Find research datasets worth reusing

openCC0May 2022View details →

Mohonk Preserve Forest Health Monitoring Data 2018-2021

In 2018, the Mohonk Preserve’s Daniel Smiley Research Center implemented a long-term research project aimed at inventorying forest vegetation and monitoring forest health. The protocol was adapted from the National Park Service’s Northeast Temperate Network (https://www.nps.gov/im/netn/forest-health.htm). This project monitors the composition and structure of the Mohonk Preserve forests, and collects data for assessing forest soil condition, impacts of white-tailed deer herbivory, and land cover. In 2018, 24 plots were established in four habitat types: Eastern hemlock forest (n = 6), white ash forest (n = 6), historic prescribed burn forest (n = 6), and randomly selected forest (n = 6). In 2021, an additional 14 plots were established in two historic Breeding Bird Survey research areas: Eastern hemlock forest (n = 8) and pitch pine forest (n = 6). All data collection occurred between the months of June through August. Plots are scheduled to be resampled every four years.

openCC (other)May 2025View details →

National Park Service - South Florida/Caribbean Inventory & Monitoring Network - SARI SET Surface Water level data from Salt River Bay National Historical Park and Ecological Preserve, St. Croix, US Virgin Islands.

Surface water level data (m) was collected in Salt River Bay National Historic Park and Ecological Preserve (SARI) by the South Florida/Caribbean Inventory and Monitoring Network (SFCN) as part of the Soil Elevation Table (SET) vital sign monitoring program. Water level data collected from 2017 to 2024 is included in this dataset. The water level data was collected using HOBOware Onset Water Level Data Loggers. This data-package is complete.

openCC (other)Dec 2025View details →

Microplastic Abundance, Shape, and Color in Passerines Captured at Rushton Woods Preserve Bird Banding Station in Newtown Square, Pennsylvania, USA, April-September 2024

Fecal samples were collected from 5 species of passerine birds between April and September 2024 at the Rushton Woods Preserve Bird Banding Station. Samples were chemically digested and filtered for the purpose of extracting, quantifying, and describing microplastics.

openCC0Jul 2021View details →

Long-term monitoring of ground-dwelling arthropods in the McDowell Sonoran Preserve, Scottsdale, Arizona, ongoing since 2012 (Reformatted to a Darwin Core Archive)

This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/248/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/643/3. The abstract below was extracted from the Level 0 data package and is included for context:

openOpenNov 2021View details →

Toklat River Fire in Denali National Park and Preserve: Site level environmental, soil, tree, vegetation, and fire characteristics measured in 2016

This dataset contains site-level average estimated of environmental, soil, tree, vegetation, and fire characteristics measured in 2016, three years after the Toklat River Fire in Denali National Park and Preserve. Measured parameters include latitude, longitude, slope, aspect, elevation, moisture classification, bulk density of the surface soil, residual organic soil depth, thaw depth, burn depth, density and basal area of all tree species pre-fire, the density of all tree species post-fire, estimates of above- and below-ground carbon combustion, and understory vegetation turnover from pre-fire to post-fire. There is also data on seed trap collection and experimental regeneration of seedlings collected in 2017 and 2018 at a subset of sites.

