Find research datasets worth reusing

openodc-byAug 2019View details →

A scale-aware parameterization for estimating subgrid variability of downward solar radiation using high-resolution Digital Elevation Model data

This is a statement on the shared data of the manuscript 'A scale-aware parameterization for estimating subgrid variability of downward solar radiation using high-resolution Digital Elevation Model data' Folder Codes includes scripts for plotting all figures of the manuscript. Folder grids includes all the data that are required by scripts in plotting. Contact information: Siwei He NOAA/ERSL/GSD 325 Broadway Boulder, CO 80305 hesiweide@gmail.com

opencc-by-4.0Sep 2024View details →

Data and code for training and testing a ResMLP model with experience replay for machine-learning physics parameterization

This directory contains the training data and code for training and testing a ResMLP with experience replay for creating a machine-learning physics parameterization for the Community Atmospheric Model.  The directory is structured as follows: 1. Download training and testing data: https://portal.nersc.gov/archive/home/z/zhangtao/www/hybird_GCM_ML 2. Unzip nncam_training.zip nncam_training     - models        model definition of ResMLP and other models for comparison purposes     - dataloader         utility scripts to load data into pytorch dataset     - training_scripts        scripts to train ResMLP model with/without experience replay     - offline_test        scripts to perform offline test (Table 2, Figure 2) 3. Unzip nncam_coupling.zip nncam_srcmods      - SourceMods           SourceMods to be used with CAM modules for coupling with neural network      - otherfiles           additional configuration files to setup and run SPCAM with neural network      - pythonfiles          python scripts to run neural network and couple with CAM       - ClimAnalysis           - paper_plots.ipynb              scripts to produce online evaluation figures (Figure 1, Figure 3-10)

opencc-by-4.0May 2024View details →

Tissue-engineered oral epithelial barrier for dental material testing: towards establishing in vitro biomimetic models - Underlying data

Underlying CT data of "Tissue-engineered oral epithelial barrier for dental material testing: towards establishing in vitro biomimetic models" <a href="https://doi.org/10.1089/ten.tec.2024.0154">https://doi.org/10.1089/ten.tec.2024.0154</a> Foteini Machla a, Paraskevi Kyriaki Monou b, c, Chrysanthi Bekiari d, Dimitrios Andreadis e Evangelia Kofidou d, , Emmanouel Panteris f, Orestis L. Katsamenis g, h, Maria Kokoti a, Petros Koidis a, Imad About i, Dimitrios Fatourosb, c, Athina Bakopoulou a a Department of Prosthodontics, Tissue Engineering Core Unit, School of Dentistry, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece b Department of Pharmaceutical Technology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece c Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki 57001, Greece d Laboratory of Anatomy and Histology, Veterinary School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece e Department of Oral Medicine/Pathology, School of Dentistry, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece f Department of Botany, School of Biology, Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece g μ-VIS X-ray Imaging Centre, Faculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom h Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom UK i Centre National de la Recherche Scientifique, Institute of Movement Sciences, Aix Marseille University, Marseille 13385, France   Measurement of ΤΕΟΕ thickness X-ray computed micro-tomography (μCT) as employed to examine the microstructure of the paraffin-embedded tissue engineered oral epithelium (TEOE), enabling comprehensive 3D assessment of thickness using volumetric analysis (cf. supplementary for imaging parameters) (11). Imaging was conducted using an isotropic voxel-edge of 6.0 μm. Local Thickness was carried out in 3D using the “Volume Thickness Map” tool within Dragonfly software (cf. supplementary), allowing for the visualization and quantification of the spatial distribution and variability of tissue thickness. Imaging was conducted at the University of Southampton’s μ-VIS X-ray Imaging Centre ( https://muvis.org ) / 3D X-ray Histology facility, utilizing a customised μCT scanner optimisedfor intricate histological analyses(1), based on Nikon’s XTH225ST system (Nikon Metrology, UK). Operating parameters were set at 80 kVp / 86 μA (6.88 W), with a source-to-object distance of 37.5 mm and a source-to-detector distance of 937.4 mm, resulting in a magnification factor of 25x and an isotropic voxel-edge of 6.0 μm. Imaging acquisition involved the collection of 4001 projections using a 2850 x 2850 dexels detector, by averaging 4 frames per projection, with an exposure time of 500 ms per projection. Visualisation and analysis of the reconstructed dataset was done using Dragonfly software (Comet Technologies Canada Inc.; software accessible at http://www.theobjects.com/dragonfly). Assessment of Local Thickness was carried out in 3D using the “Volume Thickness Map” tool within Dragonfly software, following segmentation of the tissue layer. Local thickness analysis allowed for the visualization and quantification of the spatial distribution and variability of thickness within the tissue engineered oral epithelium (TEOE). Visual representation of local thickness histograms was employed to elucidate the distribution of thickness throughout the TEOE. These histograms effectively illustrate the number of voxels associated with specific cross-sectional thickness, offering both a graphical depiction of the variation in thickness across the tissue sample, and a quantitative measure of the average thickness of the specimen. It's worth noting, the volumetric and non-destructive nature of the technique enabled whole-block imaging, which proved crucial in addressing challenges arising from tissue sample shrinkage. This shrinkage, a consequence of dehydration during the fixation process, can occur in some cases and lead led to the specimen wrapping. While wrapping is not a common occurrence, this analysis method allowed for the evaluation of challenging-shaped specimens, such as the wrapped one presented in Figure 4. Unlike conventional 2D methods such as classical histology, which rely on the angle of slicing and encounter limitations when dealing with non-perfectly perpendicular slicing, μCT-based XRH enables analysis of all specimens, including those with complex shapes.

