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1,291 results for “gap”

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zenodo44/100

TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>HCl Profile Processing and Bias Correction</h3> Collocated HCl profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Separate XGBoost regression models are then trained for these HCl differences at each height level within a given latitude bin. These trained models are subsequently used to estimate HCl bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved HCl profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean HCl profiles: <ul> <li> <code>zmhcl_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmhcl_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023

opencc-by-4.0Feb 2023View details →

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Considerable gaps in our global knowledge of potential groundwater accessibility

This data contains the model ensemble mean of water table depth in meter of four global steady-state groundwater models. The spatial resolution is 5 arc-minutes (~9 by 9 km at the Equator). Models with higher resolution were aggregated to this resolution using the mean. The Models do not contain any anthropogenic influences. The uncertainty in water table depth (m) is based on the range (Max - Min) of the ensemble.Robert Reinecke 2024

opencc-by-4.0Nov 2023View details →

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Emissions gap NDC and net zero findings after COP27

Analysis of the level and robustness of various government climate pledges, both 2030 NDCs and net zero goals. This repository contains the data required to run <a href="https://github.com/Rlamboll/Emissions_Gap">https://github.com/Rlamboll/Emissions_Gap</a>. "Analysis_update_12_14 (2).xlsx" contains the 2023 assessment of net zero pledges by governments in terms of both their quality and content. "Data_forextensions_PostCOP27_master.xlsx" contains the estimates of emissions until 2030 based on NDCs after COP27, "Data_forextensions_EGR2023_master.xlsx" contains the updated version for the paper "Credibility gap in net-zero climate targets leaves world at high risk". "2022_emission_gap_temp_summary_data.csv" is the output of the complete process.  "kyoto_and_co2_emissions_summary_23.65.csv" is the Kyoto total and CO2 emissions for each scenario, "infilled_extended_and_infilled_unep_23.65.csv" is the complete set of emissions for each scenario.  Versions 1.0.0 and 1.0.1 are identical in terms of processing, but more data is uploaded for 1.0.1 with scenarios with additional carbon price increase rates. Version 1.1.0 contains the Data_forextensions_EGR2023_master file. Version 1.2.0 contains a bugfix affecting net zero targets applied to the OECD+ regional emissions, and includes emissions data.  Data and calculation are associated with the paper at DOI: 10.1126/science.adg6248

opencc-by-4.0Jan 2023View details →

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GAPs Data Repository on Return: Guideline, Data Samples and Codebook

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at <a href="https://data.returnmigration.eu/" target="_new">https://data.returnmigration.eu/</a>. This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels. The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts. The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It  is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.     This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data. The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field. Explore the GAPs Data Repository at <a href="https://data.returnmigration.eu/" target="_new">https://data.returnmigration.eu/</a>.

opencc-by-4.0Mar 2024View details →

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Daily 1-km gap-free PM2.5 grids in China, v1 (2000–2020)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide a 21-year-long (2000–2020) gap free PM2.5 concentration product with daily 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.

opencc-by-4.0Nov 2021View details →

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Daily 1-km gap-free PM10 grids in China, v1 (2000–2020)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide a 21-year-long (2000–2020) gap free PM10 concentration product with daily 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.

opencc-by-4.0Nov 2021View details →

zenodo44/100

Daily 1-km gap-free AOD grids in China, v1 (2000–2020)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide a 21-year-long (2000–2020) gap free AOD product with daily 1-km resolution covering the land area of China. The dataset was generated via a seamless integration of the tensor flow based multimodal data fusion with ensemble learning based knowledge transfer in statistical data mining. The proposed method transformed a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility that were acquired from diversified sensors or platforms via integrative efforts of spatial pattern recognition for high dimensional gridded data analysis toward data fusion and multiresolution image analysis. The daily gap free AOD was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.

opencc-by-4.0Nov 2021View details →

zenodo44/100

Annual mean 1-km gap-free AOD, PM2.5, and PM10 grids in China, v1 (2000–2020)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide 21-year-long (2000–2020) gap free annual mean AOD, PM2.5 and PM10 concentration data with a 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.