opencc-by-4.0Jan 2022View details →

Static Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries

# About this artifact This artifact contains the implementation and the results of the evaluation of a static slicer for WebAssembly described in the ICSE 2022 paper titled "Static Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries". The artifact contains a docker image (`wassail-eval.tar.xz`) that contains everything necessary to reproduce our evaluation, and the actual data resulting from our evaluation: 1. The implementation of our slicer (presented in Section 4.1) is included in    the docker machine, and is available publicly here:    https://github.com/acieroid/wassail/tree/icse2022 2. Test cases used for our evaluation of RQ1 are included in the docker machine    and in the `rq1.tar.xz` archive. 3. The dataset used in RQ2, RQ3, and RQ4 is included in the docker machine. 4. The code needed to run our evaluation of RQ2, RQ3, and RQ4 is included in the    docker machine. 5. The scripts used to generate the statistics and graphs that are included in    the paper for RQ2, RQ3, and RQ4 are included in the docker machine and as the    `*.py` files in this artifact. 6. The data of RQ5 that has been used in our manual investigation is included in    the docker machine and in the `rq5.tar.xz` archive, along with    `rq5-manual.txt` detailing our manual analysis findings. # How to obtain it Our artifact is available on Zenodo at the following URL: https://zenodo.org/record/5821007 # Setting up the Docker image ## Downloading The Artifact The artifact is available at the following URL: https://zenodo.org/record/5821007 ## Loading The Docker Image Once the artifact is downloaded in the file `icse2022slicing.tar.xz`, it can be extracted and loaded into Docker as follows (this takes a few minutes): ``` docker import icse2022slicing.tar.xz ``` To simplify further commands, you can tag the image using the printed sha256 hash of the image: if the `docker import` command resulted in the hash `54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2`, you can run: ``` docker tag 54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2 wassail-eval ``` Once the Docker image has been loaded, you can run the following commands to obtain a shell in the appropriate environment: ``` docker volume create result docker run -it -v result:/tmp/out/ wassail-eval bash su - opam ``` # Reproducing results of RQ1 Our manual translations of the "classical" examples are included in the `rq1/` directory (available in the docker image and in `rq1.tar.xz`). We include the slices computed by our implementation in the `rq1/out/` directory. A slice can be produced for each example in the docker image as follows, where the first argument is the name of the program being sliced, the second the function index being sliced, the third the slicing criterion (indicated as the instruction index, where instructions start at 1), and the last argument is the output file for the slice: ``` cd rq1/ wassail slice scam-mug.wat 5 8 scam-mug-slice.wat wassail slice montreal-boat.wat 5 19 montreal-boat-slice.wat wassail slice word-count.wat 1 41 word-count-slice1.wat wassail slice word-count.wat 1 43 word-count-slice2.wat wassail slice word-count.wat 1 39 word-count-slice3.wat wassail slice word-count.wat 1 45 word-count-slice4.wat wassail slice word-count.wat 1 37 word-count-slice5.wat wassail slice agrawal-fig-3.wat 3 38 agrawal-fig-3-slice.wat wassail slice agrawal-fig-5.wat 3 37 agrawal-fig-5-slice.wat ``` The slice results can then be inspected manually, and compared with the original version of the .wat program to see which instructions have been removed, or with the expected solutions in the `out/` directory, e.g. by running: ``` diff word-count-slice1.wat out/word-count-slice1.wat ``` (No output is expected if the slice is correct) # Reproducing results of RQ2, RQ3, and RQ4 For these RQ, we include the data resulting from our evaluation, but we also allow reviewers to rerun the full evaluation if needed. However, such an evaluation requires a heavy machine and takes quite some time (4-5 days to run to completion with a 4 hours timeout). In our case, we used a machine with 256 GB of RAM and a 64-core processor with HyperThreading enabled, allowing us to run 128 slicing jobs in parallel. ## Runnig the Evaluation We explain how to run the full evaluation, or only a partial evaluation below. One can directly skip to the next section and reuse our raw evaluation results, provided alongside this artifact. ### Running the Full Evaluation In order to reproduce our evaluation, you can run the following commands in the docker image. It is recommended to run them in a tmux session if one wants to inspect other elements in parallel (tmux is installed in the docker image). The timeout (set to 4 hours per binary, like in the paper) can be decreased by editing the `evaluate.sh` script (vim is installed in the docker image). This is expected to take 2-3 days of time, on a machine with 128 cores. In order to produce only partial results, see the next section. ``` cd filtered cat ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {} ``` The results are outputted in the `/tmp/out/` directory. ### Running