restrictedgpl-3.0-or-laterOct 2024View details →

Data supporting: A Paradigm Shift to Assembly-like Finite Element Model Updating

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →

Data supporting "Constructing Emergent U(1) Symmetries in the Gamma-Prime (Γ′) model"

Frustrated magnets can elude the paradigm of conventional symmetry breaking and instead exhibit signatures of emergent symmetries at low temperatures. Such symmetries arise from “accidental” degeneracies within the ground state manifold and have been explored in a number of disparate models, in both two and three dimen- sions. Here we report the systematic construction of a family of classical spin models that, for a wide variety of lattice geometries with triangular motifs in one, two and three spatial dimensions, such as the kagome or hy- perkagome lattices, exhibit an emergent, continuous U(1) symmetry. This is particularly surprising because the underlying Hamiltonian actually has very little symmetry — a bond-directional, off-diagonal exchange model inspired by the microscopics of spin-orbit entangled materials (the Γ′-model). The construction thus allows for a systematic study of the interplay between the emergent continuous U(1) symmetry and the underlying discrete Hamiltonian symmetries in different lattices across different spatial dimensions. We discuss the impact of thermal and quantum fluctuations in lifting the accidental ground state degeneracy via the thermal and quan- tum order-by-disorder mechanisms, and how spatial dimensionality and lattice symmetries play a crucial role in shaping the physics of the model. Complementary Monte Carlo simulations, for representative one-, two-, and three-dimensional lattice geometries, provide a complete account of the thermodynamics and confirm our analytical expectations.

