Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Outputs from new LBA simulations. Part I: Baseline case

MIMICA model outputs for new LBA simulations. The case, model configuration and output files are all described in the attached TRANSITION_setup.pdf file. This is the first of a three part dataset containing only results from the baseline simulations (see below for a very quick overview of all data). Three sets of data outputs are available: One corresponding to the original, idealized LBA case, one in which cold pools have been suppressed by nudging temperature and moisture tendencies below cloud base(_nocp extension), and one in which the fixed surface fluxes have been replaced by interactive fluxes (_sst extension). For each set of simulations, five horizontal grid spacings were employed to test the sensitivity of the results to resolution (specified as file name extensions): 1.6 km (64 grid points), 800 m (128 grid points), 400 m (256 grid points), 200 m (512 grid points) and 100 m (1024 grid points). Finally, for each case, a series of various output files are available, including time series of domain averaged quantities (T_S), time series of horizontally averaged quantities (profiles_tot.nc), and two-dimensional horizontal slices extracted at various altitudes (slice_z_XXXX.nc). Note that all files present except T_S follow the NetCDF standard.

opencc-by-4.0Aug 2023View details →

PARSEC D(DO)MP. Project tracksheets: scholarly publications, non peer-reviewed digital outputs, dataset log, and software log.

This project outcome is allied to the Data and Digital Output Management Plan for the project (Stall et al. 2023). This resource contains templates for others to use when tracking their project outcomes and usages, accompanied by filled worksheets for the PARSEC project up until the date of version 2 (30 December 2023). The material provided is available as examplars from the PARSEC project and for use by others (excel templates) for the following categories: <ol> <li>Scholarly Publications </li> <li>Non Peer-Reviewed Digital Outputs (to include Datasets, Software, Conferences, Posters, Presentations, Training Materials, Workshop Materials, etc.)</li> <li>Datasets - working log of datasets reviewed for use by the project                                               </li> <li>Software - working log for software reviewed by the project (to include Models, Notebooks, Workflow, etc.)</li> </ol> V2 contains an update for non peer-reviewed digital outputs (2a) containing new and discovered outputs. Stall, Shelley, Specht, Alison, Corrêa, Pedro Luiz Pizzigatti, David, Romain, Edmunds, Rorie, Mabile, Laurence, Machicao, Jeaneth, Miyairi, Nobuko, Murayama, Yasuhiro, O'Brien, Margaret, Wyborn, Lesley, & Vellenich, Danton Ferreira. (2023). PARSEC Data and Digital Output Management Plan and Workbook. Zenodo. https://doi.org/<a href="https://doi.org/10.5281/zenodo.3891426">10.5281/zenodo.3891426</a>.

opencc-by-4.0Jul 2023View details →

Input parameters and output trajectory files for manuscript "Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CLPro: Insights from the Computational and Experimental Investigations"

Input parameters used for molecular dynamics simulations on GROMACS 2022 software and the output trajectory files for calculating binding free energy, in the manuscript with the title: "Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CLPro: Insights from the Computational and Experimental Investigations"

opencc-by-4.0Apr 2023View details →

Processed NEMO output investigating glider sampling strategies

This output is related to an investigation into glider sampling strategies. The data is generated from output from a 1/48o NEMO simulation of a patch of the north-eastern Weddell Sea. Each file corresponds to data required for reproducing figures included in an associated publication, where this data is also outlined. Verbose file naming is used for easier understanding of the file contents. See rdPatmore on GitHub for code used to produce and manipulate this data.

opencc-by-4.0Mar 2022View details →

The MOE Waste Input-Output table for Japan, 2011

The uploaded files provide supplementary data related to the journal article: Nakamura, Shinichiro. "Tracking the product origins of waste for treatment using the WIO data developed by the Japanese Ministry of the environment." Environmental Science & Technology 54, no. 23 (2020): 14862-14867. <a href="https://doi.org/10.1021/acs.est.0c06015">https://doi.org/10.1021/acs.est.0c06015</a> Please, refer to the abovementioned article, particularly the Supporting Information, for further description of the provided data.  Specifically, refer to Table S4 for the notation used to represent secondary waste items obtained post-treatment, such as through shredding.  For a waste item 'z,' the notation z(k) represents 'z' after undergoing treatment 'k,' as outlined below: (i): after intermediate treatment (d): after dehydration (c): after concentration (s): after shredding (f): after filtration

opencc-by-4.0Dec 2022View details →

The global distribution of plants used by humans datasets: list of utilised species, occurrence data and model outputs at 10 arc-minutes spatial resolution

