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715 results for “address”

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zenodo52/100

Raw data for the submitted manuscript entitled "Prospective Scenarios for Addressing the Agricultural Plastic Waste Issue: Results of a Territorial Analysis"

<p><span>Agricultural activities have been positively affected by the use of plastic products, but this has resulted in the production of plastic waste and led to an increase in environmental pollution.&nbsp; </span><span>This file concerns plastic waste indices to different crop types and plastic products allowed quantifying and georeferencing actual plastic waste production. Two improved scenarios were considered, the first consisted of extending the lifespan of some plastics, and the second entailed the introduction of some biodegradable alternatives. </span></p>

opencc-by-4.0Apr 2024View details →
zenodo48/100

Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"

<p>Supplementary Materials for &quot;Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis&quot;</p> <p>This package contains an anonymized packets of 802.11 probe requests captured in in December 2021 at Universitat Jaume I . The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>

opencc-by-4.0Apr 2023View details →
edi48/100

Analysis of the planning process for a new park in Minneapolis, Minnesota, at the Upper Harbor Terminal site with planners and community members focusing on redevelopment that addresses green gentrification concerns, 2019 to 2021

This dataset is from a study focuses on the Minneapolis Park and Recreation Board’s planning process for a new park at the Upper Harbor Terminal (UHT) site - a defunct barge-to-rail terminal on the Mississippi River being redeveloped with a mix of housing, commercial uses, and park space (see "UHT_location_map in Other Entities). While the redevelopment affords an opportunity to remediate pollution and provide new greenspace, a high portion of nearby residents are low-income and housing-cost burdened, raising concerns among community members about gentrification. As a counter to green gentrification, the concept of “Just Green Enough” (JGE) became a common theme around the UHT development process. JGE aims to center community-centered greening efforts with broader community development goals in mind (i.e. living-wage jobs and affordable housing). Amidst growing community pressure, the Minneapolis Park and Recreation Board (MPRB) appointed a Community Advisory Committee (CAC). The CAC was tasked with meeting monthly for in-depth deliberations (facilitated by planning staff and external consultants) with the goal of making final park design and programming recommendations, which planning staff would present to the MPRB Board of Commissioners. The Upper Harbor Terminal CAC consisted of 16 members, mostly residents of North Minneapolis and Northeast, and many with a background in nonprofit, environmental, or community organizing work. Meetings began in July 2019 and lasted nearly two years until in May 2021. This dataset includes the qualitative codebooks for the CAC meetings, accompanied by the meeting minutes and other documents related to the UHT planning process. The transcripts of semi-structured interviews with stakeholders in the UHT planning process were also analyzed and coded, but specific quotations are omitted from this dataset to protect the privacy the participants.

openCC (other)Nov 2024View details →
zenodo44/100

Addressable Nanoantennas with Cleared Hotspots for Single-Molecule Detection on a Portable Smartphone Microscope

<p>The advent of highly sensitive photodetectors and the development of photostabilization strategies made detecting the fluorescence of single molecules a routine task in many labs around the world. However, to this day, this process requires cost-intensive optical instruments due to the truly nanoscopic signal of a single emitter. Simplifying single-molecule detection would enable many exciting applications, <em>e.g.</em> in point-of-care diagnostic settings, where costly equipment would be prohibitive. Here, we introduce addressable NanoAntennas with Cleared HOtSpots (NACHOS) that are scaffolded by DNA origami nanostructures and can be specifically tailored for the incorporation of bioassays. Single emitters placed in the NACHOS emit up to 461-fold (average of 89&plusmn;7-fold) brighter enabling their detection with a customary smartphone camera and an 8-US-dollar objective lens. To prove the applicability of our system, we built a portable, battery-powered smartphone microscope and successfully carried out an exemplary single-molecule detection assay for DNA specific to antibiotic-resistant <em>Klebsiella pneumonia</em> &bdquo;on the road &ldquo;.&nbsp;Here we demonstrate the raw data on which our findings based on.</p>

opencc-by-4.0Apr 2020View details →
zenodo44/100

A dissymmetric [Gd2] coordination molecular dimer hosting six addressable spin qubits. Open data sets

