachgesellschaften und Open Access in Deutschland – eine Analyse zur Herausgabe von Zeitschriften
<p>In der Debatte um die Open-Access-Transformation wird auch die Rolle wissenschaftlicher Fachgesellschaften diskutiert. Bisher gab es keine systematische Erhebung zum Einfluss von Fachgesellschaften auf das Publikationssystem. Dieser unbefriedigende Forschungsstand führte dazu, dass das Potenzial dieses wichtigen Akteurs bei der Open-Access-Transformation bisher weitgehende unbeachtet blieb und möglichen Barrieren auf Seiten der Fachgesellschaften nicht adressiert wurden. Im Rahmen des Projekts „Options4OA“ wurden darum Publikations- und Open-Access-Aktivitäten deutscher Fachgesellschaften untersucht.</p> <p>Vorliegender Datensatz dokumentiert die dem Poster zugrundeliegenden Forschungsdaten in drei Datensätzen.</p> <p>Diese Datensätze beschreiben 182 Zeitschriften, die wissenschaftliche Fachgesellschaften, die in Deutschland angesiedelt sind, veröffentlichen. Neben allgemeinen Metadaten zu den Zeitschriften und den herausgebenden Fachgesellschaften finden sich in den Datensätzen Informationen zum Open-Access-Status der Zeitschriften und den Open-Access-Publikationsgebühren. Auch sind die Zeitschriften den Notationen der Fachsystematik der Deutschen Forschungsgemeinschaft (DFG) zugeordnet.</p> <p>Das Vorhaben wurde vom Bundesministerium für Bildung und Forschung (BMBF) im Rahmen des Projektes „Options4OA” gefördert (Förderkennzeichen: 16OA034).</p> <p>Weitere Informationen unter: <a href="https://os.helmholtz.de/projekte/options4oa/">https://os.helmholtz.de/projekte/options4oa/</a></p>
Open Access in developing countries – attitudes and experiences of researchers Dataset
<p>A survey was conducted of 507 researchers from the developing world and connected to INASP’s AuthorAID project to ascertain experiences and attitudes to Open Access publishing. This file is the raw output from the survey, with names and email addresses removed to preserve anonymity. </p>
Dataset: Open access potential and uptake in the context of Plan S - a partial gap analysis
<p>Dataset belonging to the report: <a href="https://doi.org/10.5281/zenodo.3543000">Open access potential and uptake in the context of Plan S - a partial gap analysis</a></p> <p> </p> <p>On the report: </p> <p>The analysis presented in the report, carried out by Utrecht University Library, aims to provide cOAlition S, an international group of research funding organizations, with initial quantitative and descriptive data on the availability and usage of various open access options in different fields and subdisciplines, and, as far as possible, their compliance with Plan S requirements.</p> <p>Plan S, launched in September 2018, aims to accelerate a transition to full and immediate Open Access. In the guidance to implementation, released in November 2018 and updated in May 2019, a gap analysis of Open Access journals/platforms was announced. Its goal was to inform Coalition S funders on the Open Access options per field and identify fields where there is a need to increase the share of Open Access journals/platforms. </p> <p>The report should be seen as a first step: an exploration in methodology as much as in results. Subsequent interpretation (e.g. on fields where funder investment/action is needed) and decisions on next steps (e.g. on more complete and longitudinal monitoring of Plan S-compliant venues) is intentionally left to cOAlition S and its members. </p> <p> </p> <p><em>This work was commissioned on behalf of cOAlition S by the Dutch Research Council (NWO), a member of cOAlition S. Bianca Kramer and Jeroen Bosman of Utrecht University Library were appointed to lead the project.</em></p>
Post-trial access practice in Malaria, Tuberculosis, and NTDs Clinical Trial studies in Sub-Saharan African countries, quantitative study
<p>This is the data set used <span>to evaluate post trial access plan and implementation practice on TB, Malaria and NTD clinical trial studies conducted in the sub-Saharan African countries. </span></p>
NWO and ZonMw Open Access Monitor 2023 dataset
<p>This is the full dataset of the NWO and ZonMw Open Access Monitor 2023 report and accompanying Appendix 'Estimating Costs of Open Access Publishing'. </p> <p>DOI of NWO and ZonMw Open Access Monitor report: <a href="https://doi.org/10.5281/zenodo.12685800">https://doi.org/10.5281/zenodo.12685800 </a><br>DOI to Appendix ‘Estimating costs of open access publishing’: <a href="https://doi.org/10.5281/zenodo.13885012">https://doi.org/10.5281/zenodo.13885012</a></p> <p> </p>
metapsyData: R Package to Access the Metapsy Databases
