Find research datasets worth reusing

opencc-by-4.0Apr 2023View details →

Input Data for the Simulation of Water Supply System of Athens

Input Data for the Simulation of Water Supply System of Athens: Stochastic time-series of water demands, Stochastic time-series of rainfall, evaporation runoff, Stochastic time-series of electricity price, Characteristics of the system, Operational Rules of the system

openmit-licenseApr 2023View details →

Small Modular Reactor Neutronics Challenge Problem Input Files for OpenMC

OpenMC is part of the small modular reactor (SMR) simulation subproject "ExaSMR" in the broader Exascale Computing Project (ECP), which is a US DOE project. The goal of the ExaSMR project is to enable extreme-fidelity simulation of SMRs, which have been a recent focus of engineering development. These reactors are of great interest due to their ability to economically provide consistent baseload power while avoiding many of the downsides of traditional (large) reactor designs. Part of the ExaSMR project was to define a representative challenge problem. The problem defined in this input dataset to OpenMC is a realistic SMR geometry that represents the NuScale reactor design recently licensed by the US Nuclear Regulatory Commission. The SMR problem is also defined with the goal of depletion calculations in mind, where hundreds of nuclides are present in the reactor fuel, and isoptic content lists are maintained for hundreds of thousands of unique fuel burnup regions throughout the reactor core. While the SMR problem is defined in-depth in Reference [1], we give an overview of additional key input parameters below that illustrate the difficulty of the simulation:   <table> <thead> <tr> <th scope="col">Parameter</th> <th scope="col">Value</th> </tr> </thead> <tbody> <tr> <td>Number of unique materials</td> <td>195,368</td> </tr> <tr> <td>Number of unique fuel materials</td> <td>195,360</td> </tr> <tr> <td>Number of depletion tally regions</td> <td>195,360</td> </tr> <tr> <td>Number of nuclides in each fuel region</td> <td>244</td> </tr> <tr> <td>Number of nuclides in simulation</td> <td>281</td> </tr> </tbody> </table> Overall, the ExaSMR challenge problem forms an ideal basis for performance analysis due to its relevance to the nuclear industry and its prominence as a high performance computing "hero problem" as part of ECP. For these reasons, we will use the SMR problem as the main basis of our performance analysis and scaling challenges. The model is also available in python format at: <a href="https://github.com/mit-crpg/ecp-benchmarks/tree/fom-model">https://github.com/mit-crpg/ecp-benchmarks/tree/fom-model</a> References: [1] Kord Smith. 2017. NuScale Small Modular Reactor (SMR) Progression Problems for the ExaSMR Project. Milestone Report WBS 1.2.1.08 ECP-SE-08-43. Exascale Computing Project.

opencc-by-4.0Apr 2023View details →

Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAP) Database

A commonly used method to examine the relationship between global water consumption and production is input--output analysis. However, between approximately 70% and 90% of freshwater consumption occurs in agricultural primary production, which is often represented by only a small percentage of the total number of sectors in input-output databases. In addition, the assessment of the impact of water consumption is usually carried out at the national level. Therefore, the primary objective of the Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAP) approach was to improve assessments of water use and its impacts in input-output analysis. To achieve this objective, a global spatial model of agricultural primary production MapSPAM (IFPRI, 2019) was integrated into the existing input-output database GLORIA (Lenzen et al., 2017, 2021) via prorating. The resulting IO-GHAAPP approach includes (1) a disaggregated input-output database and novel environmental extensions for freshwater consumption and scarcity. The IO-GHAAPP database consists of 150 categories and 164 regions, resulting in a total of 24,600 region-category combinations. Forty-two of the categories are dedicated to agricultural primary production (28%). In comparison, the source input--output data consist of 120 categories and 164 regions, resulting in a total of 19,680 region-category combinations, of which 14 are dedicated to agricultural primary production (12%).   Please cite as: Bunsen, Jonas, Vlad Coroamă, and Matthias Finkbeiner. 2023. ‘Input-Output Global Hybrid Analysis of Agricultural Primary Production (IO-GHAAPP) Database’. Sustainability 15 (2). <a href="https://doi.org/10.3390/su15129351">https://doi.org/10.3390/su15129351</a>.   References: <ul> <li>IFPRI. 2019. ‘Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0’. Harvard Dataverse. <a href="https://doi.org/10.7910/DVN/PRFF8V">https://doi.org/10.7910/DVN/PRFF8V</a>.</li> <li>Lenzen, Manfred, Arne Geschke, Muhammad Daaniyall Abd Rahman, Yanyan Xiao, Jacob Fry, Rachel Reyes, Erik Dietzenbacher, et al. 2017. ‘The Global MRIO Lab - Charting the World Economy’. Economic Systems Research 29 (2): 158–86. <a href="https://doi.org/10.1080/09535314.2017.1301887">https://doi.org/10.1080/09535314.2017.1301887</a>.</li> <li>Lenzen, Manfred, Arne Geschke, James West, Jacob Fry, Arunima Malik, Stefan Giljum, Llorenç Milà i Canals, et al. 2021. ‘Implementing the Material Footprint to Measure Progress towards Sustainable Development Goals 8 and 12’. Nature Sustainability, December. <a href="https://doi.org/10.1038/s41893-021-00811-6">https://doi.org/10.1038/s41893-021-00811-6</a>.</li> </ul>

Bacteriorhodopsin TR-SFX input data for LPSA.