opencc-by-4.0Nov 2021View details →

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

This repository contains the raw data and code to reproduce results and plots presented in: "Addressing gaps in small-scale fisheries: a low-cost tracking system". The release contains: <ul> <li>ssf_function.R. The R function developed for the analysis</li> <li>ssf_workflow.R.  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 →

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DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region

This dataset has been prepared as part of the Interreg Alpine Space project Eco-AlpsWater (ASP569) - Innovative Ecological Assessment and Water Management Strategy for the Protection of Ecosystem Services in Alpine Lakes and Rivers, <a href="https://www.alpine-space.eu/projects/eco-alpswater/en/home">https://www.alpine-space.eu/projects/eco-alpswater/en/home</a> Individual archives include 16S rRNA (cyanobacteria) and 18S rRNA (microalgae) FASTA sequences and associated blastn results obtained from the high throughput sequencing of plankton and biofilm bulk/eDNA samples collected in 2019 in 37 lakes and 22 rivers across the Alpine region. These are supporting files for the paper by Salmaso et al., 2022. DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region. Science of the Total Environment, in press.

opencc-by-4.0Apr 2022View details →

zenodo44/100

Smooth numbers in large prime gaps

Overview This dataset contains numbers from 25 up to 1 quadrillion (1015) that are smooth relative to the gap to the preceding prime.  More precisely, we list all numbers n so that   r + pa ≤ n where r is the largest prime smaller than n - 1, and pa is the largest prime-power divisor of n.  The dataset is the result of a 10 day computation using 15 cores on an Intel Xeon system, running code hosted at <a href="https://github.com/RussWoodroofe/invgen">GitHub</a> (see "Related identifiers").  The GitHub code checks additional conditions when r is n - 2 and n - 1 is a power of 2, but it is easy and quick to check that when (up to 1015) n = 2k + 1, the second largest prime r2 satisfies r2 + pa > n.  Thus, this additional check makes no difference in the output. Our motivations for computing this data are described in our paper On invariable generation of alternating groups by elements of prime and prime power order (<a href="http://arxiv.org/abs/2201.12371">arXiv:2201.12371</a>).  Any number n in the range which is not of the given form has the associated alternating group An generated by any element of order r together with any element having a certain cycle structure (and of order pa). Description / specification The data is stored as compressed text-based input to a computer algebra system, specifically in gzipped <a href="https://www.gap-system.org/">GAP</a> format.  The file out-k.g.gz holds numbers in the range from (k - 1)⋅1012 to k⋅1012.  The first line of each file sets the variable invgen_oversmooth_range to be the range (thus, [(k - 1)⋅1012 .. k⋅1012]).  The subsequent lines set invgen_oversmooth to a list of pairs of numbers [n, pa], where n is a smooth number as described above, and pa is the largest prime-power of n.  The largest prime preceding n - 1 is given in a GAP comment. Thus, the first few lines of out-0.g.gz (when uncompressed) appear as <pre><code>invgen_oversmooth_range:=[25..1000000000000]; invgen_oversmooth := [ [ 30, 5 ], # bp 23 [ 60, 5 ], # bp 53 [ 126, 9 ], # bp 113 [ 210, 7 ], # bp 199 [ 252, 9 ], # bp 241 [ 308, 11 ], # bp 293 [ 330, 11 ], # bp 317 [ 420, 7 ], # bp 409 ...</code></pre> where [25 .. 1000000000000] is the range considered, and for example "[ 30, 5 ],  # bp 23" represents that 23 is the largest prime preceding 30 - 1, 5 is the largest prime-power divisor of 30, and 23 + 5 ≤ 30. We created the data in GAP files for ease of inputting into a GAP program in our own use of the data.  It is easy to convert the GAP files to another format via standard technique such as regular expression-based search and replace. For example, on macOS or Linux, the following command will convert the list in out-0.g.gz to a CSV file, which it will display on the terminal. <pre><code class="language-bash">zcat out_quadrillion/out-0.g.gz | sed -En 's/ \[ ([0-9]+), ([0-9]+) \], # bp ([0-9]+)/\1,\2,\3/gp' | less</code></pre>

opencc-by-4.0Feb 2022View details →

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Periodic Hydraulic Testing Dataset for "Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)"