<p>Includes data relevant for publication with DOI&nbsp;<a href="https://doi.org/10.1038/s42004-020-00422-w">10.1038/s42004-020-00422-w</a>&nbsp;plus a table with information about how the data were obtained and processed.</p>

opencc-by-4.0Dec 2019View details →
zenodo44/100

The evolution and future of research on Nature-based Solutions to address societal challenges

<p>This dataset comprises the bibliographic text files used to analyse the Nature-based Solutions research landscape as presented in:</p> <ul> <li>Dunlop, T., Khojasteh, D., Cohen-Shacham, E., Glamore, W., Haghani, M., van den Bosch, M., Rizzi, D., Greve, P., Felder, S. The Evolution and Future of Research on Nature-based Solutions to Address Societal Challenges. <em>Communications Earth &amp; Environment</em>. 2024.</li> </ul> <p>Excel spreadsheets containing data for the Global Water Security Index (Gain et al., 2016) presented in Figure 2 and the data required to reproduce Figures 1 and 2 in the paper above are also shared.</p>

opencc-by-4.0Dec 2023View details →
zenodo44/100

Raw data and code for "Addressing gaps in small-scale fisheries: a low-cost tracking system"

<p>This repository contains the raw data and code to reproduce results and plots presented in: &quot;Addressing gaps in small-scale fisheries: a low-cost tracking system&quot;. The release contains:</p> <ul> <li>ssf_function.R. The R function developed for the analysis</li> <li>ssf_workflow.R.&nbsp; The R scripts to reproduce the analysis and the results.</li> <li>gps_data.csv. Raw data used in the paper</li> </ul>

opencc-by-4.0Dec 2021View details →
zenodo44/100

Keyword counts from US Presidential State of the Union Addresses and Presidential Budget Messages