<p>The <code>metapsyData</code> package allows to access the Metapsy meta-analytic psychotherapy databases direct in your <code>R</code> environment. Once installed, simply run the <code>data</code> function (e.g. <code>data(DepPsychDB)</code>) to save the data locally. The documentation of the package is also hosted by <a href="https://rdrr.io/github/metapsy-project/metapsyData/">rdrr.io</a>.</p> <p>The interactive Metapsy web application (<a href="https://www.metapsy.org/">metapsy.org</a>) uses <code>metapsyData</code> in the background. You can open the Metapsy website in <code>R</code> by running <code>open_app()</code>.</p> <p>The raw data files can be accessed in the associated GitHub repository under <code>data</code>. To search for available databases in <code>metapsyData</code>, type in <code>metapsyData::</code> in your RStudio console.</p>
Dataset for Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator
<p>The dataset contains all gathered data from the experiment from Wednesday, March 16, 2022 11:58:41.929 AM UTC+0 (1647431921929) until Sunday, April 3, 2022 1:08:35.353 PM UTC+0 (1648991315353). The experiment was executed during physical presence within the Arctic Circle in Tromsø, Norway 69° 40' 53.117'' N 18° 58' 36.027'' E at 35m elevation above sea level. The dataset was gathered with a prototype [1] based on the CREDO android application [2]. The main research is to use Ultra High Energy Cosmic Rays (UHECR) as an entropy source for a Random Bit Generator (RBG). </p> <p>The associated publication will probably have the title "Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator"</p> <p>In order to reproduce the results the SQLite3 database "mrng_arctic_experiment_2022.db" is needed. To get the visual representations of the detections use "image_decoding_and_codesnippets.py" to generate the cleaned (414 detections / ~15MB) or the uncleaned (5567 detections / ~195 MB) dataset. The compressed folder "raw_data_incl_space_weather.7z" contains all raw data as gathered with the MRNG prototype, unprocessed, uncleaned, and unmerged. </p> <p> </p> <p>[1] https://github.com/StefanKutschera/mrng-prototype, visited on 27.03.2023</p> <p>[2] https://github.com/credo-science/credo-detector-android, visited on 27.03.2023</p>
Criteria for prioritizing selection of Mexican maize landrace accessions for conservation in situ or ex situ based on phylogenetic analysis
<p>Data for processed SSR markers in maize accessions. A database in Structured Query Language (SQL) is provided. Please see the text file "READMEmaizeSSR.pdf".</p>
Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland
<p>This repository contains data described in the article "Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland" (Heikinheimo et al. 2023) and used in the research article "Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions" (Viinikka et al. 2023). <br> <br> This repository contains data on green space quality and path distances to different types of green spaces. The path distances represent green space accessibility using active travel modes (walking, cycling). The path distances were calculated using the pedestrian street network across the seven largest urban regions in Finland. We derived the green space typology from the Urban Atlas Data that is available across functional urban areas in Europe and enhanced it with national data on water bodies, conservation areas and recreational facilities and routes from Finland. We extracted the walkable street network from OpenStreetMap and calculated shortest paths to different types of green spaces using open-source Python programming tools. Network distances were calculated up to ten kilometers from each green space edge and the distances were aggregated into a 250 m x 250 m statistical grid that is interoperable with various statistical data from Finland. The geospatial data files representing the different types of green spaces, network distances across the seven urban regions, as well as the processing and analysis scripts are shared in an open repository. These data offer actionable information about green space accessibility in Finnish city regions and support the integration of green space quality and active travel modes into further research and planning activities.</p> <p> </p> <p><strong>Data description article: </strong></p> <p>Heikinheimo, V., Tiitu, M., & Viinikka, A. (2023). Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland. <em>Data in Brief</em>, <em>50</em>, 109458. <a href="https://doi.org/10.1016/j.dib.2023.109458">https://doi.org/10.1016/j.dib.2023.109458</a></p> <p><strong>Related research article:</strong> </p> <p>Viinikka, A., Tiitu, M., Heikinheimo, V., Halonen, J. I., Nyberg, E., & Vierikko, K. (2023). Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions. <em>Applied Geography</em>, <em>157</em>, 102973. <a href="https://doi.org/10.1016/j.apgeog.2023.102973">https://doi.org/10.1016/j.apgeog.2023.102973</a></p>
Accessible Oceans: Auditory Display. Zooplankton Daily Vertical Migration Gets Eclipsed!