opencc-by-4.0Oct 2024View details →

Pre-processed data and trained model weight in ADAF

<h1>Pre-proccesd data</h1> The pre-proccesd data consists of input-target pairs. The inputs include surface weather observations within a 3-hour window, GOES-16 satellite imagery within a 3-hour window, HRRR forecast, and topography. The target is a combination of RTMA and surface weather observations. The table below summarizes the input and target datasets utilized in this study. All data were regularized to grids of size 512 $\times$ 1280 with a spatial resolution of 0.05 $\times$ 0.05 $^\circ$.  <table> <tbody> <tr> <td> </td> <td>Dataset</td> <td>Source</td> <td>Time window</td> <td>Variables/Bands</td> </tr> <tr> <td>Input</td> <td>Surface weather observations</td> <td>WeatherReal-Synoptic (Jin et al., 2024)</td> <td>3 hours</td> <td>Q, T2M, U10, V10</td> </tr> <tr> <td>Input</td> <td>Satellite imagery</td> <td>GOES-16 (Tan et al., 2019)</td> <td>3 hours</td> <td>0.64, 3.9, 7.3, 11.2 $\mu m$</td> </tr> <tr> <td>Input</td> <td>Background</td> <td>HRRR forecast (Dowell et al., 2022)</td> <td>N/A</td> <td>Q, T2M, U10, V10</td> </tr> <tr> <td>Input</td> <td>Topography</td> <td>ERA5 (Hersbach et al., 2019)</td> <td>N/A</td> <td>Geopotential</td> </tr> <tr> <td>Target</td> <td>Analysis</td> <td>RTMA (Pondeca et al., 2011)</td> <td>N/A</td> <td>Q, T2M, U10, V10</td> </tr> <tr> <td>Target</td> <td>Surface weather observations</td> <td>WeatherReal-Synoptic (Jin et al., 2024)</td> <td>N/A</td> <td>Q, T2M, U10, V10</td> </tr> </tbody> </table> <a href="https://zenodo.org/api/records/14020879/draft/files/2022-10-01_06.nc/content" target="_blank" rel="noopener noreferrer">2022-10-01_06.nc</a> is a sample of pre-proccesd data. The vairables in this file contain the input-target pairs mentioned above. <div> A sample file contains the following variables: <table> <tbody> <tr> <td>Variable</td> <td>Decription</td> <td>Dimension</td> </tr> <tr> <td>z</td> <td>Topography, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_t</td> <td>T2M from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_q</td> <td>Q from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_u10</td> <td>U10 from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_v10</td> <td>V10 from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>sta_t</td> <td>T2M from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>sta_q</td> <td>Q from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>sta_u10</td> <td>U10 from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>sta_v10</td> <td>V10 from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI02</td> <td>ABI Band 2: visible (red), normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI07</td> <td>ABI Band 7: shortwave infrared, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI10</td> <td>ABI Band 10: low-level water vapor, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI14</td> <td>ABI Bands 14: longwave infrared, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>hrrr_t</td> <td>T2M from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> <tr> <td>hrrr_q</td> <td>Q from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> <tr> <td>hrrr_u_10</td> <td>U10 from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> <tr> <td>hrrr_v_10</td> <td>V10 from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> </tbody> </table>   </div> <h1>Pre-comuted normalization statistics</h1> <a href="https://zenodo.org/api/records/14020879/draft/files/stats.csv/content" target="_blank" rel="noopener noreferrer">stats.csv</a> is pre-comuted normalization statistics. <h1>Pre-trained model weights</h1> <a href="https://zenodo.org/api/records/14020879/draft/files/best_ckpt.tar/content" target="_blank" rel="noopener noreferrer">best_ckpt.tar</a> is the pre-trained model weights. <h1>References</h1> <ol> <li>Jin, W. et al. WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models. (2024).</li> <li>Dowell, D. et al. The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part I: Motivation and System Description. Weather and Forecasting 37, (2022).</li> <li>Tan, B., Dellomo, J., Wolfe, R. & Reth, A. GOES-16 and GOES-17 ABI INR assessment. in Earth Observing Systems XXIV vol. 11127 290–301 (SPIE, 2019).</li> <li>Hersbach, H. et al. ERA5 monthly averaged data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) 10, 252–266 (2019).</li> <li>Pondeca, M. S. F. V. D. et al. The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development. Weather and Forecasting 26, 593–612 (2011).</li> </ol>

opencc-by-4.0Nov 2024View details →

Data for Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control

This repository contains data derivatives sufficient to generate all results and figures reported in the manuscript. The code to do so is contained in the linked GitHub repository, which is also stably archived on Zenodo. GitHub repo: <a href="https://github.com/mcfreund/trr">https://github.com/mcfreund/trr</a> Stable archive for GitHub repo: <a href="https://doi.org/10.5281/zenodo.14048490">https://doi.org/10.5281/zenodo.14048490</a>

opencc-zeroDec 2010View details →

Data from: Comparison of biometrical models for joint linkage association mapping

Joint linkage association mapping (JLAM) combines the advantages of linkage mapping and association mapping, and is a powerful tool to dissect the genetic architecture of complex traits. The main goal of this study was to use a cross-validation strategy, resample model averaging and empirical data analyses to compare seven different biometrical models for JLAM with regard to the correction for population structure and the quantitative trait loci (QTL) detection power. Three linear models and four linear mixed models with different approaches to control for population stratification were evaluated. Models A, B and C were linear models with either cofactors (Model-A), or cofactors and a population effect (Model-B), or a model in which the cofactors and the single-nucleotide polymorphism effect were modeled as nested within population (Model-C). The mixed models, D, E, F and G, included a random population effect (Model-D), or a random population effect with defined variance structure (Model-E), a kinship matrix defining the degree of relatedness among the genotypes (Model-F), or a kinship matrix and principal coordinates (Model-G). The tested models were conceptually different and were also found to differ in terms of power to detect QTL. Model-B with the cofactors and a population effect, effectively controlled population structure and possessed a high predictive power. The varying allele substitution effects in different populations suggest as a promising strategy for JLAM to use Model-B for the detection of QTL and then to estimate their effects by applying Model-C.