<p>Keyword counts from US Presidential State of the Union Addresses and Presidential Budget Messages. This was done using the Python scripts provided under&nbsp;<a href="https://github.com/JeremySilver/KeywordCountsPresidentialMessages">https://github.com/JeremySilver/KeywordCountsPresidentialMessages</a>. The raw text data is from&nbsp;<a href="http://www.presidency.ucsb.edu/">The American Presidency Project</a>&nbsp;(<a href="http://www.ucsb.edu/">UCSB</a>), with some&nbsp;Presidential Budget Messages being extracted from US Federal Budget documents available through&nbsp;<a href="https://fraser.stlouisfed.org/">FRASER</a>&nbsp;(a digital library of U.S. economic, financial, and banking history) or, for the more recent documents the website of the&nbsp;<a href="https://www.whitehouse.gov/">White House</a>.</p> <p>The data headings are:</p> <ul> <li>pid: in most cases, this is the index for the text document as archived on&nbsp;<a href="http://www.presidency.ucsb.edu/">The American Presidency Project</a>&nbsp;website. In some cases, this was the filename of a plain-text file read directly.</li> <li>year: Year that the message was delivered.</li> <li>date: Date that the message was delivered.</li> <li>name: Name of the US President delivering the message.</li> <li>count_of_all_words: Count of all words in the document.</li> <li>count_of_keywords: Count of all keywords encountered in that document.</li> <li>Keyword specific columns - three per keyword. For example, for the &#39;energy&#39; keyword, the&nbsp;&#39;energy&#39; column gives the number of times the &#39;energy&#39; keyword was counted in the message, &#39;energy_pct_of_keywords&#39; gives this count as a percentage of all keywords, and &#39;energy_pct_of_all_words&#39; gives this count as a percentage of all words</li> </ul> <p>Below is the list of keywords that match when the search is applied to a dictionary file containing over 99,000 US English words.</p> <ul> <li>energy: &#39;energy&#39;</li> <li>tax: &#39;nontaxable&#39;, &#39;overtax&#39;, &#39;overtaxed&#39;, &#39;overtaxes&#39;, &#39;overtaxing&#39;, &#39;surtax&#39;, &#39;surtaxed&#39;, &#39;surtaxes&#39;, &#39;surtaxing&#39;, &#39;surtaxs&#39;, &#39;tax&#39;, &#39;taxable&#39;, &#39;taxation&#39;, &#39;taxations&#39;, &#39;taxed&#39;, &#39;taxes&#39;, &#39;taxing&#39;, &#39;taxpayer&#39;, &#39;taxpayers&#39;, &#39;taxs&#39;</li> <li>defense: &#39;defend&#39;, &#39;defense&#39;</li> <li>education: &#39;education&#39;</li> <li>employment: &#39;employ&#39;, &#39;employable&#39;, &#39;employe&#39;, &#39;employed&#39;, &#39;employee&#39;, &#39;employees&#39;, &#39;employer&#39;, &#39;employers&#39;, &#39;employes&#39;, &#39;employing&#39;, &#39;employment&#39;, &#39;employments&#39;, &#39;employs&#39;, &#39;underemployed&#39;, &#39;unemployable&#39;, &#39;unemployed&#39;, &#39;unemployeds&#39;, &#39;unemployment&#39;, &#39;unemployments&#39;</li> <li>research: &#39;research&#39;, &#39;researched&#39;, &#39;researcher&#39;, &#39;researchers&#39;, &#39;researches&#39;, &#39;researching&#39;, &#39;researchs&#39;</li> <li>shooting: &#39;shooting&#39;</li> <li>space: &#39;space&#39;</li> <li>nuclear: &#39;nuclear&#39;</li> <li>natural&nbsp;resources: &#39;natural&nbsp;resources&#39;</li> <li>racism: &#39;racism&#39;, &#39;civil rights&#39;</li> <li>crime: &#39;crime&#39;, &#39;crimes&#39;, &#39;criminal&#39;, &#39;criminally&#39;, &#39;criminals&#39;, &#39;decriminalization&#39;, &#39;decriminalizations&#39;, &#39;decriminalize&#39;, &#39;decriminalized&#39;, &#39;decriminalizes&#39;, &#39;decriminalizing&#39;</li> <li>environment: &#39;environment&#39;, &#39;environmental&#39;, &#39;environmentalism&#39;, &#39;environmentalisms&#39;, &#39;environmentalist&#39;, &#39;environmentalists&#39;, &#39;environmentally&#39;, &#39;environments&#39;</li> <li>religion: &#39;faith&#39;, &#39;god&#39;, &#39;prayer&#39;, &#39;religion&#39;</li> <li>health: &#39;health&#39;, &#39;healthful&#39;, &#39;healthfully&#39;, &#39;healthfulness&#39;, &#39;healthfulnesss&#39;, &#39;healthier&#39;, &#39;healthiest&#39;, &#39;healthily&#39;, &#39;healthiness&#39;, &#39;healthinesss&#39;, &#39;healths&#39;, &#39;healthy&#39;, &#39;unhealthful&#39;, &#39;unhealthier&#39;, &#39;unhealthiest&#39;, &#39;unhealthy&#39;</li> <li>terror: &#39;terror&#39;, &#39;terrorism&#39;, &#39;terrorisms&#39;, &#39;terrorist&#39;, &#39;terrorists&#39;, &#39;terrorize&#39;, &#39;terrorized&#39;, &#39;terrorizes&#39;, &#39;terrorizing&#39;, &#39;terrors&#39;</li> <li>war: &#39;war&#39;, &#39;warrior&#39;, &#39;warriors&#39;, &#39;wars&#39;</li> <li>economy: &#39;economic&#39;, &#39;economical&#39;, &#39;economically&#39;, &#39;economics&#39;, &#39;economicss&#39;, &#39;economy&#39;, &#39;economys&#39;, &#39;microeconomics&#39;, &#39;microeconomicss&#39;, &#39;socioeconomic&#39;, &#39;uneconomic&#39;, &#39;uneconomical&#39;</li> <li>jobs: &#39;jobs&#39;</li> <li>business: &#39;agribusiness&#39;, &#39;agribusinesses&#39;, &#39;agribusinesss&#39;, &#39;business&#39;, &#39;businesses&#39;, &#39;businesslike&#39;, &#39;businessman&#39;, &#39;businessmans&#39;, &#39;businessmen&#39;, &#39;businesss&#39;, &#39;businesswoman&#39;, &#39;businesswomans&#39;, &#39;businesswomen&#39;</li> <li>drugs: &#39;drugs&#39;, &#39;narcotics&#39;</li> <li>inflation: &#39;inflation&#39;</li> <li>climate: &#39;climate&#39;</li> <li>science: &#39;science&#39;, &#39;sciences&#39;, &#39;scientific&#39;, &#39;scientifically&#39;, &#39;scientist&#39;, &#39;scientists&#39;</li> <li>gun: &#39;gun&#39;, &#39;gunfire&#39;, &#39;gunman&#39;, &#39;guns&#39;, &#39;handgun&#39;, &#39;rifle&#39;, &#39;shotgun&#39;</li> <li>tech: &#39;biotechnology&#39;, &#39;biotechnologys&#39;, &#39;technical&#39;, &#39;technological&#39;, &#39;technologically&#39;, &#39;technologies&#39;, &#39;technologist&#39;, &#39;technologists&#39;, &#39;technology&#39;, &#39;technologys&#39;</li> <li>military: &#39;military&#39;</li> <li>security: &#39;security&#39;</li> <li>housing: &#39;housing&#39;</li> <li>pollution: &#39;pollution&#39;</li> </ul> <p>The dictionary file used is a standard file among Linux systems, and the version used was provided with version 7.1-1 of the Ubuntu &#39;wamerican&#39; package.&nbsp;Two extra phrases, which do not appear in the dictionary file, are added to the list: &#39;civil rights&#39; (under the &#39;racism&#39; keyword) and &#39;natural&nbsp;resources&#39; (under the &#39;natural&nbsp;resources&#39; theme).</p>

