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256 results for “submissions”
Observations of the Bottom Boundary Layer beneath the World's Largest Internal Solitary Waves_for JGR submission
<p>This folder contains preprocessed data, data processing scripts, Reynolds Averaged Navier Stokes (RANS) simulation scripts, and plotting scripts that produce the results in the manuscript entitled, "Observations of the Bottom Boundary Layer beneath the World's Largest Internal Solitary Waves," for submission to Journal of Geophysical Research Oceans by Trowbridge, Helfrich, Reeder, Medley, Chang, Jan, Ramp, and Yang.</p> <p> </p>
[2019 QSM Reconstruction Challenge] Submissions Stage 1
<p>This repository contains the original, unaltered files submitted to Stage 1 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge.</p> <p>The data provided to applicants of the challenge along with the scripts used to obtain the evaluation metrics are available <a href="https://doi.org/10.5281/zenodo.4559540">here</a>. Information about the submitted solutions and resulting analysis metrics are available <a href="https://doi.org/10.5281/zenodo.3687196">here</a>.</p> <p>The results of the challenge are fully reported in the journal article "<a href="http://doi.org/10.1002/mrm.28754">QSM Reconstruction Challenge 2.0: Design and Report of Results</a>".</p>
NDC-SDG Connections: Data on updated NDC submissions (V2)
<p>NDC-SDG Connections is a joint initiative of the German Institute of Development and Sustainability (IDOS) and the Stockholm Environment Institute (SEI). The research and visualisation project aims at illuminating synergies between the 2030 Agenda for Sustainable Development and the Paris Agreement, and at identifying entry points for coherent policies that promote just, sustainable and climate-smart development.</p> <p>The objective of the NDC-SDG Connections is to: foster a dialogue on meaningful interaction between the 2030 Agenda and the Paris Agreement, globally and at the national level; to increase transparency with easy accessibility to all climate activities; and to cultivate learning and catalyse partnerships between countries and other actors to raise the ambition of future NDCs.</p> <p>With its second version with data on the updated NDC submissions (V2), the NDC-SDG Connections project opened its data for public re-use. In 2025, the V2 is updated with new data to version 1.2.0. The data is provided in the following formats:</p> <ul> <li>single .csv files (data per SDG)</li> <li>zip .csv file (data per SDG for all SDG in one zip) </li> </ul> <p>Visit the Online Data Visualisation to interact directly with the data<strong>: www.NDC-SDG.info</strong></p> <p> </p> <p><strong>Note:This data set contains data for second NDC submissions (V2)</strong>. The terms ‘First’ and ‘Updated’ do not fully follow the UNFCCC nomenclature. For most countries, updated NDCs are called ‘First updated NDC’ or ‘Enhanced NDCs’, while some countries call their updated NDCs for ‘Second NDC’. In order to make it comprehensible, the tool developers have chosen to distinguish between ‘First’ and ‘Updated’. Detailed description of which version is counted as ‘First’ and which as ‘Updated’ has been documented in the data.<br><br></p> <p> </p> <p><strong>Updated NDCs included in version 1.2.0 of V2 - </strong><strong>New data was added for 27 updated NDCs plus one NDC updated to a newer version:</strong></p> <p>New Updated NDCs:</p> <p>Azerbaijan, Benin, Democratic Republic of the Congo, Grenada, India, Kyrgyzstan, Madagascar, Mali, Montenegro, Nepal, Nigeria, Oman, Qatar, Saint Kitts and Nevis, Saint Lucia, Samoa, Sierra Leone, Singapore, South Sudan, Sri Lanka, Suriname, Togo, Tunisia, Tuvalu, Ukraine, United States of America</p> <p>New version of Updated NDC:</p> <p>Namibia’s NDC has been updated to the most recent version.</p>
List of countries and NDCs in the NDC-SDG Connections: Data on updated NDC submissions (V2)
<p>NDC-SDG Connections is a joint initiative of the German Institute of Development and Sustainability (IDOS) and the Stockholm Environment Institute (SEI). The research and visualisation project aims at illuminating synergies between the 2030 Agenda for Sustainable Development and the Paris Agreement, and at identifying entry points for coherent policies that promote just, sustainable and climate-smart development.</p> <p>The objective of the NDC-SDG Connections is to: foster a dialogue on meaningful interaction between the 2030 Agenda and the Paris Agreement, globally and at the national level; to increase transparency with easy accessibility to all climate activities; and to cultivate learning and catalyse partnerships between countries and other actors to raise the ambition of future NDCs.</p> <p>With its second version, the NDC-SDG Connections project opened its data for public re-use. <br><br>This dataset contains a list of the countries and NDCs which are included in the NDC-SDG Connections tool as part of updated NDC submissions (V2), in .xlxs format. It is a complement to the dataset available at: https://doi.org/10.5281/zenodo.11400384 <br><br><strong>Visit the Online Data Visualisation to interact directly with the data: www.NDC-SDG.info</strong></p>
Model Checkpoints for AE Studio's AESMTE3 Submission to NLB 2021 Challenge
<p>This dataset contains all model checkpoints acquired while training <a href="https://ae.studio/">AE Studio</a>'s AESMTE3 submission for the <a href="https://neurallatents.github.io/">NLB 2021 Challenge</a>. The models are <a href="https://github.com/snel-repo/neural-data-transformers">neural-data-transformers</a> and were trained using AE's <a href="https://github.com/agencyenterprise/ae-nlb-2021">fork</a> of the neural-data-transformers repo.</p> <p>These model checkpoints are intended to be used by the NLB organizers in order to validate AE's submission.</p>
Numerical data analysed to produce Figure 3a of Nature Climate Change submission "Five challenges for subseasonal to decadal prediction research " by Merryfield et al.
