Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus - data set of indoor temperature and relative humidity

This data supplements the journal article:  Buechler E, Pallin S, Boudreaux P, Stockdale M. Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus. Journal of Building Physics. 2017;41(3):225-246. doi:<a href="https://doi.org/10.1177/1744259117701893">10.1177/1744259117701893</a> Abstract: The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, heating, ventilation, and air-conditioning system performance, and occupant comfort. Therefore, indoor climate data are generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies are influenced by weather, occupant behavior, and internal loads and are generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The purpose of this study was to probabilistically model homes with the simulation engine EnergyPlus to generate indoor climate data that are widely applicable to residential buildings. Monte Carlo methods were used to perform 840,000 simulations on the Oak Ridge National Laboratory supercomputer (Titan) that accounted for stochastic variation in internal loads, air tightness, home size, and thermostat set points. The Effective Moisture Penetration Depth model was used to consider the effects of moisture buffering. The effects of location and building type on indoor climate were analyzed by evaluating six building types and 14 locations across the United States. The average monthly net indoor moisture supply values were calculated for each climate zone, and the distributions of indoor air temperature and relative humidity conditions were compared with ASHRAE 160 and EN 15026 design conditions. The indoor climate data will be incorporated into an online database tool to aid the building community in designing effective heating, ventilation, and air-conditioning systems and moisture durable building envelopes. This supplemental data set includes the hourly temperature and relative humidity for the 10th, 50th, and 90th percentile simulations for each building type in each climate zone. The column headings are of the following format buildingtype_climatezone_output_percentile. There are six building types, B1 (unfinished basement 1-story), B2 (unfinished basement 2-story), C1 (unvented crawlspace 1-story), C2 (unvented crawlspace 2-story), S1 (slab 1-story), and S2 (slab 2-story).

data sets from "Updated trends of the stratospheric ozone vertical distribution in the 60S–60N latitude range based on the LOTUS regression model"

Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model". Information about and the most recent versions of each dataset can be found at their individual source locations: Merged satellite datasets <ol> <li>SBUV MOD – https://acd-ext.gsfc.nasa.gov/Data_services/merged/index.html (NASA GSFC, USA)</li> <li>SBUV COH: https://ftp.cpc.ncep.noaa.gov/SBUV_CDR/ (NOAA, USA).</li> <li>GOZCARDS: https://www.earthdata.nasa.gov/esds/competitive-programs/measures/gozcards (JPL, NASA, USA)</li> <li>SWOOSH: https://csl.noaa.gov/groups/csl8/swoosh/ (NOAA, USA).</li> <li>SAGE-CCI-OMPS and MEGRIDOP datasets are available through https://climate.esa.int/en/projects/ozone/data/ and ftp://cci_web@ftp-ae.oma.be/esacci (ESA Climate Office). They are provided by FMI, Finland</li> <li>SAGE-SCIAMACHY-OMPS: data record is available upon registration via the following link: http://www.iup.uni-bremen.de/DataRequest/ (U. Bremen, Germany).</li> <li>SAGE-OSIRIS-OMPS: downloading instructions can be found at https://research-groups.usask.ca/osiris/data-products.php#OSIRISLevel3andMergedDataProducts (U. Saskatchewan, Canada).</li> </ol> Ground-based records: <ol> <li>Umkehr – https://gml.noaa.gov/aftp/data/ozwv/Dobson/AC4/Umkehr/Monthly/ (NOAA, USA)</li> <li>ozonesondes – https://hegiftom.meteo.be/datasets/ozonesondes (HEGIFTOM). Measurements at the various stations are provided by the following institutions: <ul> <li>Hohenpeissenberg: DWD, Germany</li> <li>Payerne:MeteoSwiss, Switzerland</li> <li>OHP, CNRS, France</li> <li>Hilo, NOAA, USA</li> <li>Lauder, NIWA, New Zealand</li> </ul> </li> <li>lidar: <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> . Measurement at the various stations are provided by the following institutions: <ul> <li>Hohenpeissenberg: DWD, Germany</li> <li>OHP: CNRS, France</li> <li>MLO: JPL, NASA, USA</li> <li>Lauder: NIWA, New Zealand</li> </ul> </li> <li>FTIR spectrometers – <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> Three sites only provided quality checked measurements relevant for the article. For other ozone FTIR measurements, data in <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> must be used. Measurement used in the article are provided by the following institutions: <ul> <li>Zugspitze: KIT, Germany</li> <li>Jungfraujoch: ULiège, GIRPAS team, Belgium</li> <li>Lauder: NIWA, New Zealand</li> </ul> </li> <li>Microwave spectrometers: <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> Measurement at the various stations are provided by the following institutions: <ul> <li>Payerne: MeteoSwiss, Switzerland</li> <li>Mauna Loa: NRL, USA</li> <li>Lauder: NRL, USA</li> </ul> </li> </ol> Chemistry Climate Model (CCM) CCMI simulations are avilable at  https://blogs.reading.ac.uk/ccmi

opencc-by-4.0Jan 2022View details →

Multimodal dataset: Protein Function Prediction using STRING data & COVID19 Mortality Model by EI