Input matrices for the low-pass spectral analysis of a time-resolved serial femtosecond crystallography data set from bacteriorhodopsin. T_sel_sparse_light.jbl: bR TR-SFX intensities (m Bragg points x S time points) ts_sel_light.jbl: associated timestamps in femtoseconds (S values) miller_h_light.jbl, miller_k_light.jbl and miller_l_light.jbl: m-element vectors with Miller indices. dT_bst_sparse_light_mirrored.jbl: intensity deviations derived from T_sel_sparse_light.jbl, by subtracting reflection-dependent time averages and applying a scale factor to account for the different redundancy of observations. To overcome the LPSA cyclic boundary conditions, 2S frames are generated, with frames S+1, ..., 2S corresponding to frames 1, ..., S in reversed order. ts_sel_light_mirrored: 2S timestamps associated to the frames in dT_bst_sparse_light_mirrored.jbl. Values 1, ..., S are the experimental timestamps in ts_sel_ligth.jbl, and values S+1, ..., 2S are fictitious. I_dark_avg.mtz: merged resting state data for map calculation.

Data and Code for "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application"

This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application" authored by Juan José Price and Arne Henningsen and published in the Journal of Productivity Analysis (DOI: <a href="https://doi.org/10.1007/s11123-023-00684-1">10.1007/s11123-023-00684-1</a>). We conducted the empirical analysis with the "R" statistical software (version 4.3.0) using the add-on packages "combinat" (version 0.0.8), "miscTools" (version 0.6.28), "quadprog" (version 1.5.8), sfaR (version 1.0.0), stargazer (version 5.2.3), and "xtable" (version 1.8.4) that are available at CRAN. We created the R package "micEconDistRay" that provides the functions for empirical analyses with ray-based input distance functions that we developed for the above-mentioned paper. Also this R package is available at CRAN (https://cran.r-project.org/package=micEconDistRay). This replication package contains the following files and folders: <ul> <li>README This file</li> <li>MuseumsDk.csv The original data obtained from the Danish Ministry of Culture and from Statistics Denmark. It includes the following variables: <ul> <li>museum: Name of the museum. </li> <li>type: Type of museum (Kulturhistorisk museum = cultural history museum; Kunstmuseer = arts museum; Naturhistorisk museum = natural history museum; Blandet museum = mixed museum).</li> <li>munic: Municipality, in which the museum is located.</li> <li>yr: Year of the observation.</li> <li>units: Number of visit sites.</li> <li>resp: Whether or not the museum has special responsibilities (0 = no special responsibilities; 1 = at least one special responsibility).</li> <li>vis: Number of (physical) visitors.</li> <li>aarc: Number of articles published (archeology).</li> <li>ach: Number of articles published (cultural history).</li> <li>aah: Number of articles published (art history).</li> <li>anh: Number of articles published (natural history).</li> <li>exh: Number of temporary exhibitions.</li> <li>edu: Number of primary school classes on educational visits to the museum.</li> <li>ev: Number of events other than exhibitions.</li> <li>ftesc: Scientific labor (full-time equivalents).</li> <li>ftensc: Non-scientific labor (full-time equivalents).</li> <li>expProperty: Running and maintenance costs [1,000 DKK].</li> <li>expCons: Conservation expenditure [1,000 DKK]. </li> <li>ipc: Consumer Price Index in Denmark (the value for year 2014 is set to 1).</li> </ul> </li> <li>prepare_data.R This R script imports the data set MuseumsDk.csv, prepares it for the empirical analysis (e.g., removing unsuitable observations, preparing variables), and saves the resulting data set as DataPrepared.csv.</li> <li>DataPrepared.csv This data set is prepared and saved by the R script prepare_data.R. It is used for the empirical analysis.</li> <li>make_table_descriptive.R This R script imports the data set DataPrepared.csv and creates the LaTeX table /tables/table_descriptive.tex, which provides summary statistics of the variables that are used in the empirical analysis.</li> <li>IO_Ray.R This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions with the 'optimal' ordering of outputs, imposes monotonicity on this distance function, creates the LaTeX table /tables/idfRes.tex that presents the estimated parameters of this function, and creates several figures in the folder /figures/ that illustrate the results.</li> <li>IO_Ray_ordering_outputs.R This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions, imposes monotonicity for each of the 720 possible orderings of the outputs, and saves all the estimation results as (a huge) R object allOrderings.rds.</li> <li>allOrderings.rds (not included in the ZIP file, uploaded separately) This is a saved R object created by the R script IO_Ray_ordering_outputs.R that contains the estimated ray-based Translog input distance functions (with and without monotonicity imposed) for each of the 720 possible orderings.</li> <li>IO_Ray_model_averaging.R This R script loads the R object allOrderings.rds that contains the estimated ray-based Translog input distance functions for each of the 720 possible orderings, does model averaging, and creates several figures in the folder /figures/ that illustrate the results.</li> <li>/tables/ This folder contains the two LaTeX tables table_descriptive.tex and idfRes.tex (created by R scripts make_table_descriptive.R and IO_Ray.R, respectively) that provide summary statistics of the data set and the estimated parameters (without and with monotonicity imposed) for the 'optimal' ordering of outputs.</li> <li>/figures/ This folder contains 48 figures (created by the R scripts IO_Ray.R and IO_Ray_model_averaging.R) that illustrate the results obtained with the 'optimal' ordering of outputs and the model-averaged results and that compare these two sets of results.</li> </ul>