opencc-by-4.0Jun 2019View details →
zenodo44/100

GRTSmh_diffres: the raster data source GRTSmaster_habitats converted to 9 hierarchical cell address levels at the corresponding lower resolution

<p>The&nbsp;<code>GRTSmh_diffres</code>&nbsp;data source file is a file collection, composed of nine monolayered GeoTIFF files of the&nbsp;<code>INT4S</code>&nbsp;datatype plus a GeoPackage with six polygon layers:</p> <ul> <li> <p>The polygon layers in the GeoPackage are the dissolved, polygonized versions of levels 4 to 9 of the&nbsp;<code>GRTSmh_brick</code>&nbsp;data source (<a href="https://doi.org/10.5281/zenodo.3354403">link</a>). This means that they provide the decimal (i.e. base 10) integer values of these&nbsp;<em>higher hierarchical levels</em>&nbsp;of the GRTS cell addresses of the raw data source&nbsp;<code>GRTSmaster_habitats</code>&nbsp;(<a href="https://doi.org/10.5281/zenodo.2682323">link</a>). Hence, the polygons are typically squares that correspond to the GRTS cell at the specified hierarchical level. The polygon layer is however restricted to the non-<code>NA</code>&nbsp;cells of the original&nbsp;<code>GRTSmaster_habitats</code>&nbsp;raster. Consequently, a part of the polygons is clipped along the Flemish border. Levels 1 to 3 are not provided for the whole of Flanders, because this would inflate the GPKG file. You can look at the&nbsp;<a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf5">source code</a>&nbsp;to do such things.</p> </li> <li> <p>The GeoTIFF files provide the respective levels 1 to 9 of the&nbsp;<code>GRTSmh_brick</code>&nbsp;data source in a raster format, at the resolution that corresponds to the GRTS cell at the specified hierarchical level. The presence of&nbsp;<code>NA</code>&nbsp;cells around Flanders at level 0 implies that, with decreasing resolution, the raster&#39;s extent increases and larger areas outside Flanders are covered by non-<code>NA</code>&nbsp;cells along the border.</p> </li> </ul> <p>The higher-level ranking numbers (compared to the original level 0) allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes.</p> <p>See R-code in the GitHub repository&nbsp;<a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_30_GRTSmh_diffres">&#39;n2khab-preprocessing&#39; at commit ecadaf5</a>&nbsp;for the creation from the&nbsp;<code>GRTSmh_brick</code>&nbsp;data source.</p> <p>A reading function to return the data source in a standardized way into the R environment&nbsp;is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>Beware that not all GRTS ranking numbers at the specified level are provided, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>

opencc-zeroJul 2019View details →
zenodo44/100

GRTSmh_brick: the raster data source GRTSmaster_habitats converted to 10 hierarchical cell address levels at the original resolution