<p>NetCDF4-formatted files containing daily sea ice concentration data from Environment and Climate Change Canada's CanSIPSv2 seasonal forecasting system described in Lin et al. (2020) https://doi.org/10.1175/WAF-D-19-0259.1 </p> <ul> <li>2 models, CanCM4i and GEM-NEMO</li> <li>10 ensemble members for each model, each in separate files as indicated by suffixes _1 to _10</li> <li>initialized May 1, 1980 to 2021</li> <li>840 files total (42 predicted years x 10 ensemble members x 2 models)</li> <li>model outputs interpolated to common 1-degree grid</li> </ul> <p>The calibrated probabilistic forecast map shown in Figure 3a is based on the nonhomogeneous censored Gaussian regression (NCGR) method described in Dirkson et al, (2021) https://doi.org/10.1175/WAF-D-20-0066.1 and produced using scripts available at https://github.com/adirkson/sea-ice-timing </p> <p>The procedure uses as inputs</p> <ul> <li>freeze-up dates calculated from the provided model outputs as described in Sigmond et al. (2016) https://doi.org/10.1002/2016GL071396</li> <li> <p>NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3: https://nsidc.org/data/G02202/versions/3</p> </li> </ul>
A Comprehensive Review of ANDA Submissions and Amendments Under GDUFA: FDA Guidelines for the Generic Drug Industry
<p><span>This review provides an in-depth analysis of the Food and Drug Administration's (FDA) guidance document titled <em>ANDA Submissions — Amendments to Abbreviated New Drug Applications (ANDAs) Under the Generic Drug User Fee Amendments (GDUFA)</em>, released in September 2024. The document serves as a comprehensive guide for the pharmaceutical industry, detailing the FDA's expectations regarding the classification, submission, and assessment of amendments to ANDAs and Prior Approval Supplements (PASs). The review discusses key elements of the guidance, including amendment categories (major, minor, and unsolicited), assessment timelines, the process for reclassification of amendments, and potential deficiencies in submissions. The guidance also addresses changes in classifications and assessment goals, deferred amendments, and best practices for ensuring timely FDA approval. This review aims to clarify the FDA’s current thinking on ANDA submissions under GDUFA and the practical implications for generic drug manufacturers seeking to comply with the established regulations.</span></p>
Map. The early Gupta kingdom with the approximate location of surrounding powers that offered submission to Samudragupta as recounted in the Allahābād Pillar inscription.