opencc-zeroDec 2012View details →

Data from: Effects of diet on resource utilization by a model human gut microbiota containing Bacteroides cellulosilyticus WH2, a symbiont with an extensive glycobiome

The human gut microbiota is an important metabolic organ. However, little is known about how its individual species interact, establish dominant positions, and respond to changes in environmental factors such as diet. In the current study, gnotobiotic mice colonized with a simplified model microbiota composed of 12 sequenced human gut bacterial species were fed oscillating diets of disparate composition. Rapid, reproducible and reversible changes in community structure were observed. Time series microbial RNA-Seq analyses revealed staggered functional responses to diet shifts throughout the community that were heavily focused on carbohydrate and amino acid metabolism. High-resolution shotgun metaproteomics confirmed many of these responses at a protein level. One member, Bacteroides cellulosilyticus WH2, proved exceptionally fit regardless of diet. Its genome encoded more carbohydrate active enzymes than any known Bacteroidetes. Transcriptional profiling indicated that B. cellulosilyticus WH2 is an adaptive forager that tailors its versatile carbohydrate utilization strategy to available dietary polysaccharides, with a strong emphasis on plant-derived xylans abundant in dietary staples like cereal grains. Two highly expressed, diet-specific polysaccharide utilization loci (PULs) in B. cellulosilyticus WH2 were identified, one with characteristics of xylan utilization systems. Introduction of a B. cellulosilyticus WH2 library comprising 26,750 isogenic transposon mutants into gnotobiotic mice along with other model community members confirmed that these loci represent critical diet-specific fitness determinants. The specific carbohydrates that trigger overexpression of these two loci and many of the organism's 111 other predicted PULs were identified by RNA-Seq during in vitro growth on 31 distinct carbohydrate substrates, allowing us to better interpret in vivo RNA-Seq and proteomics data. These results offer insight into how gut microbes adapt to dietary perturbations, both at a community level and from the perspective of a well-adapted symbiont with exceptional saccharolytic capabilities, and illustrate the value of studying defined models of the human gut microbiota.

opencc-zeroAug 2019View details →

Data from: Discovery of potential urine-accessible metabolite biomarkers associated with muscle disease and corticosteroid response in the mdx mouse model for Duchenne

Urine is increasingly being considered as a source of biomarker development in Duchenne Muscular Dystrophy (DMD), a severe, life-limiting disorder that affects approximately 1 in 4500 boys. In this study, we considered the mdx mice—a murine model of DMD—to discover biomarkers of disease, as well as pharmacodynamic biomarkers responsive to prednisolone, a corticosteroid commonly used to treat DMD. Longitudinal urine samples were analyzed from male age-matched mdx and wild-type mice randomized to prednisolone or vehicle control via liquid chromatography tandem mass spectrometry. A large number of metabolites (869 out of 6,334) were found to be significantly different between mdx and wild-type mice at baseline (Bonferroni-adjusted p-value < 0.05), thus being associated with disease status. These included a metabolite with m/z = 357 and creatine, which were also reported in a previous human study looking at serum. Novel observations in this study included peaks identified as biliverdin and hypusine. These four metabolites were significantly higher at baseline in the urine of mdx mice compared to wild-type, and significantly changed their levels over time after baseline. Creatine and biliverdin levels were also different between treated and control groups, but for creatine this may have been driven by an imbalance at baseline. In conclusion, our study reports a number of biomarkers, both known and novel, which may be related to either the mechanisms of muscle injury in DMD or prednisolone treatment.