Datasets and model outputs used to map the global distribution of utilised plants by humans. The folder is composed of two subfolders raw_data and processed_data containing respectively the list of utilised plant species modelled -utilised_plants_species_list.csv-, and their occurrence data -occurrence_data.zip- and predicted distribution -species_proba_per_cell.rds-.   <ul> <li>The file utilised_plants_species_list.csv in the raw_data folder contains a list of 35687 plant species (and hybrids) used by humans and 10 plant use categories with the following 14 fields:</li> </ul> plant_ID: plant identifier number ranging from between 1-35687 binomial_acc_name: binomial accepted name of the plant species author_acc_name:  name of the author(s) is_hybrid: logical TRUE or FALSE indicating whether the species is an hybrid or not. AnimalFood: forage and fodder for vertebrate animals only. EnvironmentalUses: examples include intercrops and nurse crops, ornamentals, barrier hedges, shade plants, windbreaks, soil improvers, plants for revegetation and erosion control, wastewater purifiers, indicators of the presence of metals, pollution, or underground water. Fuels: charcoal, petroleum substitutes, fuel alcohols, etc. Given the importance of energy plants for people, those were distinguished from Materials. GeneSources: wild relatives of major crops which may possess traits associated with biotic or abiotic resistance and may be valuable for breeding programs. HumanFood: food for humans only, including beverages and food additives. InvertebrateFood: plants consumed by invertebrates used by humans, such as bees, silkworms, lac insects and edible grubs. Materials: woods, fibers, cork, cane, tannins, latex, resins, gums, waxes, oils, lipids, etc. and their derived products. Medicines: both human and veterinary. Poisons: plants which are poisonous to both vertebrates and invertebrates, both accidentally and intentionally, e.g., for hunting and fishing, molluscicides, herbicides, insecticides. SocialsUses: plants used for social purposes, which cannot be defined as food or medicine, for instance, masticatories, smoking materials, narcotics, hallucinogens and psychoactive drugs, and plants with ritual or religious significance. Totals: total number of uses recorded for a species   <ul> <li>The zipfile occurrence_data.zip in the processed_data folder contains 35687 Comma Separated Values (CSV) files, one for each species, containing curated geographic occurrence records used to  build species distribution models with the following 14 fields:</li> </ul> Species: the binomial accepted name of the species Fullname:  same as species decimalLongitude: the geographic longitude of the occurrence records of the species in decimal degrees decimalLatitude: the geographic latitude of the occurrence records of the species in decimal degrees countryCode: a three-letter standard abbreviation for the country of the occurrence locality coordinateUncertaintyinMeters: indicator for the accuracy of the coordinate location, described as the radius of a circle around the stated point location year: year of the observation of the occurrence record of the species individualCount: the number of individuals present at the time of the observation gbifID: unique identifier number for the occurrence from the original database basisOfRecords: the type of the individual record, e.g. observation, physical specimen, fossil, living ex-situ, culture collection specimen institutionCode: the name of the institution or organization listed as the data publisher on GBIF establishmentMeans: statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans is_cultivated_observation: whether or not an organism is cultivated sourceID: name of the source database   <ul> <li>The file species_proba_per_cell.rds in the processed_data folder is a R Data Serialization (RDS) file containing a data.table object with the following 3 fields:</li> </ul> plant_ID: plant identifier number ranging from between 1-35687 proba: species occurrence probability cell: raster grid cell number between 1-2251762 This object can be used in combination with a raster layer to reconstruct the modelled distribution of each species or retrieve species richness and endemism.

dryad36/100

Raw data and outputs from: Sensory trap leads to reliable communication without a shift in nonsexual responses to the model cue