<p>The data source file is a 10-layered GeoTIFF file, derived from the&nbsp;raster data source&nbsp;<code>GRTSmaster_habitats</code>&nbsp;(<a href="https://doi.org/10.5281/zenodo.2682323">link</a>).&nbsp;Both GeoTIFFs (<code>GRTSmaster_habitats</code>,&nbsp;<code>GRTSmh_brick</code>) use the&nbsp;<code>INT4S</code>&nbsp;datatype. The&nbsp;<code>GRTSmh_brick</code>&nbsp;data source (resolution 32 m) holds the decimal integer ranking numbers of 10 hierarchical levels of the GRTS cell addresses, including the one from&nbsp;<code>GRTSmaster_habitats</code>&nbsp;(with GRTS cell addresses at the resolution level).</p> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/ecadaf54d4a5aa662d0d18fbfe59788732bb7182/src/generate_GRTS_20_GRTSmh_brick">&#39;n2khab-preprocessing&#39;&nbsp;at commit&nbsp;ecadaf5</a> for its creation from the&nbsp;<code>GRTSmaster_habitats</code>&nbsp;data source.</p> <p>A reading function to return the data source in a standardized way into the R environment&nbsp;is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p>The higher-level ranking numbers of the RasterBrick allow spatially balanced samples at lower spatial resolution than that of 32 m, and can also be used for aggregation purposes. The provided hierarchical levels correspond to the resolutions vector&nbsp;<code>32 * 2^(0:9)</code>&nbsp;(minimum: 32 meters, maximum: 16384 meters).</p> <p>Beware that not all GRTS ranking numbers are present in the data source, as the original GRTS raster has been clipped with the Flemish outer borders (i.e., not excluding the Brussels Capital Region).</p>

opencc-zeroJul 2019View details →
zenodo44/100

Shenzhen address corpus (part)

<p>Data for&nbsp;our paper&nbsp;<strong>Lin, Y., Kang, M., Wu, Y., Du, Q. and Liu, T. (2019) A deep learning architecture for semantic address matching,&nbsp;<em>International Journal of Geographical Information Science</em>, DOI:&nbsp;10.1080/13658816.2019.1681431</strong></p> <p>The first 498,294 records of the corpus derived from the Shenzhen Address Database, which are part of the training samples for word2vec to obtain the vector representations of address elements.</p>

opencc-by-4.0Oct 2019View details →
zenodo44/100

ADDRESSING TAX COMPLIANCE ISSUES FOR LOAN-BASED PAYMENT TYPES: DEVELOPMENT OF THE TAX BUFFER MECHANISM AND ITS USE IN THE FINTECH INDUSTRY

<p>In the modern world of the fintech industry, tax changes are one of the key problems, especially with fixed loans. This study examines the Tax Buffer mechanism, designed to effectively manage tax obligations that vary depending on the jurisdiction and stages of delivery of goods. The main task of the mechanism is to automatically recalculate taxes to minimize the risk of errors and reduce the burden on the accounting and legal departments of the company. The implementation of this solution allows you to reduce the number of manual operations, reduce transaction costs and improve the customer experience by eliminating the need to notify users of every change in the amount of taxes. The results of the implementation of the mechanism have shown its high efficiency: a significant reduction in the number of errors and financial disputes, as well as an increase in operational efficiency. The Tax Buffer mechanism is an important innovation that helps to increase the resilience of fintech companies to changes in tax legislation.</p>

opencc-by-4.0Oct 2024View details →
zenodo44/100

Data for the publication "Addressing complexity in global aerosol climate model cloud microphysics"

<p>This repository contains the data for the paper:</p> <p>Authors: Ulrike Proske, Sylvaine Ferrachat, and Ulrike Lohmann<br> Titel: Addressing complexity in global climate model cloud microphysics<br> Date: 2022</p> <p>Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.7375978).</p>

opencc-by-4.0Nov 2022View details →
zenodo40/100

ePharmaLib: A Versatile Library of e-Pharmacophores to Address Small-Molecule (Poly-)Pharmacology