The PFP.zip file contains 1. 5 well-formated GO terms dataset for EI, 2. STRING data 3. GO term annotation. The last two could be merged by the 'generate_data.py' script in https://github.com/GauravPandeyLab/ensemble_integration The covid19_model_built.zip contained the EI model built based on the COVID-19 Mortality dataset, the detail of usage are here:.

Oservational data for sfdda nudging analysis in WRF model over China during 2017

Oservational data for sfdda nudging analysis in WRF model over China during 2017.

opencc-by-4.0Jan 2021View details →

Lakkasuo carbon isotope and AWEN extraction data for Yasso-C13 model development

This package contains carbon isotope and AWEN extraction data from litterbag experiments (5 year) at Lakkasuo, a raised bog complex near Hyytiälä weather station in Finland. Additionally present are driving data and parameter values needed to run soil carbon model Yasso. The given data is used to implement and calibrate carbon-13 related soil organic matter decomposition in the Yasso model. The dataset also contains calibration results, scripts to run the results anew and to produce plots and images. The updated dataset uses new Yasso20 parameter values.

opencc-zeroAug 2022View details →

Data for: Modeling the distribution of the endangered Jemez Mountains salamander (Plethodon neomexicanus) in relation to geology, topography, and climate

The Jemez Mountains salamander (Plethodon neomexicanus; hereafter JMS) is an endangered salamander restricted to the Jemez Mountains in north-central New Mexico, United States. This strictly terrestrial species requires moist surface conditions for mating and foraging. Threats to its current habitat include fire suppression and ensuing severe fires, changes in forest composition, habitat fragmentation, and climate change. Forest composition changes resulting from reduced fire frequency and increased tree density suggest that its current aboveground habitat does not mirror its historically successful habitat regime. We hypothesized that geology and topography might play a significant role in the current distribution of the salamander. We modeled the distribution of the JMS using a machine learning algorithm to assess how geology, topography, and climate variables influence its distribution. Our habitat suitability map reveals low uncertainty in model predictions, and we found slight discrepancies between the designated critical habitat and the most suitable areas for the JMS. Because geological features are important to its distribution, we recommend that geological and topographical data are considered, both during survey design and in the description of localities of JMS records once detected.

Data & model products from "Identification of carbon dioxide in an exoplanet atmosphere"

Associated Publication: <a href="https://www.nature.com/articles/s41586-022-05269-w">https://www.nature.com/articles/s41586-022-05269-w</a>   OVERVIEW: Carbon dioxide (CO2) is a key chemical species that is found in a wide range of planetary atmospheres. In the context of exoplanets, CO2 is an indicator of the metal enrichment (i.e., elements heavier than helium, also called “metallicity”), and thus formation processes of the primary atmospheres of hot gas giants. It is also one of the most promising species to detect in the secondary atmospheres of terrestrial exoplanets. Previous photometric measurements of transiting planets with the Spitzer Space Telescope have given hints of the presence of CO2, but have not yielded definitive detections due to the lack of unambiguous spectroscopic identification. Here we present the detection of CO2 in the atmosphere of the gas giant exoplanet WASP-39b from transmission spectroscopy observations obtained with JWST as part of the Early Release Science Program (ERS). The data used in this study span 3.0 - 5.5 µm in wavelength and show a prominent CO2 absorption feature at 4.3 µm (26σ significance). The overall spectrum is well matched by one-dimensional, 10x solar metallicity models that assume radiative-convective-thermochemical equilibrium and have moderate cloud opacity. These models predict that the atmosphere should have water, carbon monoxide, and hydrogen sulfide in addition to CO2, but little methane. Furthermore, we also tentatively detect a small absorption feature near 4.0 µm that is not reproduced by these models.