The sensory trap model of signal evolution suggests that males manipulate females into mating using traits that mimic cues used in a nonsexual context. Despite much empirical support for sensory traps, little is known about how females evolve in response to these deceptive signals. Female sea lamprey (Petromyzon marinus) evolved to discriminate a male sex pheromone from the larval odor it mimics and orient only towards males during mate search. Larvae and males release the attractant 3-keto petromyzonol sulfate (3kPZS), but spawning females avoid larval odor using the pheromone antagonist, petromyzonol sulfate (PZS), which larvae but not males, release at higher rates than 3kPZS. We tested the hypothesis that migratory females also discriminate between larval odor and the male pheromone and orient only to larval odor during anadromous migration, when they navigate within spawning streams using larval odor before they begin mate search. In-stream behavioral assays revealed that, unlike spawning females, migratory females do not discriminate between mixtures of 3kPZS and PZS applied at ratios typical of larval versus male odorants. Our results indicate females discriminate between the sexual and nonsexual sources of 3kPZS during but not outside of mating and show sensory traps can lead to reliable sexual communication without females shifting their responses in the original context.

opencc-zeroFeb 2024View details →

opencc-by-4.0Feb 2024View details →

Pieris napi microRNA mirDeep2 output pdfs

miRDeep2 pdf outputs for diapausing Pieris napi. Code used for miRNA identification and mapping (miRNA_identification_code.txt) Code for identification of nearest feature to miRNAs (closest_featuress_code.txt) Code for identifying differentially expressed genes and clustering (clustering.R) Code for cluster gene set enrichment analysis (GSEA_diapause_all.R) Code for ideification of differentially expressed genes in diapause termination (DEG_specific_comps.R)   From the paper "A time course analysis through diapause reveals dynamic temporal patterns of microRNAs associated with endocrine regulation in the butterfly Pieris napi"

opencc-by-4.0Oct 2023View details →

Model output from historical and future scenarios related to 'Carbon Dioxide Removal: Tradeoffs and Lags'

Open the record for dataset details and reuse information.

Plastic damage in sea ice - model outputs and observations.

Sea ice model outputs and observations analyzed in the paper: "On the sensitivity of sea ice deformation statistics to plastic damage". For the outputs, there is one file every 6 hours (for January 2002). Each folder represents one experiment with the associated snapshot files each containing a matrix of the given variable. The observations are the 3-day gridded velocity derivatives. The files are numpy arrays (.npy) containing time slices for every 3-day period for January 2002.   1. Naming convention of the files Model outputs The folders are named according to the model configuration following the names used in (Savard and Tremblay, 2024): "Model name" + ( + "configuration" + ) The files in each configuration follow the following convention: "Variable" + "year" + _ +  "month" + _ + "day" + _ + "hour" + _ + "minutes" + . + "experiment number" There is also an info file, which provides the numerical parameters used in each run: info + . + "experiment number" Observations The files are the velocity derivatives: "partial derivative" + .npy There is also a mask for the period of January-February-March: mask80JFM.npy   2. Variables included <ul> <li>u,v : sea ice velocity in the x,y directions (in m/s);</li> <li>A: ice concentration per grid cell (0 -- 1);</li> <li>h: mean ice thickness per grid cell (in m);</li> <li>dam: plastic damage per grid cell (0 -- 1); </li> <li>div: sea ice divergence per grid cell (in s^-1);</li> <li>shear: sea ice shear per grid cell (in s^-1).</li> </ul>   4. Recommended citation usage If any or all simulations included in the current archive are used in a future study, we ask to cite this archive and the plastic damage paper (Savard and Tremblay, 2024). If the observations provided here are used in a future study, we ask to cite this archive and the original paper in which the data is used (Kwok, 1997). The original RGPS 3-day gridded dataset can be accessed here: https://asf.alaska.edu/data-sets/derived-data-sets/seaice-measures/sea-ice-measures-data-products/