opencc-by-4.0Feb 2023View details →

Example Inputs for PIPEFISH Spatial Transcriptomics Pipeline Tool

This repository contains example input data, including raw images, codebooks,  parameters, and segmentation labels needed to run the FISH spatial transcriptomics pipeline tool <a href="https://github.com/hubmapconsortium/spatial-transcriptomics-pipeline">PIPEFISH</a>. The datasets contained are: <ul> <li>in situ sequencing (ISS) of a whole coronal slice of a mouse brain (50 genes). <a href="https://www.biorxiv.org/content/10.1101/2021.10.12.464086v1">Link to publication</a>.</li> </ul> Gataric, M., Park, J.S., Li, T., Vaskivskyi, V., Svedlund, J., Strell, C., Roberts, K., Nilsson, M., Yates, L.R., Bayraktar, O. and Gerstung, M., 2021. PoSTcode: Probabilistic image-based spatial transcriptomics decoder. bioRxiv, pp.2021-10. <ul> <li>MERFISH of human U2-OS cell cultures (130 genes). <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5047202/">Link to publication</a>.</li> </ul> Moffitt, J.R., Hao, J., Wang, G., Chen, K.H., Babcock, H.P. and Zhuang, X., 2016. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proceedings of the National Academy of Sciences, 113(39), pp.11046-11051. <ul> <li>seqFISH of a developing mouse embryo (351 genes). <a href="https://www.nature.com/articles/s41587-021-01006-2">Link to publication</a>.</li> </ul> Lohoff, T., Ghazanfar, S., Missarova, A., Koulena, N., Pierson, N., Griffiths, J.A., Bardot, E.S., Eng, C.H., Tyser, R.C.V., Argelaguet, R. and Guibentif, C., 2022. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nature biotechnology, 40(1), pp.74-85. In order to correctly format the inputs, run the prep_input.py script for the dataset you wish to run while in the same directory as the script.

dryad36/100

Coarse woody debris accelerates the decomposition of deadwood inputs across temperate forest

Wood decomposition is regulated by multiple controls, including climate and wood traits, that vary at local to regional scales. Yet decomposition rates differ dramatically when these controls do not. Fungal community dynamics are often invoked to explain these differences, suggesting that knowledge of ecosystem properties that influence fungal communities will improve understanding and projection of wood decomposition. We hypothesize that deadwood inputs decompose faster in forests with higher stocks of downed coarse woody material (CWM) because CWM is a resource from which lignocellulolytic fungi rapidly colonize new inputs. To test this hypothesis, we measure decomposition of 1,116 pieces of fine woody material (FWM) of five species, incubated for 13 to 49 months at five locations spanning 10°-latitude in eastern U.S. forest. We place FWM pieces near and far from CWM across observational transects and experimental common gardens. Soil temperature positively affects location-level mean decomposition rates, but these among-location differences are smaller than within-location variation in decomposition. Some of this variability is caused by CWM, where FWM pieces next to CWM decompose more rapidly. These effects are greater with time of incubation and lower initial wood density of FWM. The effect size of CWM is of the same relative magnitude as for the known controls of temperature, deadwood density and diameter. Abundance data for CWM is available for many forests and hence may be an ecosystem variable amenable for inclusion in decomposition models. Our findings suggest that conservation efforts to rebuild depleted CWM stocks in temperate forests may accelerate decomposition of fresh deadwood inputs.