Noah-MP data for modeling Canadian spring wheat study

This zip file contains the simulation results from a Noah-MP crop model for a Canadian spring wheat study. There are two separate folders inside: one for single-point data and one for regional data results. The single-point folder contains three model outputs from the three site-year (2016, 2019SW, 2019SE) and three model treatments (default NoahMP, wheat model, and TAVE for dynamic planting threshold) The regional folder contains the combined agricultural statistics from USDA and StatisCanada (combine_crop_PPR.nc), default wheat model results, and the temperature stress results.  Please feel free to contact Dr. Zhe Zhang (zhe.zhang@usask.ca) or Dr. Yanping Li (yanping.li@usask.ca) for further details.

Modeling the Extragalactic Background Light and the Cosmic Star Formation History (data)

Paper: Modeling the Extragalactic Background Light and the Cosmic Star Formation History Authors: Justin D. Finke, Marco Ajello, Alberto Dominguez, Abhishek Desai, Dieter H. Hartmann, Vaidehi S. Paliya, Alberto Saldana-Lopez Description: These are the luminosity densities, EBL energy density/intensities, and gamma-ray absorption optical depths for "Model A" from this publication. Contents: lumdens.tar.gz: The model luminosity density, with redshift given in the title of the file.  Each file contains the wavelength in microns, and the luminosity density in Watts/Mpc^3. EBL_energydensity.tar.gz: The model EBL energy density, with redshift given in the name of the file.  Each file contains the photon energy in eV, and the energy density in erg/cm^3. EBL_intensity.tar.gz: The model EBL intensity, with redshift given in the name of the file.  Each file contains the wavelength in angstroms, and the intensity in nW/(m^2 srad). tau.tar.gz: The model gamma-gamma absorption optical depths, with redshift given in the name of the file.  Each file contains the photon energy in TeV, and the absorption optical depth. Distribution Statement A.  Approved for public release.  Distribution is unlimited.

Data for Magnetosphere-Ionosphere-Thermosphere Coupling Study at Jupiter Based on Juno's First 30 Orbits and Modeling Tools

Data used in the code associated to the manuscript "Magnetosphere-Ionosphere-Thermosphere Coupling Study at Jupiter Based on Juno’s First 30 Orbits and Modeling Tools", by Al Saati et al. (2022, Journal of Geophysical Research - Space Physics, https://doi.org/10.1029/2022JA030586). Please read the documentation associated with the corresponding code.

Raw output data from ColabFold modelling for the paper 'Interaction of C21ORF2 with a domain of NEK1 mutated in human diseases is vital for NEK1 function in human cells'

Raw output data from ColabFold modelling for the paper 'Interaction of C21ORF2 with a domain of NEK1 mutated in human diseases is vital for NEK1 function in human cells' File descriptions: NEK11160endC21ORF2_amber_2e60f_relaxed_rank_1_model_1_fixed.pdb ColabFold output PDB file - Rank 1 model NEK11160endC21ORF2_amber_2e60f_relaxed_rank_2_model_2_fixed.pdb ColabFold output PDB file - Rank 2 model NEK11160endC21ORF2_amber_2e60f_relaxed_rank_3_model_4_fixed.pdb ColabFold output PDB file - Rank 3 model NEK11160endC21ORF2_amber_2e60f_relaxed_rank_4_model_3_fixed.pdb ColabFold output PDB file - Rank 4 model NEK11160endC21ORF2_amber_2e60f_relaxed_rank_5_model_5_fixed.pdb ColabFold output PDB file - Rank 5 model NEK11160endC21ORF2_amber_2e60f_coverage.png ColabFold output chart - MSA sequence coverage NEK11160endC21ORF2_amber_2e60f_PAE.png ColabFold output chart - PAE for each model NEK11160endC21ORF2_amber_2e60f_plddt.png ColabFold output chart - predicted IDDT per position Supplementary Excel file 1 List of residues predicted to be involved in intermolecular interactions, and the type of interaction (based on PDB files for each models, generated using BIOVIA Discovery Studio 2021)

Example of Surabaya COPACR Business Process Architecture and Conceptual Data Model

In this repository consist of three diagrams. Two diagrams are part of Surabaya COPACR Business Process Architecture (BPA), they are: 1. Fig 1. Shows six value chains that become BPA Level 0 of the Surabaya COPACR.  2. Fig 2. Shows Electronic ID card printing services as PBA Level 5 And one diagram, Fig. 3 show Conceptual Data Model for Surabaya COPACR Core Process

Midnight Sun to Polar Night: A model of seasonal light in the Barents Sea - Supplementary Data

Supplementary Data Midnight Sun to Polar Night: A model of seasonal light in the Barents Sea. Connan-McGinty, S., Banas, N.S., Berge, J., Cottier, F., Grant, S., Johnsen, G., Kopec, T.P., Porter, M., McKee, D. (2022). All data and Python code required to re-create figures from the above manuscript.