Modelling outputs of the AWESOME water supply model of the Nile River Basin

This folder contains the output of the water supply model as trajectories for the simulated storage levels and water release for the Grand Ethiopian Renaissance Dam, Merowe Dam, and High Aswan Dam. The trajectories are simulated under both historical climatic and water demand scenarios, as well as for future climatic and demographic scenarios (RCP4.5 + SSP2). h_GERD_ : Grand Ethiopian Renaissance Dam water level h_MER_ : Merowe Dam water level h_HAD_ : High Aswan Dam water level r_GERD_ : Water release from Grand Ethiopian Renaissance Dam r_HAD_ : Water release from High Aswan Dam BestHP_hist: Best performance for hydropower production at the basin level under historical conditions of water availability and demand  BestEgypt_hist: Best performance for irrigation deficit in Egypt under historical conditions of water availability and demand  BestSudan_hist: Best performance for irrigation deficit in Sudan under historical conditions of water availability and demand LowReduction_hist: Low water reduction solution under historical conditions of water availability and demand  MediumReduction_hist: Best performance for irrigation deficit in Egypt under historical conditions of water availability and demand  LowReduction_2050: Low water reduction solution under climatic (RCP4.5) and demographic projection (SSP2) in 2050  MediumReduction_2050: Best performance for irrigation deficit in Egypt under climatic (RCP4.5) and demographic projection (SSP2) in 2050

Output data from "Emissions leakage and economic losses could limit the effectiveness of deforestation-linked oil crop import restrictions"

Contains a .dat file with queried output from scenario runs used in the paper, Yarlagadda, B., X. Zhao, G. Iyer, T. Wild, N. Hultman and J. Lamontagne, "Emissions leakage and economic losses could limit the effectiveness of deforestation-linked oil crops". This output can be run with the workflow in <a href="https://github.com/brinday/oilcrop-trade-deforestation-leakage">https://github.com/brinday/oilcrop-trade-deforestation-leakage</a> to generate the figures presented in the paper.

Hybracter v0.7.0 Benchmarking Output

This dataset contains: <ol> <li>The subsampled FASTQ files used to benchmarking Hybracter (https://github.com/gbouras13/hybracter).</li> <li>Benchmarking Output for Hybracter v0.7.0 vs Unicycler v0.5.0 vs Dragonflye v1.1.2 on these files.</li> </ol> The full benchmarking code and explanation is available https://github.com/gbouras13/hybracter_benchmarking.  The `hybracter_benchmarking_fastqs.tar.gz` tarball will contain subsampled FASTQs (gzipped) of the first 20 samples used to benchmarking `hybracter`. These are the JKD6159, Lerminiaux, Chitale and super-accuracy model basecelled simplex ATCC fastqs. The `PRJNA1087001_ATCC_SUP_Duplex_FAST_Simplex_fastqs.tar.gz` tarball will contain subsampled FASTQs (gzipped) of the 10 added samples in v2 of the prepint used to benchmarking `hybracter`. These are the fast model basecelled simplex ATCC fastqs and super-accuracy model basecelled duplex ATCC fastqs. The other 4 tarballs ( `hybracter_benchmarking_results_v0.7.0.tar.gz`, `hybracter_benchmarking_results_fast.tar.gz`, `hybracter_benchmarking_results_duplex.tar.gz` and `hybracter_depth_Lerminiaux_isolateB_benchmarking_results.tar.gz`) contain benchmarking outputs for the first 20 samples, 5 fast model basecelled simplex ATCC samples, 5 super-accuracy model basecelled duplex ATCC and the depth analysis for Lerminiaux isolate B. The when untared, each tarball will contain: <ul> <li>`BENCHMARKS` - contains the time etc benchmarking for each run (sample x tool)</li> <li>`DNADIFF` - contains raw chromosome Dnadiff results for each run (sample x tool)</li> <li>`DNADIFF_PARSED_OUTPUT` - contains parsed chromosome Dnadiff results for each sample</li> <li>`DNADIFF_PLASMIDS` - contains plasmid Dnadiff results for each run (sample x tool)</li> <li>`DNADIFF_PARSED_OUTPUT_PLASMID` - contains parsed plasmid Dnadiff results for each sample</li> <li>`REAL` - this contains all the actual output for each assembler. The following 5 directories will contain the all the raw output with subdirectories for each sample: <ul> <li>`HYBRACTER_HYBRID_OUTPUT`</li> <li>`HYBRACTER_LONG_OUTPUT`</li> <li>`DRAGONFLYE_HYBRID_OUTPUT`</li> <li>`DRAGONFLYE_LONG_OUTPUT`</li> <li>`UNICYCLER_OUTPUT`</li> <li>Additionally, `hybracter_benchmarking_results_v0.7.0.tar.gz` will have `HYBRACTER_HYBRID_OUTPUT_REAL_BULK` - this contains the output for the 12 Lerminiaux et al isolates assembled using `hybracter hybrid` with modified config file `bulk_assemble_lerminiaux_config.yaml`.</li> </ul> </li> <li>It will also contain a number of other subdirectories `_SUMMARIES`, `_PLASMIDS`, `_CHROMOSOMES` with parsed summary outputs and parsed specific plasmids and chromosome assemblies for Unicycler and Dragonflye (this made the assessment a lot easier and automated).</li> </ul> To untar e.g. `tar -xzf hybracter_benchmarking_results_v0.7.0.tar.gz`