This dataset is associated with the SNSF-SPARK project “Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)”. Please read the ReadMe file for more information.

opencc-by-4.0Mar 2022View details →

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An 18.9-minute Blue Large-Amplitude Pulsator Crossing the ''Hertzsprung Gap'' of Hot Subdwarfs

This record contains the data of spectra and photometry used for the paper An 18.9-minute Blue Large-Amplitude Pulsator Crossing the ''Hertzsprung Gap'' of Hot Subdwarfs. LRIS_TMTS-BLAP-1.[0-3].spec - LRIS spectra of TMTS-BLAP-1; ZTF_[r,g]_[TMTS-BLAP-1,ZGP-BLAP-09].dat - Reduced ZTF r-/g-band light curves of TMTS-BLAP-1/ZGP-BLAP-09. The raw data were provided by NASA/IPAC Infrared Science Archive (<a href="https://irsa.ipac.caltech.edu">https://irsa.ipac.caltech.edu</a> ); ATLAS_[o,c]_[TMTS-BLAP-1,ZGP-BLAP-09].dat - Reduced ATLAS o-/c-band light curves of TMTS-BLAP-1/ZGP-BLAP-09. The raw data were obtained from ATLAS forced photometry server (<a href="https://fallingstar-data.com/forcedphot">https://fallingstar-data.com/forcedphot</a>); [TMTS, SNOVA]_[202xxxxx]_TMTS-BLAP-1.dat - Reduced high-cadence photometry of TMTS-BLAP-1 obtained from TMTS or SNOVA [HeB, preWD]_tracks.zip - Evolutionary tracks for helium-burning stars / pre-WDs TMTS-BLAP-1_simulated_spectra.zip - A compressed package for four simulated spectra of TMTS-BLAP-1

opencc-by-4.0Apr 2022View details →

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Raw data for the article "Size-Dependent Structural Alterations in Ag Nanoparticles During CO2 Electrolysis in a Gas-Fed Zero-Gap Electrolyzer"

In the article "Size-Dependent Structural Alterations in Ag Nanoparticles During CO2 Electrolysis in a Gas-Fed Zero-Gap Electrolyzer" we described our investigation on the size-dependent degradation behavior of Ag NPs (10, 40, and 100 nm in size) on GDE during CO2 electrolysis. Here we present the dataset the work was based on. For each figure in the article and the supporting information we provide a set of raw and unprocessed data.

opencc-by-4.0Aug 2022View details →

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Canopy gap sizes across the Brazilian Amazon

This data frame contains the raw gap data used to study the gap size-frequency distributions across the Brazilian Amazon in: Reis, C., Jackson, T., et al 2022. Forest disturbance and growth processes are reflected in the geographic distribution of large canopy gaps across the Brazilian Amazon. Journal of Ecology.  They gaps were extracted from the EBA LiDAR data set https://zenodo.org/record/4968706#.YygSGHZKg5s

opencc-by-4.0Sep 2022View details →

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Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project