Proccessed Data for the Pipelines of the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome"

Proccessed and Input Data for the Pipelines of the Project "Multiomics and quantitative modelling disentangle diet, host, and microbiota contributions to the host metabolome" ----------------------------------------------------------------------------------------------------- Contents: ----------------------------------------------------------------------------------------------------- Folder /ProcessedData/metabolomics/ contains processed metabolomics data from the project: /metabolomics/metabolites_allions_combined_norm_intensity.csv - file containing normalized intensities of ions detected across tissues with six measurement methods. /metabolomics/metabolites_allions_combined_formulas_with_metabolite_filters_spatial100clusters_with_mean.csv - file containing metabolite attribution to spatial clusters and mean intensity values across tissues and conditions. Other files are described in README_ProcessedData.md. ----------------------------------------------------------------------------------------------------- Folder /ProcessedData/sequencing/ contains raw and normalized counts of metagenomics and metatransriptomics data mapped to bacterial genomes. Folder /ProccessedData/util/ contains files used for data preprocessing and attribution to chemical classes and pathways. Folder /ProcessedData/example_output/ contains example output of the pipelines: /output/model_results_SMOOTH_raw_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (intestinal flux and metabolic flux values) for the forward problem for metabolomics measurements in the GIT. /output/model_results_SMOOTH_normbyabsmax_reciprocal_problem_allions.csv - file containing estimated model parameters for the reverse problem (metabolite intensities) for the parameters estimated with the forward problem. /output/model_results_SMOOTH_normbyabsmax_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (intestinal flux and metabolic flux values) for the forward problem for metabolomics measurements in the GIT, normalized by absolute maximum value. /output/model_results_SMOOTH_normbyabsmax_ONLYMETCOEF_2LIcoefHost1LIcoefbact_allions.csv - file containing estimated model parameters (only metabolic flux values) for the forward problem for metabolomics measurements in the GIT, normalized by absolute maximum value. /output/table_hierarchical_clustering_groups.csv - file containing attribution of the annotated metabolites to groups according to hierarchical clustering of the normalized model parameters. /output/cgo_clustergrams_of_model_coefficients.mat - matlab object containing clustergram of the normalized model parameters and manually derived sub-clustergrams corresponding to different largest parameter values. Description of other files is provided in the file README_ProcessedData.md. ----------------------------------------------------------------------------------------------------- Folder /InputData/ contains HMDB and KEGG tables used for metabolite annotations and chemical group analysis. Folder InputData_KEGGreaction_path contains matlab files with metabolite-metabolite paths calculated from KEGG reaction-pair information (Each matrix contains a subset of paths). These files are used by the script workflow_extract_keggECpathes_for_SPpairs_final.m. Folder InputData_metabolomics_data contains raw metabolomics data from six methods (three LC columns: C08, C18 and HILIC, and positive and negative acquisition modes) and file tissue_weights.txt with tissue weight information used for normalization. Folder InputData_sequencing_data contains folders ballgown_DNA and ballgown_RNA with results of metagenomic and metatranscriptomic data analysis (raw counts, GetMM normalized counts, EdgeR and DeSeq2 analysis).    Description of folders is provided in the file readme_InputData.md. -----------------------------------------------------------------------------------------------------

opencc-zeroSep 2022View details →

Data from: Phylogenomic structure and speciation in an emerging model: The Sphagnum magellanicum complex (Bryophyta)