<p>Map. The early Gupta kingdom with the approximate location of surrounding powers that offered submission to Samudragupta as recounted in the <a href="https://siddham.network/object/ob00001/">Allahābād Pillar inscription</a>.</p>
Nationally Determined Contributions (Latest submissions)
<p>Links to the latest (up to October 2021) national climate action plans under the Paris Agreement</p>
Research Excellence Framework (REF) 2021 enhanced submissions dataset
<p>An enhanced version of the public <a href="https://results2021.ref.ac.uk/">REF 2021 submissions dataset</a>, produced by Jisc, which contains metadata for all outputs submitted to the exercise. Metadata has been cleaned and new fields have been added to increase the potential for analytical purposes, e.g. by identifying where the publisher exists as an imprint of a larger parent company.</p> <p>For its own analytical purposes, Jisc focused on long-form output types (books and parts of books), but cleaning measures were performed on the entire dataset, uploaded here.</p>
NDC-SDG Connections: Data on first NDC submissions (V1)
<p>NDC-SDG Connections is a joint initiative of the German Institute of Development and Sustainability (IDOS) and the Stockholm Environment Institute (SEI). The research and visualisation project aims at illuminating synergies between the 2030 Agenda for Sustainable Development and the Paris Agreement, and at identifying entry points for coherent policies that promote just, sustainable and climate-smart development.</p> <p>The objective of the NDC-SDG Connections is to: foster a dialogue on meaningful interaction between the 2030 Agenda and the Paris Agreement, globally and at the national level; to increase transparency with easy accessibility to all climate activities; and to cultivate learning and catalyse partnerships between countries and other actors to raise the ambition of future NDCs.<br> <br> With its second version, the NDC-SDG Connections project opened its data for public re-use. The data on first NDC submissions (V1) is provided in the following formats:</p> <ul> <li>single .csv files (per data per SDG)</li> <li>zip .csv file (data per SDG for all SDG in one zip)</li> <li>.xlxs file (Excel)</li> </ul> <p><strong>Visit the Online Data Visualisation to interact directly with the data: www.NDC-SDG.info</strong></p> <p>Additional files:</p> <ul> <li>.pdf file documenting the methodological framework including the coding and data validation process of the NDC-SDG Connections project</li> <li>.csv file with all NDCs included into the analysis (V1)</li> </ul> <p><br> <strong>Note: This data set contains data for first NDC submissions (V1). </strong>The terms ‘First’ and ‘Updated’ do not fully follow the UNFCCC nomenclature. For most countries, updated NDCs are called ‘First updated NDC’ or ‘Enhanced NDCs’, while some countries call their updated NDCs for ‘Second NDC’. In order to make it comprehensible, the tool developers have chosen to distinguish between ‘First’ and ‘Updated’. Detailed description of which version is counted as ‘First’ and which as ‘Updated’ has been documented in the data.</p> <p> </p>
BNZ Soil Temperature Data for submission to EcoTrends
This datafile if a product dataset that is for submission to the EcoTrends project with the LTER program. Data for a selected site was checked for quality issues and aggregated to a single daily average value. The source data can be accessed in the Bonanza Creek Experimental Forest: Hourly Soil Temperature at varying depths from 1988 to Present dataset (http://www.lter.uaf.edu/data_detail.cfm?datafile_pkey=3).
BNZ Stream Flow Data for submission to EcoTrends
This datafile if a product dataset that is for submission to the EcoTrends project with the LTER program. Data for a selected site was checked for quality issues and aggregated to a single daily average value. The source data can be accessed in the dataset Caribou-Poker Creeks Research Watershed: Daily Flow Rates for C2, C3, C4 (http://www.lter.uaf.edu/data_detail.cfm?datafile_pkey=142).
Datasets for How is the Pandemic Affecting AGU Journal Article Submissions?
<p>These files provide tabular data on gender, age, and country of corresponding authors (the person submitting the manuscript to the peer review system) of American Geophysical Union (AGU) journals from January 2018 through April 2020. They supplement the article 'How is the Pandemic Affecting AGU Journal Article Submissions?' in Eos (https://eos.org/).</p>
A global flood risk modeling framework built with climate models and machine learning - Submission - Data Supplement
<p>This contribution contains data, fitted statistical models, and an analysis script for the submitted manuscript "A global flood risk modeling framework built with climate models and machine learning" by David A. Carozza and Mathieu Boudreault.</p>
[2019 QSM Reconstruction Challenge] Submissions Stage 2
<p>This repository contains the original, unaltered files submitted to Stage 2 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge.</p> <p>The data provided to applicants of the challenge along with the scripts used to obtain the evaluation metrics are available <a href="https://doi.org/10.5281/zenodo.4559540">here</a>. Information about the submitted solutions and resulting analysis metrics are available <a href="https://doi.org/10.5281/zenodo.3687196">here</a>.</p> <p>The results of the challenge are fully reported in the journal article "<a href="http://doi.org/10.1002/mrm.28754">QSM Reconstruction Challenge 2.0: Design and Report of Results</a>".</p>
hrafsuicidedata: Initial release upon submission of revised manuscript