Flux tower observations, model spin-up and site characteristics data for Urban-PLUMBER sites associated with the manuscript: <blockquote> "Harmonized, gap-filled dataset from 20 urban flux tower sites"  <a href="https://doi.org/10.5194/essd-14-5157-2022">https://doi.org/10.5194/essd-14-5157-2022</a> </blockquote> Use of any data must give credit through citation of the above manuscript and other site sources as appropriate (see below). We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant site contacts are included in site metadata.  Data can be downloaded from the bottom of this page.  <table> <tbody> <tr> <td> Sitename </td> <td> City </td> <td> Country </td> <td> Observed period </td> <td> References </td> </tr> <tr> <td> AU-Preston </td> <td> Melbourne </td> <td> Australia </td> <td> Aug 2003 – Nov 2004 </td> <td> (Coutts et al., 2007a, b) </td> </tr> <tr> <td> AU-SurreyHills </td> <td> Melbourne </td> <td> Australia </td> <td> Feb 2004 – Jul 2004 </td> <td> (Coutts et al., 2007a, b) </td> </tr> <tr> <td> CA-Sunset </td> <td> Vancouver </td> <td> Canada </td> <td> Jan 2012 – Dec 2016 </td> <td> (Christen et al., 2011; Crawford and Christen, 2015) </td> </tr> <tr> <td> FI-Kumpula </td> <td> Helsinki </td> <td> Finland </td> <td> Dec 2010 – Dec 2013 </td> <td> (Karsisto et al., 2016) </td> </tr> <tr> <td> FI-Torni </td> <td> Helsinki </td> <td> Finland </td> <td> Dec 2010 – Dec 2013 </td> <td> (Järvi et al., 2018; Nordbo et al., 2013) </td> </tr> <tr> <td> FR-Capitole </td> <td> Toulouse </td> <td> France </td> <td> Feb 2004 – Mar 2005 </td> <td> (Masson et al., 2008; Goret et al., 2019) </td> </tr> <tr> <td> GR-HECKOR </td> <td> Heraklion </td> <td> Greece </td> <td> Jun 2019 – Jun 2020 </td> <td> (Stagakis et al., 2019) </td> </tr> <tr> <td> JP-Yoyogi </td> <td> Tokyo </td> <td> Japan </td> <td> Mar 2016 – Mar 2020 </td> <td> (Hirano et al., 2015; Ishidoya et al., 2020) </td> </tr> <tr> <td> KR-Jungnang </td> <td> Seoul </td> <td> South Korea </td> <td> Jan 2017 – Apr 2019 </td> <td> (Jo et al., n.d.; Hong et al., 2020) </td> </tr> <tr> <td> KR-Ochang </td> <td> Ochang </td> <td> South Korea </td> <td> Jun 2015 – Jul 2017 </td> <td> (Hong et al., 2019, 2020) </td> </tr> <tr> <td> MX-Escandon </td> <td> Mexico City </td> <td> Mexico </td> <td> Jun 2011 – Sep 2012 </td> <td> (Velasco et al., 2011, 2014) </td> </tr> <tr> <td> NL-Amsterdam </td> <td> Amsterdam </td> <td> Netherlands </td> <td> Jan 2019 – Oct 2020 </td> <td> (Steeneveld et al., 2020) </td> </tr> <tr> <td> PL-Lipowa </td> <td> Łódź </td> <td> Poland </td> <td> Jan 2008 – Dec 2012 </td> <td> (Fortuniak et al., 2013; Pawlak et al., 2011) </td> </tr> <tr> <td> PL-Narutowicza </td> <td> Łódź </td> <td> Poland </td> <td> Jan 2008 – Dec 2012 </td> <td> (Fortuniak et al., 2013, 2006) </td> </tr> <tr> <td> SG-TelokKurau </td> <td> Singapore </td> <td> Singapore </td> <td> Feb 2015 – Feb 2016 </td> <td> (Roth et al., 2017) </td> </tr> <tr> <td> UK-KingsCollege </td> <td> London </td> <td> UK </td> <td> Apr 2012 – Jan 2014 </td> <td> (Bjorkegren et al., 2015; Kotthaus and Grimmond, 2014a, b) </td> </tr> <tr> <td> UK-Swindon </td> <td> Swindon </td> <td> UK </td> <td> May 2011 – Apr 2013 </td> <td> (Ward et al., 2013) </td> </tr> <tr> <td> US-Baltimore </td> <td> Baltimore </td> <td> USA </td> <td> Jan 2002 – Jan 2007 </td> <td> (Crawford et al., 2011) </td> </tr> <tr> <td> US-Minneapolis </td> <td> Minneapolis </td> <td> USA </td> <td> Jun 2006 – May 2009 </td> <td> (Peters et al., 2011; Menzer and McFadden, 2017) </td> </tr> <tr> <td> US-WestPhoenix </td> <td> Phoenix </td> <td> USA </td> <td> Dec 2011 – Jan 2013 </td> <td> (Chow, 2017; Chow et al., 2014) </td> </tr> </tbody> </table> For further site information and timeseries plots see <a href="https://urban-plumber.github.io/sites">https://urban-plumber.github.io/sites</a>. For processing code see <a href="https://github.com/matlipson/urban-plumber_pipeline">https://github.com/matlipson/urban-plumber_pipeline</a>. Data Two data archives are available on this page. <ul> <li>The full collection includes all observed, gap-filled, spin-up and site characteristic data, in both netcdf and text form.</li> <li>The "obs_only" archive includes a duplicate of site observation timeseries (after quality control) in a single netcdf file.</li> </ul> Full collection The full archive includes site folders with: <ul> <li><code>index.html</code>: A summary page with site characteristics and timeseries plots.</li> <li><code>SITENAME_sitedata_v1.csv</code>: comma separated file for numerical site characteristics e.g. location, surface cover fraction etc.</li> <li><code>timeseries/</code> (following files are available as netCDF and txt) <ul> <li><code>SITENAME_raw_observations_v1</code>: site observed timeseries before project-wide quality control.</li> <li><code>SITENAME_clean_observations_v1</code>: site observed timeseries after project-wide quality control.</li> <li><code>SITENAME_metforcing_v1</code>: gap-filled and prepended (10yr spinup) site observation forcing dataset for model evaluation.</li> <li><code>SITENAME_era5_corrected_v1</code>: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset.</li> </ul> </li> </ul> "Obs Only" This archive contains duplicate data from the full collection (observations after QC): <ul> <li><code>UP_all_clean_observations_UTC_v1.nc</code>: in coordinated universal time (UTC)</li> <li><code>UP_all_clean_observations_localstandardtime_v1.nc</code>: in local standard time</li> </ul> Site references Bjorkegren, A. B., Grimmond, C. S. B., Kotthaus, S., and Malamud, B. D.: CO2 emission estimation in the urban environment: Measurement of the CO2 storage term, Atmospheric Environment, 122, 775–790, https://doi.org/10.1016/j.atmosenv.2015.10.012, 2015. Chow, W.: Eddy covariance data measured at the CAP LTER flux tower located in the west Phoenix, AZ neighborhood of Maryvale from 2011-12-16 through 2012-12-31, https://doi.org/10.6073/PASTA/FED17D67583EDA16C439216CA40B0669, 2017. Chow, W. T. L., Volo, T. J., Vivoni, E. R., Jenerette, G. D., and Ruddell, B. L.: Seasonal dynamics of a suburban energy balance in Phoenix, Arizona, International Journal of Climatology, 34, 3863–3880, https://doi.org/10.1002/joc.3947, 2014. Christen, A., Coops, N. C., Crawford, B. R., Kellett, R., Liss, K. N., Olchovski, I., Tooke, T. R., van der Laan, M., and Voogt, J. A.: Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements, Atmospheric Environment, 45, 6057–6069, https://doi.org/10.1016/j.atmosenv.2011.07.040, 2011. Coutts, A. M., Beringer, J., and Tapper, N. J.: Characteristics influencing the variability of urban CO2 fluxes in Melbourne, Australia, Atmospheric