The moss genus Sphagnum has unparalleled ecological importance because some 30% of the total terrestrial carbon pool is bound up in Sphagnum-dominated peatlands. A major peat-former, S. magellanicum, is one of two species for which a reference-quality genome exists to facilitate research in ecological genomics, but recently published work indicated that S. magellanicum s. str. is restricted to South America and two other species, S. divinum and S. medium occur in North America and Europe. We report herein that there are four clades/species within the S. magellanicum complex in eastern North America, two in South America, and another in eastern Asia. The reference genome belongs to S. divinum. Phylogenetic analyses at the whole genome and chromosome levels, using genome resequencing and RADseq, resolve sister group relationships within the complex. Species are monophyletic in most analyses and exhibit tens of thousands (RADseq) to millions (resequencing) of fixed nucleotide differences, but two, referred to informally as S. diabolicum and S. magni because they have not been formally described, are differentiated by only hundreds (RADseq) to thousands (resequencing) of differences. Data from 14 of the 19 resequenced chromosomes (7 chromosomes for RADseq) resolve the reciprocal monophyly of S. magni and S. diabolicum. These two appear to be in the process of speciation and because they differ in geographic ranges and the climate zones they occupy – S. diabolicum in boreal peatlands and S. magni in warm temperate to subtropical communities of the southern U.S. – they provide an exciting opportunity for comparative genomic analyses of climate niche evolution. Introgression among species in the complex is demonstrated using D-statistics and f4-ratios. One ecologically important functional trait that underlies peat (carbon) accumulation, tissue decomposability, does not differ between segregate North American species in the S. magellanicum complex although previous research showed that many related Sphagnum species have evolved differences in decomposability/carbon sequestration. Phylogenetic resolution and more accurate species delimitation in the S. magellanicum complex substantially increase the value of this group for studying the early evolutionary stages of climate adaptation, and ecological evolution more broadly.

opencc-zeroSep 2022View details →

Model data to investigate wood frog abundance in 17-year post harvest variable retention mixed wood forests

Variable retention forest harvesting aims to reduce negative effect of harvesting on forest biodiversity, but its effectiveness is not well understood for many taxa. To better understand the effects of variable retention forest management and environmental features on amphibians, we used pitfall traps to capture wood frogs (Lithobates sylvaticus) across 4 levels of retention harvest (clearcut [0%], 20%, 50%, and unharvested control [100%]), and 2 forest types (deciduous and coniferous), in 17-year post-harvest forests in northwest Alberta. We mapped breeding sites and used a terrain moisture index (Depth-to-Water) derived from airborne LiDAR to examine relationships between relative abundance, breeding site proximity and soil moisture. Retention level alone had no effect on relative abundance, but in late summer (July and August) there was a significant interaction between retention level and forest type: capture rates decreased with amount of retention for deciduous forests, but increased with amount of retention in conifer forests. During late summer, capture rates were higher in conifer forests than in deciduous forests, with soil moisture (lower Depth-to-Water) positively related to capture rates. Though timber retention may be beneficial to wood frogs in the short-term, any impacts of forest harvesting on wood frog abundance was undetectable in stands 17 years post-harvest.

opencc-by-4.0Sep 2022View details →

Data and code: Bayesian Multi-level model calibration of the SPASS phenology model for silage maize

Data and code supporting the research article: Bayesian Multi-level model calibration of the SPASS phenology model for silage maize - M. Viswanathan, A. Scheidegger, T. Streck, S. Gayler, T.K.D. Weber. This includes R code for the implementation of the SPASS phenology maize model; Jags in R to implement the Bayesian multi-level models .

opencc-by-4.0Sep 2022View details →

Data set of 1D model runs, CTRL and ICE runs, associated with "Underestimation of oceanic carbon uptake in the Arctic Ocean: Ice melt as predictor of the sea ice carbon pump"

Dataset of one-dimensional runs for investigation on the sea ice carbon pump. Associated with Sect. 3.1 and 4.1 of manuscript "Underestimation of oceanic carbon uptake in the Arctic Ocean: Ice melt as predictor of the sea ice carbon pump".

opencc-zeroAug 2022View details →

Data: Detecting preservation and reintroduction sites for endangered plant species using a two-step modelling and field approach

To withstand the surge of species loss worldwide, (re)introduction of endangered plant species has become an increasingly common technique in conservation biology. Successful (re)introduction plans, however, require identifying sites that provide the optimal ecological conditions for the target species to thrive. In this study, we propose a two-step approach to identify appropriate (re)introduction sites. The first step involves modelling the niche and distribution of the species with bioclimatic and topographical predictors, both at continental and at national scales. The second step consists of refining these bioclimatic predictions by analysing stationary ecological parameters, such as soil conditions, and relating them to population-level fitness values. We demonstrate this methodology using Swiss populations of the lady's slipper orchid (Cypripedium calceolus L., Orchidaceae), for which conservation plans have existed for years but have generally been unfruitful. Our workflow identified sites for future (re)introductions based on the species requirements for mid-to-sunny light conditions and specific soil physico-chemical properties, such as basic to neutral pH and low soil organic matter content. Our findings show that by combining wide-scale bioclimatic modelling with fine scale field measurements it is possible to carefully identify the ecological requirements of a target species for successful (re)introductions.