opencc-zeroJul 2021View details →

Data from: Model egg rejection by Eastern Bluebirds

Brood parasitism results in substantial costs to hosts, yet not all species eject foreign eggs. Because the costs of mistakenly ejecting one's own eggs are high, selection may favor ejection behavior only if it is unlikely a host will incorrectly eject her own eggs. Eastern Bluebirds are currently subject to relatively low levels of interspecific brood parasitism but still sometimes eject parasitic eggs. Therefore, we tested which visual cues they use to eject foreign eggs with the prediction that only the most dissimilar eggs would be ejected, reducing the likelihood of a female's making a mistake. House Sparrows, which occasionally parasitize bluebirds, lay eggs that have an off-white ground color with brown speckling. Therefore, to test which colors or patterns allow for discrimination of parasitic eggs, we generated 3D-printed model House Sparrow eggs and painted them entirely off-white, entirely brown, half off-white and half brown, or off-white with brown speckling. We then sequentially placed these four different model eggs in the nests of Eastern Bluebirds, with each nest receiving all treatments over the course of four days. After watching females enter and leave the nest box just one time after placement of the model egg, we found that speckled eggs were ejected half the time (7 of 14 nests), while no other treatment was ejected more than three times. Thus, Eastern Bluebird females eject eggs based primarily on color patterning (i.e., a speckled pattern) rather than coloration per se, and that they can do so very quickly, as the average female had removed the model egg within six minutes of entering the nest. Because Eastern Bluebirds do not lay speckled eggs, but some brood parasites (e.g., House Sparrows, Brown-headed Cowbirds) do, selection may specifically favor ejection of eggs with a speckled pattern, not just eggs that have within-egg color contrasts.

opencc-zeroOct 2021View details →

Water potential data and model output

<ol> <li>Classifying the diverse ways that plants respond to hydrologic stress into generalizable 'water-use strategies' has long been an eco-physiological research goal. While many schemes for describing water-use strategies have proven to be quite useful, they are also associated with uncertainties regarding their theoretical basis and their connection to plant carbon and water relations. In this review, we discuss the factors that shape plant water stress responses and assess the approaches used to classify a plant's water-use strategy, paying particular attention to the popular but controversial concept of a continuum from isohydry to anisohydry.</li> <li>A generalizable and predictive framework for assessing plant water-use strategies has been historically elusive, yet recent advances in plant physiology and hydraulics provide the field with a way past these obstacles. Specifically, we promote the idea that many metrics that quantify water-use strategies are highly dynamic and emergent from the interaction between plant traits and environmental conditions, and that this complexity has historically hindered the development of a generalizable water-use strategy framework.</li> <li>This idea is explored using a plant hydraulics model to identify: 1) distinct temporal phases in plant hydraulic regulation during drought that underpin dynamic water-use responses, and 2) how variation in both traits and environmental forcings can significantly alter common metrics used to characterize plant water-use strategies. This modeling exercise can bridge the divide between various conceptualizations of water-use strategies and provides targeted hypotheses to advance understanding and quantification of plant water status regulation across spatial and temporal scales.</li> <li>Finally, we describe research frontiers that are necessary to improve the predictive capacity of the plant water-use strategies concept, including further investigation into the belowground determinants of plant water relations, targeted data collection efforts, and the potential to scale these concepts from individuals to whole regions.</li> </ol>

opencc-by-4.0Oct 2022View details →

Data generated by our developed theoretical asperity contact creep model

The uploaded data was generated by our developed theoretical asperity contact creep model of interfacial friction for Geomaterials, including excel-style data and origin-style data.

openogl-uk-3.0May 2022View details →

A subsection of England and Wales EPC households, joined with PPD data, used for simulation modelling