Environment, 41, 51–62, https://doi.org/10.1016/j.atmosenv.2006.08.030, 2007a. Coutts, A. M., Beringer, J., and Tapper, N. J.: Impact of Increasing Urban Density on Local Climate: Spatial and Temporal Variations in the Surface Energy Balance in Melbourne, Australia, J. Appl. Meteor. Climatol., 46, 477–493, https://doi.org/10.1175/JAM2462.1, 2007b. Crawford, B. and Christen, A.: Spatial source attribution of measured urban eddy covariance CO2 fluxes, Theor Appl Climatol, 119, 733–755, https://doi.org/10.1007/s00704-014-1124-0, 2015. Crawford, B., Grimmond, C. S. B., and Christen, A.: Five years of carbon dioxide fluxes measurements in a highly vegetated suburban area, Atmospheric Environment, 45, 896–905, https://doi.org/10.1016/j.atmosenv.2010.11.017, 2011. Fortuniak, K., Kłysik, K., and Siedlecki, M.: New measurements of the energy balance components in Łódź, in: Preprints, sixth International Conference on Urban Climate: 12-16 June, 2006, Göteborg, Sweden, Sixth International Conference On Urban Climate, Göteborg, Sweden, 64–67, 2006. Fortuniak, K., Pawlak, W., and Siedlecki, M.: Integral Turbulence Statistics Over a Central European City Centre, Boundary Layer Meteorology; Dordrecht, 146, 257–276, https://doi.org/10.1007/s10546-012-9762-1, 2013. Goret, M., Masson, V., Schoetter, R., and Moine, M.-P.: Inclusion of CO2 flux modelling in an urban canopy layer model and an evaluation over an old European city centre, Atmospheric Environment: X, 3, 100042, https://doi.org/10.1016/j.aeaoa.2019.100042, 2019. Hirano, T., Sugawara, H., Murayama, S., and Kondo, H.: Diurnal Variation of CO2 Flux in an Urban Area of Tokyo, Sola, 11, 100–103, https://doi.org/10.2151/sola.2015-024, 2015. Hong, J., Lee, K., and Hong, J.-W.: Observational data of Ochang and Jungnang in Korea, 2020. Hong, J.-W., Hong, J., Chun, J., Lee, Y. H., Chang, L.-S., Lee, J.-B., Yi, K., Park, Y.-S., Byun, Y.-H., and Joo, S.: Comparative assessment of net CO2 exchange across an urbanization gradient in Korea based on eddy covariance measurements, Carbon Balance and Management, 14, 13, https://doi.org/10.1186/s13021-019-0128-6, 2019. Ishidoya, S., Sugawara, H., Terao, Y., Kaneyasu, N., Aoki, N., Tsuboi, K., and Kondo, H.: O2 : CO2 exchange ratio for net turbulent flux observed in an urban area of Tokyo, Japan, and its application to an evaluation of anthropogenic CO2 emissions, Atmospheric Chemistry and Physics, 20, 5293–5308, https://doi.org/10.5194/acp-20-5293-2020, 2020. Järvi, L., Rannik, Ü., Kokkonen, T. V., Kurppa, M., Karppinen, A., Kouznetsov, R. D., Rantala, P., Vesala, T., and Wood, C. R.: Uncertainty of eddy covariance flux measurements over an urban area based on two towers, Atmospheric Measurement Techniques, 11, 5421–5438, https://doi.org/10.5194/amt-11-5421-2018, 2018. Jo, S., Hong, J.-W., and Hong, J.: The observational flux measurement data of suburban and low-residential areas in Korea (in preparation), n.d. Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., W., O. K., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land‐surface models in the high‐latitude city Helsinki, Q.J.R. Meteorol. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016. Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part I: Temporal variability of long-term observations in central London, Urban Climate, 10, Part 2, 261–280, https://doi.org/10.1016/j.uclim.2013.10.002, 2014a. Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part II: Impact of spatial heterogeneity of the surface, Urban Climate, 10, Part 2, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014b. Masson, V., Gomes, L., Pigeon, G., Liousse, C., Pont, V., Lagouarde, J.-P., Voogt, J., Salmond, J., Oke, T. R., Hidalgo, J., Legain, D., Garrouste, O., Lac, C., Connan, O., Briottet, X., Lachérade, S., and Tulet, P.: The Canopy and Aerosol Particles Interactions in TOulouse Urban Layer (CAPITOUL) experiment, Meteorol Atmos Phys, 102, 135, https://doi.org/10.1007/s00703-008-0289-4, 2008. Menzer, O. and McFadden, J. P.: Statistical partitioning of a three-year time series of direct urban net CO2 flux measurements into biogenic and anthropogenic components, Atmospheric Environment, 170, 319–333, https://doi.org/10.1016/j.atmosenv.2017.09.049, 2017. Nordbo, A., Järvi, L., Haapanala, S., Moilanen, J., and Vesala, T.: Intra-City Variation in Urban Morphology and Turbulence Structure in Helsinki, Finland, Boundary-Layer Meteorol, 146, 469–496, https://doi.org/10.1007/s10546-012-9773-y, 2013. Pawlak, W., Fortuniak, K., and Siedlecki, M.: Carbon dioxide flux in the centre of Łódź, Poland—analysis of a 2-year eddy covariance measurement data set, International Journal of Climatology, 31, 232–243, https://doi.org/10.1002/joc.2247, 2011. Peters, E. B., Hiller, R. V., and McFadden, J. P.: Seasonal contributions of vegetation types to suburban evapotranspiration, Journal of Geophysical Research: Biogeosciences, 116, https://doi.org/10.1029/2010JG001463, 2011. Roth, M., Jansson, C., and Velasco, E.: Multi-year energy balance and carbon dioxide fluxes over a residential neighbourhood in a tropical city, Int. J. Climatol., 37, 2679–2698, https://doi.org/10.1002/joc.4873, 2017. Stagakis, S., Chrysoulakis, N., Spyridakis, N., Feigenwinter, C., and Vogt, R.: Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece, Atmospheric Environment, 201, 278–292, https://doi.org/10.1016/j.atmosenv.2019.01.009, 2019. Steeneveld, G.-J., Horst, S. van der, and Heusinkveld, B.: Observing the surface radiation and energy balance, carbon dioxide and methane fluxes over the city centre of Amsterdam, Copernicus Meetings, https://doi.org/10.5194/egusphere-egu2020-1547, 2020. Velasco, E., Pressley, S., Grivicke, R., Allwine, E., Molina, L. T., and Lamb, B.: Energy balance in urban Mexico City: observation and parameterization during the MILAGRO/MCMA-2006 field campaign, Theor Appl Climatol, 103, 501–517, https://doi.org/10.1007/s00704-010-0314-7, 2011. Velasco, E., Roth, M., Tan, S. H., Quak, M., Nabarro, S. D. A., and Norford, L.: The role of vegetation in the CO2 flux from a tropical urban neighbourhood, Atmospheric Chemistry and Physics, 13, 10185–10202, https://doi.org/10.5194/acp-13-10185-2013, 2013. Velasco, E., Perrusquia, R., Jiménez, E., Hernández, F., Camacho, P., Rodríguez, S., Retama, A., and Molina, L. T.: Sources and sinks of carbon dioxide in a neighborhood of Mexico City, Atmospheric Environment, 97, 226–238, https://doi.org/10.1016/j.atmosenv.2014.08.018, 2014. Ward, H. C., Evans, J. G., and Grimmond, C. S. B.: Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmospheric Chemistry and Physics, 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, 2013.