<p>Data to test the bargaining model vs. the inclusive fitness model of suicidal behavior against the HRAF Probability Sample</p>
Matrix multiplication software and results bundle for paper "Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library" for P^3MA submission
<p>This is the archive containing the matrix multiplication software and the results of the publication "<em>Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library</em>" submitted to the P^3MA workshop 2017.</p> <p><strong>The archive has the following content:</strong></p> <ul> <li>Source code for the (tiled) matrix multiplication in "src": <ul> <li>regular version in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-compatible-alpaka-0-1-0</li> <li>Commit: a63ba4810d6bfcca62c68dd57408af15028e78a3</li> </ul> </li> <li>forked version for XL in "src/matmul": <ul> <li>Remote: https://github.com/theZiz/matmul.git (copy will be removed)</li> <li>Branch: topic-xl-workaround</li> <li>Commit: 1fee028eccb8cf7b677e8071233e08aa9f81846a</li> </ul> </li> </ul> </li> <li>The compiled binaries and the results of the tuning and scaling runs are in "runs" in sub folders for each type of run and architectures.</li> </ul>
Uncertainties from the UNFCCC National Inventory Reports (submission 2017)
<p><strong>Summary:</strong></p> <p>This data repository contains the uncertainties of the national greenhouse-gas inventories submitted to the United Nations Framework Convention on Climate Change (UNFCCC). We extracted the data from the National Inventory Reports (NIR) submitted in 2017 covering the emission from 2015. We use the data in our study on "Estimating the uncertainty of the greenhouse gas extensions in Multi-Regional Input-Output analysis" submitted to the Journal of Earth System Science Data (ESSD): <a href="https://essd.copernicus.org/preprints/essd-2023-473/">https://essd.copernicus.org/preprints/essd-2023-473/</a></p> <p><strong>Background:</strong></p> <p>NIRs are only available in pdf-format which makes accessing them from computer impossible. Against this background, we extracted the uncertainty tables from the Annex of the NIR pdf documents in a semi-automated way using a set of Python and R scripts. To bring the uncertainty into a common format, manual data cleaning and adjustments were necessary due to different structuring and processing of uncertainty data by the parties. </p> <p><strong>Data: </strong></p> <p>We brought the data into the format provided in the IPCC 2006 guidelines (Volume 1, Chapter 3). The guidelines distinguish two approaches to uncertainty quantification, tier 1 based on analytical error propagation, and tier 2 based on Monte-Carlo simulations. For each, tier 1 and tier 2 uncertainties, the IPCC 2006 guidelines provide a distinct table template, a screenshot of which can be found in this repository under <a href="../api/records/10037714/draft/files/IPCC2006_table3-2/content">IPCC2006_table3-2</a> and <a href="../api/records/10037714/draft/files/IPCC2006_table3-3/content">IPCC2006_table3-3</a>.</p> <p>Accordingly we provide two different data sets: </p> <ul> <li><a href="../api/records/10037714/draft/files/tier1.csv/content">tier1.csv</a> containing the Tier 1 uncertainties structured according to Table 3.2 of the IPCC 2006 guidelines (see <a href="../api/records/10037714/draft/files/IPCC2006_table3-2/content">IPCC2006_table3-2</a>)</li> <li><a href="../api/records/10037714/draft/files/tier2.csv/content">tier2.csv </a>containing the Tier 2 uncertainties structured according to Table 3.2 of the IPCC 2006 guidelines (see <a href="../api/records/10037714/draft/files/IPCC2006_table3-3/content">IPCC2006_table3-3</a>)</li> </ul> <p>Compared to the table templates from the IPCC 2006 guidelines we added three identifying columns to each dataset: </p> <ul> <li><strong>party</strong>: Name of the party</li> <li><strong>year</strong>: Inventory year (2015 for all items)</li> <li><strong>LULUCF</strong>: if emissions from Land use, land-use change, and forestry (LULUCF) are included (<em>incl</em>) or excluded (<em>excl</em>) in the inventory. Background: parties often publish two versions of the uncertainty table: One including emissions from Land use, land-use change, and forestry (LULUCF), one excluding.</li> </ul> <p>Moreover, we split the column <strong>A </strong>into two columns <strong>category </strong>and <strong>classification </strong>and renamed the original column <strong>A </strong>into <strong>A_raw</strong>. </p> <p> </p>
Simulations used in the Ocean Science Journal submission titled "Internal and forced ocean variability in the Mediterranean Sea " by Benincasa et al., 2024
<p>Temperature (votemper) and current speed datasets from the EAS5 (Clementi et al., <em>Mediterranean Sea Analysis and Forecast (CMEMS MED-Currents, EAS5 system),</em> 2019; Coppini et al., <em>The Mediterranean forecasting system. Part I: evolution and performance</em>, EGUsphere, pp. 1–50, 2023) simulations used in the manuscript titled "<em>Internal and forced ocean variability in the Mediterranean Sea"</em> and submitted to the journal Ocean Science by Benincasa et al. </p> <p>The daily fields are at 2 depth levels ( 0 = 0 m, 2 = 30 m) and in the 2 seasons (JFMA = winter, JASO = summer) for the entire Mediterranean Sea. The vertical profile of the temperature field up to about 950 m depth is available at 8 locations distributed over the basin. The depth levels are found in <em>depth.pkl</em>: the first column represents the depth of the various levels, whereas the second is the increments between 2 consecutive depth levels. </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.