a Partial Evaluation If one does not have access to a high-end machine with 128 cores nor the time to run the full evaluation, it is possible to produce partial results. To do so, the following commands can be run. This will run the evaluation on the full dataset in a random order, which can be stopped early to represent a partial view of our evaluation, on a random subset of the data. In order to gather more datapoints, it is also advised to decrease the timeout in the `evaluate.sh` file, for example to 20 minutes by setting `TIMEOUT=20m` with `nano evaluate.sh`. The number of slicing jobs running in parallel can also be decreased to match the number of processors on the machine running the experiments (the `-j 128` argument in the following command runs 128 parallel jobs) ``` sudo chown opam:opam /tmp/out/ cd filtered shuf ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {} ``` The evaluation results will be stored in the `/tmp/out/` directory. ### Skipping the Evaluation Run Instead of rerunning the evaluation, one can rely on our full results included in the `data.txt.xz` and `error.txt.xz` archives. These can simply be downloaded from within the Docker machine and extracted in `/tmp/out/`: ``` cd /tmp/out/ wget https://zenodo.org/record/5821007/files/data.txt.xz wget https://zenodo.org/record/5821007/files/error.txt.xz unxz data.txt.7z unxz error.txt.7z ``` ## Processing the data In order to process this data, we included multiple python script. These require around 100GB of RAM to load the full dataset in memory. The scripts should be run with Python 3. When running this in the docker image, first run `cd /tmp/out/ && cp /home/opam/*.py ./` - To count the number of functions sliced, run `cut -d, -f 1,2 data.txt | sort   -u | wc -l`. This takes around 6 minutes to run on the full dataset. - To count the total number of slices encountered, run `wc -l data.txt   error.txt`. This takes around 15 seconds to run. - To count the number of errors encountered, run `wc -l error.txt`. This takes   around 1 second to run. - To produce data and graphs regarding the sizes and timing, run `python3   statistics-and-plots.py`. This will output the statistics presented in the   paper, along with Figure 2 (rq2-sizes.pdf) and Figure 3 (rq2-times.pdf). This   script takes around 35 minutes to run. - To find the executable slices that are larger than the original programs, run   `python3 larger-slices.py > larger.txt`. This script takes around 2h30 to   run. It will list the slice using the notation `filename function-sliced   slicing-criterion` in the larger.txt file, from which the slice can be   recomputed by running `wassail slice function-sliced slicing-criterion   output.wat` in the docker image. It will also output statistics regarding   these slices, which you can easily inspect by running `tail larger.txt`. - To investigate slices that could not be computed, run:   ```   sed -i error.txt -e 's/annotation,/annotation./'   python3 errors.py   ```   This will take a few seconds to run and will print a summary of the errors   encountered during the slicing process, and requires some manual sorting to map   to the categories we discuss in the paper. Here is a summary of the errors   encountered and their root cause: ### Root Cause: Unsupported Usage of br_table Error: (Failure"Invalid vstack when popping 2 values") Error: (Failure"Spec_inference.drop: not enough elements in stack") Error: (Failure"Spec_inference.take: not enough element in var list") Error: (Failure"unsupported in spec_inference: incompatible stack lengths (probably due to mismatches in br_table branches)") ### Root Cause: Unreachable Code Error: (Failure"Unsupported in slicing: cannot find an instruction. It probably is part of unreachable code.") Error: (Failure"bottom annotation") Error: (Failure"bottom annotation. this an unreachable instruction") # RQ5: Comparison to Slicing C Programs For this RQ, we include the following data in the `rq5.7z` archive, and in the `rq5/` directory in the docker image: - The slicing subjects in their C and textual wasm form in `rq5/subjects/` - The CodeSurfer slices in their C and textual wasm form in `rq5/codesurfer/` - Our slices in their wasm form in `rq5/wasm-slices/` As this RQ requires heavy manual comparison, we do not expect the reviewers to reproduce all of our results. We include a summary of our manual investigation in `rq5-manual.txt`. In order to validate these manual findings, one can for example inspect a specific slice. For example, the following line in `rq5-manual.txt`: ``` adpcm_apl1_565_expr.c.wat INTERPROCEDURAL ``` can be validated as follows: ``` cd ~/ # This generates a trimmed down version of the CodeSurfer slice, only containing the function of interest wassail count-in-slice rq5/codesurfer/adpcm_slices/adpcm_apl1_565_expr.c.wat slice.wat # This compares the CodeSurfer slice with our slice diff --side-by-side slice.wat rq5/adpcm_apl1_565_expr.c.wat ``` In this case, most extraneous instructions are present in the CodeSurfer slices, at the end of the function. This indicates that these are present in order to preserve interprocedural behavior, which corresponds to the `INTERPROCEDURAL` tag in the `rq5-manual.txt`