opencc-by-4.0Dec 2021View details →

zenodo44/100

Supplementary material 2 from: Bayliss H, Stewart G, Wilcox A, Randall N (2013) A perceived gap between invasive species research and stakeholder priorities. NeoBiota 19: 67-82. https://doi.org/10.3897/neobiota.19.4897

Journal article classifications (doi: 10.3897/neobiota.19.4897.app2) File format: Comma Separated Value File (csv).:

opencc-by-4.0Oct 2013View details →

zenodo44/100

Supplementary material 1 from: Bayliss H, Stewart G, Wilcox A, Randall N (2013) A perceived gap between invasive species research and stakeholder priorities. NeoBiota 19: 67-82. https://doi.org/10.3897/neobiota.19.4897

Stakeholder priorities. (doi: 10.3897/neobiota.19.4897.app1) File format: Micrisoft Comma Separated Value File (csv).:

opencc-by-4.0Oct 2013View details →

zenodo44/100

Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations

Photos and metadadata pannels of a "Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations" produced to support the "A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations" presentation given at EGU 2024. ------ With ever growing abilities to process greater volumes of data the abiiity to sustain the citability and tracability of the underluing source data within outputs such as publications is becoming increasingly challenging. With a range of use-cases, work on how to handle complex citations from the perspective of those producing outputs, journals and those handling the knowledge graph and associated services, is exmaning a how to handle these situations in a sustainable and manageable fashion. At the European Geophysical Union (EGU) General Assembly in Vienna, 2024, a pragmatic solution using Zenodo to store 'reliquary' objects was presented. The poster presentation demonstrated the use of existing strucutres within a Zenodo object to address the complex citation use-case around figure, the related data and the source datasets related to the IPCC's AR5 figure data. I.e. how to utulise the existing constructs of a Zenodo item and the range of available metadata fields to give an off-the-shelf solution to allow tracability to the specific datasets used (via their Handle identifiers) and citability of the higher level, DOI-ed dataset collections within which the specific Handle-ed datasets were selected from. Additionally, the connectivity between these two levels of PID objects was also captured within the stored files around which the rich metata was captured. The concept of a complex citation 'reliquary' as a metadtata rich object, acting as a referencable nexus in the knowledge graph has been put forth as a solution to the complex citation challenge. It borrows the concept from its historical use, denoting a container or shrine, often richly embellished, for sacred relics (e.g. saints bones, artefacts etc). In the same way here we have both the rich metadata 'container' around the specific details (the 'bones in the box', with their preserved connectivity). However, the term 'reliquary' is often a hard one to convey, being somewhat of an obscure term (likewise the term 'nexus' may also be one lacking wider recogniton). Thus, to aid the discussions around the presentation by Pascoe et al. (2024) at the EGU 2023 General Assembly, a physical representation of a metadata reliquary object was produced. The purpose of this object was two fold:  - The first was to show how the reliquary container itself is metadata rich, detailing through the use of ORCIDS, RORs and a DOI, references to external items, complemented by further metadata concerning the specifics of the reliquary's own metadata (its title and the credit for the artist that created it). Futher more, the relationship between the reliquary and those referenced parties/objects was also captured. The contents were also used to demonstrate the importance of making the contents useful for onward users (in this case contact details on business cards).  - The second, and for the funder of this piece, arguably the most important aspect was a degree of outreach this provided, both to engage the audience of Pascoe et al (2024), and directly to the artist to demonstrate the importance of this work to the international research data management community and overall to aid engagemeng with the funder's work. This resource is provided here as a repository of images of the reliquary itself and in context at the EGU 2024 event as a potential resource others may use to aid further discussions around the use of reliquaries with regards to complex citations. The slides provided of the reliquary box labels are also provided with some annotation to further expand on the metadata aspects of their content.

opencc-by-4.0Apr 2024View details →

zenodo44/100

Multi-Temporal Cloud Gap Imputation With HLS Data Across CONUS

This release contains the version 1.0 of the dataset which was used in <a href="https://arxiv.org/abs/2404.19609">Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model</a> and is included as one of the tasks in the <a href="https://madewithclay.org/challenge">AI for Earth Challenge 2024</a>.

opencc-by-4.0May 2024View details →

ScienceDex guides

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

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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

Find research datasets worth reusing

Dataset results

TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements

Considerable gaps in our global knowledge of potential groundwater accessibility

Emissions gap NDC and net zero findings after COP27

GAPs Data Repository on Return: Guideline, Data Samples and Codebook

Daily 1-km gap-free PM2.5 grids in China, v1 (2000–2020)

Daily 1-km gap-free PM10 grids in China, v1 (2000–2020)

Daily 1-km gap-free AOD grids in China, v1 (2000–2020)

Annual mean 1-km gap-free AOD, PM2.5, and PM10 grids in China, v1 (2000–2020)

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

DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region

Smooth numbers in large prime gaps

Periodic Hydraulic Testing Dataset for "Borehole-based fracture unclogging experiment: bridging the gap between laboratory- and field-scale evidence (FRANC)"

An 18.9-minute Blue Large-Amplitude Pulsator Crossing the ''Hertzsprung Gap'' of Hot Subdwarfs

Raw data for the article "Size-Dependent Structural Alterations in Ag Nanoparticles During CO2 Electrolysis in a Gas-Fed Zero-Gap Electrolyzer"

Canopy gap sizes across the Brazilian Amazon

Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project

Supplementary material 2 from: Bayliss H, Stewart G, Wilcox A, Randall N (2013) A perceived gap between invasive species research and stakeholder priorities. NeoBiota 19: 67-82. https://doi.org/10.3897/neobiota.19.4897

Supplementary material 1 from: Bayliss H, Stewart G, Wilcox A, Randall N (2013) A perceived gap between invasive species research and stakeholder priorities. NeoBiota 19: 67-82. https://doi.org/10.3897/neobiota.19.4897

Reliquary of contacts for: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations

Multi-Temporal Cloud Gap Imputation With HLS Data Across CONUS

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

Your academic CV and NIH biosketch