<p><em><strong>The peer-reviewed publication for this dataset has now been published&nbsp;in&nbsp;Journal of Chemical Information and Modeling, and can be accessed here:&nbsp;<a href="https://doi.org/10.3390/epidemiologia2030024">https://doi.org/10.1021/acs.jcim.1c00135</a>. Please cite this when using the dataset.</strong></em></p> <p><em>Reverse pharmacophore screening (parallel screening) is an efficient and cost-effective computational method used to study the polypharmacology of drugs. To this end, we created ePharmaLib: a collection of 15,148 energetically optimized, structure-based pharmacophores (e-pharmacophores) , with 3 to 8 features constituting 12.6%, 17.9%, 20.9%, 17.1%, 10.6% and 20.9%, respectively. The pharmacophores were generated from the 3D macromolecular structures of druggable proteins in complex with diverse ligands, retrieved from the sc-PDB database (<a href="http://bioinfo-pharma.u-strasbg.fr/scPDB/">http://bioinfo-pharma.u-strasbg.fr/scPDB/</a>). ePharmaLib can either be used with the Schr&ouml;dinger&rsquo;s PHASE program (<a href="https://www.schrodinger.com/products/phase">https://schrodinger.com/products/phase</a>) or PHARAO, also known as Align-it (<a href="https://silicos-it.be.s3-website-eu-west-1.amazonaws.com/software/align-it/1.0.4/align-it.html">https://silicos-it.be.s3-website-eu-west-1.amazonaws.com/software/align-it/1.0.4/align-it.html</a>). &nbsp;Designed for drug discovery research, this ready-to-use library could dramatically expedite drug discovery by revealing novel molecular interactions of drugs in an efficient and cost-effective manner.</em></p>

opencc-by-4.0Jan 2022View details →
zenodo40/100

Assessing and Addressing Deficiencies in the HCM Weaving Segment Analyses- Phase II- Project J5

<p>Traffic operation and geometric data from Ramp and Major weaves. These data were used to develop the speed and capacity models under the STRIDE J3 project.</p>

opencc-by-4.0Sep 2022View details →
zenodo40/100

Figure 3 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes

Figure 3. Aboveground biomass (g m−2) across densities (plants m−2) for maize and Setorio foberi. Vertical arrows indicate the maize (black numbers) and S. foberi (gray numbers) densities at which S. foberi reaches inflection point and 90% of maximum biomass. Horizontal arrows indicate S. foberi biomass at inflection point and 90% of maximum biomass.

opencc-by-4.0Oct 2023View details →
zenodo40/100

Figure 4 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes

Figure 4. Relative biomass of Amoronthus hybridus:maize replacement series from experiment 1 (left) and experiment 2 (right) with densities based in inflection point (A and B), maximum biomass (C and D), and equal N uptake (E and F). Black circles represent maize relative biomass,white circles represent A. hybridus relative biomass,and gray diamonds represent relative yield total biomass (RYT). Dotted line represents a relative biomass of 1 for all the proportions. The points and error bars represent data means and standard errors.

opencc-by-4.0Oct 2023View details →
zenodo40/100

Figure 2 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes

Figure 2. Aboveground biomass (g m−2) across densities (plants m−2) for maize and Amoronthus hybridus. Vertical arrows indicate the maize (black numbers) and A. hybridus (gray numbers) densities at which A. hybridus reaches inflection point and 90% of maximum biomass. Horizontal arrows indicate A. hybridus biomass at the inflection point and 90% of maximum biomass.

opencc-by-4.0Oct 2023View details →
zenodo40/100

Figure 1 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes

Figure 1. Theoretical relationship between relative biomass and plant density ratios of (A) two species of the same size and (B) two species with species 1 being larger than species 2. Dashed lines represent the regression lines for both species, and solid lines represent theoretical line of parity (1:1) between species.

opencc-by-4.0Oct 2023View details →
zenodo40/100

Figure 5 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes

Figure 5. Relative biomass of Setorio foberi:maize replacement series from experiment 1 (left) and experiment 2 (right) with densities based in inflection point (A and B), maximum biomass (C and D), and equal N uptake (E and F). Black circles represent maize relative biomass, white circles represent S. foberi relative biomass, and gray diamonds represent relative yield total biomass (RYT). Dotted line represents a relative biomass of 1 for all the proportions. The points and error bars represent data means and standard errors.

opencc-by-4.0Oct 2023View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record