<p>The nine tracks make up an auditory display of the daily vertical migration of zooplankton off the coast of Oregon. The tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/92HixRBFY6YEyUgjzO1Y">listen online here</a>.</p> <p>The ocean data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the <a href="https://datalab.marine.rutgers.edu/ooi-nuggets/zooplankton-eclipse/">OOI Nugget</a> developed by Dr. Leslie Smith and Dr. Lori Garzio. Please note that there is no track 1B in this version. We removed track 1B in order to reduce redundancy in the display. </p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Accessible Oceans: Auditory Display. Longterm Axial Seamount Inflation Record
<p>The thirteen tracks make up an auditory display of the Longterm Axial Seamount Inflation Record. The tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/MViV0dJLZjJpFXEHN8EA">listen online here</a>.</p> <p>The display leverages data from NOAA PMEL that extend the record of the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) data back to 1997. This audio display focuses on the long-term pattern observed by bottom pressure recorders where the seafloor inflates (lifts), then an eruption event occurs, and the seafloor drops.</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Accessible Oceans: Auditory Display. 2015 Axial Seamount Eruption
<p>The ten tracks make up an auditory display of the 2015 Axial Seamount Eruption. The ten tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/qRKlQUoRe1n8TWDZOhTn">listen online here</a>.</p> <p>The data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the OOI Nugget developed by Dr. Leslie Smith. (<a href="https://datalab.marine.rutgers.edu/ooi-nuggets/axial-eruption/">https://datalab.marine.rutgers.edu/ooi-nuggets/axial-eruption/</a>)</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Accessible Oceans: Data Sonification Wrapper Earcons
<p>Original, earcon sounds to play before and after a data sonification. These auditory icons ensure there is a clear notification of the start and stop of the sonifications so that the learner knows when to start fully listening and then knows when the sonification is over.</p> <p>A semi-structured interview with two BLV teachers at the Perkins School for the Blind offered several ideas for helpful tactics in how to use sound to explain the principles of graphs. Sounds wrapping data sonifications was one best practice that emerged from the interview. We created original earcons for our project to serve this specific function.</p>
Notably Inaccessible – Data Driven Understanding of Data Science Notebook (In)Accessibility
<p><strong>Overview</strong></p> <p>This dataset artifact contains the intermediate datasets from pipeline executions necessary to reproduce the results of the paper.<br> We share this artifact in hopes of providing a starting point for other researchers to extend the analysis on notebooks, discover more about their accessibility, and offer solutions to make data science more accessible. The scripts needed to generate these datasets and analyse them are shared in the <a href="https://github.com/make4all/notebooka11y">GitHub repository</a> for this work.</p> <blockquote> <p><strong>The dataset contains large files of approximately 60 GB so please exercise caution when extracting the data from compressed files.</strong></p> </blockquote> <blockquote> <p><br> <strong>The dataset contains files which could take a significant amount of run time of the scripts to generate/reproduce.</strong></p> </blockquote> <p><strong>Dataset Contents</strong></p> <p>We briefly summarize the included files in our dataset. Please refer to the <a href="https://github.com/make4all/notebooka11y/blob/main/pipeline/README.md">documentation</a> for specific information about the structure of the data in these files, the scripts to generate them, and runtimes for various parts of our data processing pipeline.</p> <ol> <li><code>epoch_9_loss_0.04706_testAcc_0.96867_X_resnext101_docSeg.pth</code>: We share this model file, originally provided by <a href="https://github.com/jobinkv/DocFigure">Jobin <em>et al.</em></a>, to enable the classification of figures found in our dataset. Please place this into the `model/` <a href="https://github.com/make4all/notebooka11y/tree/main/model">directory</a>.</li> <li><code>model-results.csv</code>: This file contains results from the classification performed on the figures found in the notebooks in our dataset. <blockquote> <p>Performing this classification may take upto a day.</p> </blockquote> </li> <li> <p>a11y-scan-dataset.zip: This archive contains two files and results in datasets of approximately 60GB when extracted. Please ensure that you have sufficient disk space to uncompress this zip archive. The archive contains:</p> <ul> <li> <p><code>a11y/a11y-detailed-result.csv</code>: This dataset contains the accessibility scan results from the scans run on the 100k notebooks across themes.