opencc-by-4.0May 2024View details →

CVoiceFake (crafted by SafeEar: Content Privacy-Preserving Audio Deepfake Detection)

<h1>Introduction:</h1> CVoiceFake (small) is a dataset that features a random selection of 10% of samples from the entire collection. This dataset encompasses five common languages (English, Chinese, German, French, and Italian) and utilizes multi-advanced and classical voice cloning techniques (Parallel WaveGAN, Multi-band MelGAN, Style MelGAN, Griffin-Lim, WORLD, and DiffWave) to produce audio samples that bear a high resemblance to authentic audio. <ol> <li>Parallel WaveGAN: As a non-autoregressive vocoder-based model, Parallel WaveGAN produces high-fidelity audio rapidly, ideal for efficient and quality deepfake generation.</li> <li>Multi-band MelGAN: Multi-band MelGAN is a variant of MelGAN that divides the frequency spectrum into sub-bands for faster and more stable multi-lingual vocoder training, enhancing the robustness and scalability of the dataset.</li> <li>Style MelGAN: Style MelGAN is designed to capture fine prosodic and stylistic nuances of speech, making it particularly compelling for deepfake applications that require high levels of expressivity and variation in speech synthesis.</li> <li>Griffin-Lim: This algorithm reconstructs waveforms from spectrograms using an iterative phase estimation method. Though less high-fidelity than neural vocoders, it serves as a traditional baseline for comparing deepfake generation.</li> <li>WORLD: WORLD is a statistical parameter-based voice synthesis system that offers fine control over the spectral and prosodic features of the synthesized audio. Its fine manipulation is useful for crafting the nuanced variations needed in deepfake datasets.</li> <li>We have also built the SOTA diffusion-based deepfake audio (DiffWave); please contact the author at <code>xinfengli@zju.edu.cn</code> if you are interested in the dataset, particularly the DiffWave portion. Furthermore, any additional discussions are welcomed. DiffWave: DiffWave is a diffusion probability model for waveform generation. It converts the white noise signal into structured waveform through a Markov chain, capable of both conditional and unconditional generation tasks. DiffWave represents the advanced synthesis method for its fast synthesis speed and high synthesis quality.</li> </ol> <h1>🔥News:</h1> Please note that we recently released our DiffWave subset in Version 2 in comparison to Version 1, which is available on <a href="../records/14062964" target="_blank" rel="noopener">CVoiceFake Full</a>. You can download the file named CVoiceFake_Large_diffwave_update.tar.gz.xx, and after unzipping it, you will find it retains the same file structure as before.   | CVoiceFake_Large_diffwave_update.tar.gz.00 |   | CVoiceFake_Large_diffwave_update.tar.gz.01 |   <h1>Full Dataset & Project Page:</h1> The whole dataset is available on <a href="../records/14062964" target="_blank" rel="noopener">CVoiceFake Full</a> as well. Please kindly also refer to the project page: <a title="SafeEar Website" href="https://safeearweb.github.io/Project/" target="_blank" rel="noopener">SafeEar Website</a>.   <h1>Citation:</h1> If you find our paper/code/benchmark helpful, please kindly consider citing this work with the following reference: <pre><code>@inproceedings{li2024safeear,   author       = {Li, Xinfeng and Li, Kai and Zheng, Yifan and Yan, Chen and Ji, Xiaoyu, and Xu, Wenyuan},   title        = {{SafeEar: Content Privacy-Preserving Audio Deepfake Detection}},   booktitle    = {Proceedings of the 2024 {ACM} {SIGSAC} Conference on Computer and Communications Security (CCS)}   year         = {2024}, } </code></pre> <div> <div> </div> </div>

opencc-by-4.0Apr 2019View details →

Dataset to "Persistent Identifiers for File Formats: enabling preservation and re-use of research data"

This fileset includes a "preprint" and the main dataset fileformatRecognizer (as .xlsx and .csv) to the paper "Persistent identifiers for file formats: enabling preservation and re-use of research data" submitted to iPRES 2019, but subsequently rejected after peer review.  For the sake of transparency, permission to make available here the anonymous reviews motivating the rejection (ReviewsPIDs4fileFormats.odt) was asked, but was left without response. Some images (screendumps) and text result files from file identification tools tested are included. Further, a simple xquery command file (BaseX) for fetch:content-type(), used for getting MIME-types for files, is also provided.

opencc-by-4.0May 2024View details →

Graphic Illustration of Verity Mathis' Talk: Virome composition in fresh bat guano, frozen and fluid-preserved bat tissues

<a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Verity Mathis at an NSF-supported Workshop:  Digital Collections Data and Tracking Disease.

edi48/100

Mohonk Preserve Ground Water Springs Data, 1930 to Present

In a region strongly affected by acid precipitation, the Mohonk Preserve's Daniel Smiley Research Center, has been monitoring the monthly pH and temperature of eight ground springs (Chestnut, Rock, Mohonk, Rhododendron, Mossy Brook Bank, Mossy East Box, Mossy West Box, Rock Rift) on the Preserve dating as far back as 1930 to present day. Monitoring before January 1991 was intermittent and opportunistic but starting in 1991 sampling became more consistent. Monitoring of dissolved oxygen, nitrate concentrations, and conductivity of the springs using a YSI Sonde Professional Plus Instrument began in November 2017.