If you want to give feedback on this dataset, or wish to request it in another form (e.g csv), please fill out this survey <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqCAoQt4AzuGH8Th5tJjnkGP956Fgc6O8T6wJaM7Nhd_nRdg/viewform?usp=pp_url&entry.1276408097=10.5281/zenodo.7322967">here</a>. We are a not-for-profit research organisation keen to see how others use our open models and tools, so all feedback is appreciated! It's a short form that takes 5 minutes to complete.  Important Note: Before downloading this dataset, please read the License and Software Attribution section at the bottom. This dataset aligns with the work published in Centre for Net Zero's report "Hitting the Target". In this work, we simulate a range of interventions to model the situations in which we believe the UK will meet its 600,000 heat pump installation per year target by 2028. For full modelling assumptions and findings, read our <a href="https://www.centrefornetzero.org/res/hitting-the-target/">report on our website</a>. The code for running our simulation is open source <a href="https://github.com/centrefornetzero/domestic-heating-abm">here</a>. This dataset contains over 9 million households that have been address matched between Energy Performance Certificates (EPC) data and Price Paid Data (PPD). The code for our address matching is <a href="https://github.com/centrefornetzero/epc-ppd-address-matching">here</a>. Since these datasets are Open Government License (OGL), this dataset is too. We basically model specific columns from various datasets, as set out in our methodology section in our report, to simplify and clean up this dataset for academic use. License information is also available in the appendix of our report above. The EPC data loaders can be found <a href="https://github.com/centrefornetzero/epc-england-wales-parquet">here</a> (the data is <a href="https://epc.opendatacommunities.org/">here</a>) and the rest of the schemas and data download locations can be found <a href="https://github.com/centrefornetzero/bigquery-schemas">here</a>. Note that this dataset is not regularly maintained or updated. It is correct as of January 2022. The data was curated and tested using dbt via <a href="https://github.com/centrefornetzero/domestic-heating-data">this Github repository</a> and would be simple to rerun on the latest data. The schema / data dictionary for this data can be found <a href="https://github.com/centrefornetzero/domestic-heating-data/blob/main/cnz/models/marts/domestic_heating/domestic_heating.yml#L5">here</a>. Our recommended way of loading this data is in Python. After downloading all "parts" of the dataset to a folder. You can run: ``` import pandas as pd data = pd.read_parquet("path/to/data/folder/") ```   Licenses and software attribution: For EPC, PPD and UK House Price Index data: For the EPC data, we are permitted to republish this providing we mention that all researchers who download this dataset follow <a href="https://epc.opendatacommunities.org/docs/copyright">these copyright restrictions</a>. We do not explicitly release any Royal Mail address data, instead we use these fields to generate a pseudonymised "address_cluster_id" which reflects a unique combination of the address lines and postcodes, as well as other metadata. When viewing <a href="https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/what-is-personal-data/what-is-personal-data/">ICO and GDPR guidelines</a>, this still counts as personal data, but we have gone to measures to pseudonymise as much as possible to fulfil our obligations as a data processor. You must read this carefully before downloading the data, and ensure that you are using it for the research purposes as determined by this copyright notice. Contains HM Land Registry data © Crown copyright and database right 2021. This data is licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2022. Contains Office for National Statistics data licensed under the Open Government Licence v.3.0. The OGL v3.0 license states that we are free to: <ul> <li>copy, publish, distribute and transmit the Information;</li> <li>adapt the Information;</li> <li>exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application.</li> </ul> However we must (where we do any of the above): <ul> <li>acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence;</li> </ul> You can see more information <a href="https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">here</a>. For XOServe Off Gas Postcodes: This dataset has been released openly for all uses <a href="https://www.cse.org.uk/projects/view/1259#GB_postcodes_off_the_mains_gas_grid">here</a>. For the address matching: GNU Parallel: O. Tange (2018): GNU Parallel 2018, March 2018, https://doi.org/10.5281/zenodo.1146014

single-cell RNAseq data (data set 13) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset13) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor11 downloaded from the GEO website  (GSE114297).  see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a> For more information, version updates of the scFASTCORMICS.

single-cell RNAseq data (data set 10) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset10) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor8 downloaded from the GEO website  (GSE114297).  see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: https://github.com/sysbiolux/scFASTCORMICS For more information, version updates of the scFASTCORMICS.

single-cell RNAseq data (data set 6) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset6) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor4 downloaded from the GEO website  (GSE114297).  see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a> For more information, version updates of the scFASTCORMICS.

single-cell RNAseq data (data set 2) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data

The present dataset (dataset2) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from normal mucosa samples downloaded from the GEO website  (GSE81861).  see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data and github: https://github.com/sysbiolux/scFASTCORMICS For more information, version updates of the scFASTCORMICS.

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Data for "The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)" by Verjans et al.

Results and scripts to reproduce figures of The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0) Input files, preprocessing, run control and postprocessing scripts for all simulations are also provided. See readme.txt for details. Update 22 November 2022: use v2 of code files for updated version of StISSM by Verjans et al.