</p> <blockquote><strong>The detailed result file can be really large (> 60 GB) and can be time-consuming to construct.</strong></blockquote> </li> <li> <p><code>a11y/a11y-aggregate-scan.csv</code>: This file is an aggregate of the detailed result that contains the number of each type of error found in each notebook.</p> <blockquote><strong>This file is also shared outside the compressed directory.</strong></blockquote> </li> </ul> </li> <li> <p><code>errors-different-counts-a11y-analyze-errors-summary.csv</code>: This file contains the counts of errors that occur in notebooks across different themes.</p> </li> <li> <p><code>nb_processed_cell_html.csv</code>: This file contains metadata corresponding to each cell extracted from the html exports of our notebooks.</p> </li> <li> <p><code>nb_first_interactive_cell.csv</code>: This file contains the necessary metadata to compute the first interactive element, as defined in our paper, in each notebook.</p> </li> <li> <p><code>nb_processed.csv</code>: This file contains the necessary data after processing the notebooks extracting the number of images, imports, languages, and cell level information.</p> </li> <li> <p><code>processed_function_calls.csv</code>: This file contains the information about the notebooks, the various imports and function calls used within the notebooks.</p> </li> </ol>
The Brazilian Soil Spectral Library (VIS-NIR-SWIR-MIR) Database: Open Access
<p><strong>Abstract:</strong></p> <p>NEW VERSION V.002 (Some Lat Long Coordinates added).</p> <p>Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use.</p> <p><strong>BSSL Description and Usefulness</strong></p> <p>The Brazilian Soil Spectral Library (BSSL), available at <a href="https://bibliotecaespectral.wixsite.com/english">https://bibliotecaespectral.wixsite.com/english</a>, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data.</p> <p>The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative.</p> <p>The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability.</p> <p><strong>Coverage</strong></p> <p>The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a <em>.xlsx</em> format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (22 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range.</p> <p><strong>Accessing original data source</strong></p> <p>Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors:</p> <p>a) Refer to sheet two for name and code-based searches;</p> <p>b) Visit the website <a href="https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes">https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes</a> or locate the contributors' list by Brazilian state;</p> <p>c) Visit the website of the Brazilian Soil Spectral Service – Braspecs <a href="http://www.besbbr.com.br/">http://www.besbbr.com.br/</a>, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found.</p> <p><strong>Proceeding to data analysis</strong></p> <p>We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011).</p> <p>The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition.</p> <p>a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh;</p> <p>b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm;</p> <p>c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings;</p> <p>d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample;</p> <p>e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm;</p> <p>f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and</p> <p>g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°.</p> <p>The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface.</p> <p>We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°.</p> <p>Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard.</p> <p>For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory.</p> <p>The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm<sup>-1</sup> to 600 cm<sup>-1</sup>, with a resolution of 2 cm<sup>-1</sup>. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate.</p> <p> </p> <p><strong>Dataset characterization</strong></p> <p>The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows:</p> <p> </p> <p><strong>Sheet 1. BSSL_Soil_Attributes_Dataset</strong></p> <p>Column 1. <strong>ID_unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data;</p> <p>Column 3. <strong>Vis_NIR_SWIR_availability</strong>: availability of spectral data in visible, near-infrared, and shortwave infrared ranges;</p> <p>Column 4. <strong>MIR_availability</strong>: availability of spectral data in the middle infrared range;</p> <p>Column 5. <strong>Sampling</strong>: type of soil sampling;</p> <p>Column 6. <strong>Depth_cm</strong>: soil surface layer depth in centimeters; </p> <p>Column 7. <strong>Lat</strong>: Latitude; </p> <p>Column 8. <strong>Lat</strong>: Longitude; </p> <p>Column 9. <strong>Region</strong>: Brazilian geographical region of samples' source;</p> <p>Column 10. <strong>Municipality</strong>: Brazilian municipality of samples' source;</p> <p>Column 11. <strong>State</strong>: Brazilian Federation Unit of samples' source;</p> <p>Column 12. <strong>Vegetation</strong>: type of vegetal covering;</p> <p>Column 13. <strong>Biome</strong>: groupings of ecosystems that share similar characteristics and span different regions;</p> <p>Column 14. <strong>Geology</strong>: type of rock matter from local soil sampling;</p> <p>Column 15. <strong>Sand_gkg</strong>: Content of the soil fraction with grain size between 2 and 0.053 mm, expressed in grams per kilogram;</p> <p>Column 16. <strong>Clay_gkg</strong>: Content of soil fraction with grain size smaller than 0.002 mm, expressed in grams per kilogram;</p> <p>Column 17. <strong>SOM_gkg</strong>: Soil organic matter content, expressed in grams per kilogram;</p> <p>Column 18. <strong>pH_H2O</strong>: Soil hydrogen ion potential measured in water;</p> <p>Column 19. <strong>Ca_mmolkg</strong>: Exchangeable calcium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 20. <strong>Mg_mmolkg</strong>: Exchangeable magnesium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 21. <strong>Na_mmolkg</strong>: Exchangeable sodium content in the soil, expressed in millimoles per kilogram; and</p> <p>Column 22. <strong>CEC_Ph7_mmolkg</strong>: Cation exchange capacity of the soil at neutral pH, expressed in millimoles per kilogram.</p> <p> </p> <p><strong>Sheet 2. BSSL_Vis_NIR_SWIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 2153. <strong>350 – 2500</strong>: Reflectance in 2151 spectral bands in nanometers from visible and near-infrared to shortwave infrared range (350 – 2500 nm).</p> <p> </p> <p><strong>Sheet 3. BSSL_MIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique:</strong> Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner_code:</strong> Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 683. <strong>4000 – 600:</strong> Reflectance in 681 spectral bands in centimeters in the middle infrared range (4000 – 600 cm<sup>-1</sup>).</p> <p> </p> <p><strong>Sheet 4. Contributors</strong></p> <p>Column 1. <strong>Owner_code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data, which identifies and links it to datasets;</p> <p>Column 2. <strong>Owner</strong>: Name of the collaborator who agreed to the availability of the data;</p> <p>Column 3. <strong>E-mail</strong>: Contact the e-mail of the owner for more information or a data request;</p> <p>Column 4. <strong>Institution</strong>: Contributor's affiliation;</p> <p>Column 5. <strong>Samples NIR</strong>: Number of Vis-NIR-SWIR samples sent to the BSSL collection;</p> <p>Column 6. <strong>Samples MIR</strong>: Number of MIR samples sent to the BSSL collection;</p> <p> </p> <p><strong>Sheet 5. Metadata</strong></p> <p>Column 1. <strong>Material and Methods</strong>: Description of procedures performed for soil data analyses</p> <p> </p> <p><strong>Expectation and Social Relevance</strong></p> <p>These data can impact various disciplines such as soil surveying, soil attribute mapping, soil analysis, soil mineralogy, soil management zones, precision agriculture, development of new datasets and scientific groups, and others. We expect this contribution to be valuable and useful to the soil research community in promoting this non-renewable natural resource's conservation and sustainable use.</p>
Accessible Oceans: Auditory Display. Net flux of CO2 between Ocean and Atmosphere
<p>The seven tracks make up an auditory display of the net flux of carbon dioxide between the ocean and the atmosphere. The seven tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/RhkRBKbRTueE2F86b5QS">listen online here</a>.</p> <p>The data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the OOI Nugget developed by Dr. Leslie Smith. (<a href="https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/">https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/</a>)</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Accession numbers for radiocarbon and stable carbon isotopes of dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) in soil leachates from permafrost soils collected from the North Slope of Alaska in the summers of 2018 and 2022
Leachates of dissolved organic carbon (DOC) from permafrost soils were prepared from soils collected from the North Slope of Alaska in 2018 and 2022. Soil leachates were then either kept in the dark or exposed to light from LEDs at 305 nm (UV) and 405 nm (visible), and then inoculated with native microbial communities and incubated. At the start of the biological incubations, single replicates of the DOC after dark or light treatment and inoculation were assigned accession numbers and analyzed for 14C and 13C at the National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS) facility. At the end of the biological incubations, duplicates of the dissolved inorganic carbon (DIC) in those waters were assigned accession numbers and analyzed for 14C and 13C at the NOSAMS facility.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.