openCC0Dec 2024View details →

edi48/100

Mohonk Preserve Stream Water Quality Invasive Species and Macroinvertebrate Sampling in from 2017-Present

The mission of the Mohonk Preserve is to protect the Shawangunk Mountains region and inspire people to care for, enjoy, and explore their natural world. Among these 8,000 acres are the vernal pools, permanent springs, tributaries, Humpo Marsh, and the Humpo Kill, and parts of the Kleine Kill and Coxing Kill watersheds within the Hudson River Drainage Basin. Not only are the areas around the Shawangunks established habitats for New York State (NYS) protected species, including an Audubon-designated Important Bird Area, but the watershed also encapsulates more than one agricultural land use area, as well as Rondout Creek, which is an important waterway for the New York City water supply. A conservation plan must be implemented in these areas in particular, keeping in line with the Mohonk Preserves goal to conserve the Shawangunk region for both humans and the greater ecosystem within it. Recognizing the immediate and long-term conservation needs of the streams in this region by employing volunteer data collection will be a catalyst to the Preserves understanding of which environmental threats of this area should be prioritized. The StreamWatch citizen science program will be the newest addition to an array of volunteer research areas, which include collection of weather data, phenology observations, monitoring of peregrine falcon breeding activities, and monitoring of fall hawk migration. Using concise stream monitoring protocol designed for volunteer safety and maximum data accuracy, StreamWatch will evaluate water quality using an array of parameters. Following thorough observation and assessment (which will include analyzing appearance and smell of the water, shape of the stream, canopy cover, nearby land uses, recent weather, and presence of riparian vegetation including invasive species) water quality will be evaluated by means of temperature, dissolved oxygen, pH, and turbidity measurements, in addition to a macroinvertebrate count. Width and depth will also be

openCC0Feb 2020View details →

edi48/100

CBS05 Estimates of vegetation structure and composition collected on Konza Prairie watersheds and on the nearby Rannell’s Preserve

Data set includes estimates of vegetation structure and composition collected during ~monthly sampling events on Konza Prairie watersheds and on the nearby Rannell’s Preserve. Vegetation data were collected from three (prior to 2017) or 10 randomly-selected locations on each watershed; two from outside the 10-ha plot (see project abstract) and one inside the plot. We sampled vegetation on each watershed once a month, during May, June, and July. Additional vegetation data were collected from bird nest sites within ~3 days of nests failing. We used 5 sets of Daubenmire frame measures to determine percent cover of major plant functional groups (at the center of the plot and 5 m from center at the 4 cardinal directions). We estimated visual obstruction by placing a Robel Pole in the middle, and 5 m from the middle of the plot in each of the 4 cardinal directions. For each pole placement, we stood 4 m away with eye 1 m above the ground in each of 4 directions, and counting the highest 5-cm segment not completely obscured by vegetation. At nests, we also estimated the slope and aspect in the center of each plot.

openCC0Apr 2023View details →

opencc-by-4.0Apr 2020View details →

figure data for "Radiation environment and doses on Mars at Oxia Planum and Mawrth Vallis: support for exploration at sites with biosignature preservation potential", by F. Da Pieve, G. Gronoff, J. Guo et al (2020)

The data are the tabular format of the plots in figures 2-7 of the paper submitted.

opencc-by-4.0Mar 2024View details →

Integrated Preservation of Water Activity as Key to Intensified Chemoenzymatic Synthesis of Bio-Based Styrene Derivatives

The valorization of lignin-derived feedstocks by catalytic means enables their defunctionalization and upgrading to valuable products. However, the development of productive, safe, and low-waste processes remains challenging. This paper explores the industrial potential of a chemoenzymatic reaction performing the decarboxylation of bio-based phenolic acids in wet cyclopentyl methyl ether (CPME) by immobilized phenolic acid decarboxylase from Bacillus subtilis, followed by a base-catalyzed acylation. Key-to-success is the continuous control of water activity, which fluctuates along the reaction progress, particularly at high substrate loadings (triggered by different hydrophilicities of substrate and product). A combination of experimentation, thermodynamic equilibrium calculations, and MD simulations revealed the change in water activity which guided the integration of water reservoirs and allowed process intensification of the previously limiting enzymatic step. With this, the highly concentrated sequential two-step cascade (400 g·L–1) achieves full conversions and affords products in less than 3 h. The chemical step is versatile, accepting different acyl donors, leading to a range of industrially sound products. Importantly, the finding that water activity changes in intensified processes is an academic insight that might explain other deactivations of enzymes when used in non-conventional media.