Derks et al. 2022 (Nat. Biotechnol.) - plexDIA DIA-NN report.tsv output

Single cell proteomics data acquired by the Slavov Lab using the plexDIA protocol. It contains quantitative information from pancreatic ductal acinar cells (PDAC; HPAF-II), melanoma cells (WM989-A6-G3) and monocytes (U-937) at precursor and protein level. The each run acquired 3 samples thanks to mTRAQ multiplexing. The data were downloaded from: <a href="https://drive.google.com/drive/folders/1pUC2zgXKtKYn22mlor0lmUDK0frgwL_-">https://drive.google.com/drive/folders/1pUC2zgXKtKYn22mlor0lmUDK0frgwL_-</a>  -> DIANN_outputs -> wJD1146_1193_1200_tsvLib -> Report.tsv The file is reshare here allow its dissemination via the <a href="https://bioconductor.org/packages/release/bioc/html/MsDataHub.html">MsDataHub</a> package.

ELMv1 and ELM-Wet Output Datasets

Open the record for dataset details and reuse information.

Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves -> Model outputs

This repository contains MITgcm outputs associated with the paper "Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves", submitted to the Journal of Geophysical Research: Oceans.  The outputs are presented in netCDF format and come from two simulations - GREEN, with chlorophyll, and BLUE, without chlorophyll affecting shortwave heating. <ul> <li>Temperature and salinity <ul> <li>green_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>green_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>green_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> <li>blue_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>blue_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>blue_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> </ul> </li> <li>Sea ice <ul> <li>ice_green.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li>sit_green.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> <li>ice_blue.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li> sit_blue.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> <li>Ice shelves <ul> <li>green_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> <li>blue_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> </ul> </li> <li>Surface heat fluxes <ul> <li>tflux_green.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_green.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_green.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_green.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_green.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the GREEN experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>tflux_blue.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_blue.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_blue.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the BLUE experiment </li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_blue.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_blue.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the BLUE experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> </ul> </li> <li>BLING <ul> <li>chlorophyll.nc <ul> <li>2-dimensional monthly fields of surface chlorophyll in the GREEN experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: mg/m^3</li> </ul> </li> <li>euphotic_depth.nc <ul> <li>2-dimensional monthly fields of euphotic depth in the GREEN experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> </ul>

Raw data of surface texture parameter values (incl. output of statistical tests).

This is the raw data (supplement 3) of the publication with the title "Prey size reflected in tooth wear – a comparison of two wolf populations from Sweden and Alaska" written by: Ellen Schulz-Kornas, Mirella H. Skiba and Thomas M. Kaiser and accepted for publication as manuscript RSFS-2023-0070.R1 in the journal Interface Focus on 2-April 2024.

GFDL AM2.1 Model Output -Changes in Monsoon Meteorological Elements Due to the Warming of the Tropical Indian Ocean

Changes in Climatological Mean Stream Function, Eddy Flux, Wind Field, Moisture Flux, and Precipitation from May to September Due to the Warming of the Indian Ocean

ATAC-seq raw reads, MACS3 outputs

The dataset contains raw reads that were mapped to the nuclear genome and all outputs of MACS3 in <a href="https://doi.org/10.5281/zenodo.10972575">10.5281/zenodo.10972575</a>

Outputs of NO2 from GEOS-Chem v13.4.0

These ncfiles contain ground and total column NO2 data simulated from GEOS-Chem v13.4.0 in China. They are utilized in the preprint "Observational operator for fair model calibration with ground NO2 measurements" (https://doi.org/10.5194/gmd-2023-216) to ensure reproducibility.