opencc-by-4.0Oct 2023View details →

Subglacial valleys preserved in the highlands of south and east Greenland record restricted ice extent during past warmer climates: Datasets

This dataset contains the following files:<ul><li>mountain_glacial_valleys_2km.shp (and ancillary files: .dbf, .prj, .shx): ESRI shapefile of the subglacial valleys mapped along the southern and eastern highlands of Greenland using MODIS Mosaic of Greenland imagery and radio-echo sounding data.</li><li>mountain_glacial_limit.shp (and ancillary files: .dbf, .prj, .shx): ESRI shapefile of the interpreted palaeo-glacial valley limit along the southern and eastern highlands of Greenland based on the distribution and morphology of the mapped subglacial valleys.</li><li>ex_g5km_10ka_hy_east.nc: NetCDF file of the Parallel Ice Sheet Model output spatially variable fields for the best-fitting simulation for the eastern highlands (run at 5 km horizontal resolution for 10,000 model years).</li><li>ts_g5km_10ka_hy_east.nc: NetCDF file of the Parallel Ice Sheet Model output scalar time series for the best-fitting simulation for the eastern highlands (run at 5 km horizontal resolution for 10,000 model years).</li><li>ex_g5km_10ka_hy_south.nc: NetCDF file of the Parallel Ice Sheet Model output spatially variable fields for the best-fitting simulation for the southern highlands (run at 5 km horizontal resolution for 10,000 model years).</li><li>ts_g5km_10ka_hy_south.nc: NetCDF file of the Parallel Ice Sheet Model output scalar time series for the best-fitting simulation for the southern highlands (run at 5 km horizontal resolution for 10,000 model years).</li></ul>

openother-ncMay 2022View details →

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the region of the Yucatán Peninsula

The ensemble provides future projections of key marine variables under climate change for the region of the Yucatán Peninsula. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and five different variables (potential temperature, salinity, dissolved oxygen, pH and chlorophyll) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS).   Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the Chilean coast, see “Related identifiers”. The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul>   We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.

openother-ncJun 2022View details →

An ensemble of trend preserving statistically downscaled projections for key marine variables under three different future scenarios for the Chilean coast

The ensemble provides future projections of key marine variables under climate change for the Chilean coast. The datasets were produced for three different future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) and three different variables (potential temperature, dissolved oxygen, and pH) at three different depth levels (5m, 25m and seafloor with the exception of chlorophyll) at monthly frequency for the years 1993 - 2099. The statistical metrics provided are the mean, standard deviation, minimum, maximum median, 2.5 and 97.5 percentile. The ensemble is computed over 3-7 different CMIP6 model realisations (depending on variable), the bias corrections and statistical downscaling was trained on the GLORYS12V1 reanalysis provided by the Copernicus Marine Environment Monitoring Service (CMEMS). The following Earth System Models were used in building the ensemble: <ul> <li>CMCC-ESM2 (Lovato et al. 2022)</li> <li>CMCC-CM2-SR5 (Cherchi et al. 2019)</li> <li>GFDL-ESM4 (Dunne et al., 2020)</li> <li>MPI-ESM1-2-LR (Mauritsen et al., 2020)</li> <li>IPSL-CM6A-LR (Boucher et al. 2020)</li> </ul>   Analogue datasets are provided in separate zenodo entries for the regions of the Mediterranean Sea, the North Sea, the Baltic Sea, the Bay of Biscay and the area around the Yucatán Peninsula, see “Related identifiers”. A description of the downscaling approach and evaluation of the datasets over the European regions is published in <a href="https://doi.org/10.1038/s41598-024-51160-1">Kristiansen et al. 2024</a>. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP. Generated using E.U. Copernicus Marine Service Information; <a href="https://doi.org/10.48670/moi-00021">https://doi.org/10.48670/moi-00021</a>,  <a href="https://doi.org/10.48670/moi-00019">https://doi.org/10.48670/moi-00019</a>. This data is distributed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.