opencc-zeroMay 2023View details →

dryad36/100

Input files for targeted MD and umbrella sampling of cardiac thin filament

The cardiac thin filament is comprised of F-actin, tropomyosin, and troponin (cTn). cTn is composed of three subunits: troponin C (cTnC), troponin I (cTnI), and troponin T (cTnT). To computationally study the effect of the thin filament on cTn activation events, we employed targeted molecular dynamics followed by umbrella sampling using a model of the thin filament to measure the thermodynamics of cTn transition events. Our simulations revealed that the thin filament causes an increase in the free energy required to open the cTnC hydrophobic patch and causes a more favorable interaction between this region and the cTnI switch peptide. Mutations to the cTn complex can lead to cardiomyopathy, a collection of diseases that present clinically with symptoms of hypertrophy or dilation of the cardiac muscle, leading to impairment of the heart's ability to function normally, and ultimately myocardial infarction or heart failure. Upon introduction of cardiomyopathic mutations to R145 of cTnI, we observed a general decrease in the free energy of opening the cTnC hydrophobic patch, which is on par with previous experimental results. These mutations also exhibited a decrease in electrostatic interactions between cTnI-R145 and actin-E334. After introduction of a small molecule to the wild-type cTnI-actin interface to intentionally disrupt intersubunit contacts, we successfully observed similar thermodynamic consequences and disruptions to the same protein-protein contacts as observed with the cardiomyopathic mutations. Computational studies utilizing the cTn complex in isolation would have been unable to observe these effects, highlighting the importance of using a more physiologically relevant thin filament model to investigate the global consequences of cardiomyopathic mutations to the cTn complex.

opencc-zeroMay 2023View details →

Emission input data for WRF-CMAQ

Emission input data for WRF-CHIMERE in eastern China during 2017.

Emission input data for WRF-Chem

Emission input data for WRF-Chem in eastern China during 2017.

Analysis scripts and PFLOTRAN input files for "Impacts of permeability heterogeneity and background flow on supercritical CO2 dissolution in the deep subsurface"

Supporting files for manuscript Impacts of permeability heterogeneity and background flow on supercritical CO2 dissolution in the deep subsurface (preprint published at https://arxiv.org/abs/2305.12575). Contents: <ul> <li>PFLOTRAN input files (*.in) for the simulations that were used in the manuscript.</li> <li>The corresponding heterogeneous permeability fields for those simulations (*.h5).</li> <li>The CO2 property database for use with the PFLOTRAN MPHASE module.</li> <li>Python scripts for generation of the random fields and quantification of uptake rate.</li> <li>Jupyter notebooks for generation of summary figures.</li> <li>Excel spreadsheet summarizing the output of each of the simulations.</li> </ul>

opencc-by-4.0Dec 2022View details →

Molecular dynamics simulation input files: Histone Tail Electrostatics Modulate E2-E3 Enzyme Dynamics: A Gateway to Regulate Ubiquitination Machinery

Molecular dynamics simulation input files: Histone Tail Electrostatics Modulate E2-E3 Enzyme Dynamics: A Gateway to Regulate Ubiquitination Machinery (<a href="https://zenodo.org/record/7423328">https://zenodo.org/record/7423328</a>)

Input and output data for the paper "Evaluating the German PV auction program: The secrets of individual bids revealed"

Batz Liñeiro, T., Müsgens, F., (2021). Energy Policy <a href="http://doi.org/10.1016/j.enpol.2021.112618">doi.org/10.1016/j.enpol.2021.112618</a> ABSTRACT Auctions have become the primary instrument for promoting renewable energy around the world. However, the data published on such auctions are typically limited to aggregated information (e.g., total awarded capacity, average payments). These data constraints hinder the evaluation of realisation rates and other relevant auction dynamics. In this study, we present an algorithm to overcome these data limitations in German renewable energy auction programme by combining publicly available information from four different databases. We apply it to the German solar auction programme and evaluate auctions using quantitative methods. We calculate realisation rates and—using correlation and regression analysis—explore the impact of PV module prices, competition, and project and developer characteristics on project realisation and bid values. Our results confirm that the German auctions were effective. We also found that project realisation took, on average, 1.5 years (with 28% of projects finished late and incurring a financial penalty), nearly half of projects changed location before completion (again, incurring a financial penalty) and small and inexperienced developers could successfully participate in auctions. Description The data package offered in this publication comprises input, processing, and output files, accompanied by the corresponding R-codes used for data processing at different stages. Among the various data outputs, the "Auctions" sheet within the file "2 Auction Realizations Solar-2" holds particular significance for users. Within this sheet, users can identify the realized projects, their respective IDs, and the reported individual bid values. However, it is recommended to refer to the attached publication to gain a comprehensive understanding of the bid-value identification process. For users seeking to update the results, the input files can be easily updated by referring to partner publications that share the same file names. These partner publications include the <a href="https://zenodo.org/record/7945029">UnitRegister</a>, <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and <a href="https://zenodo.org/record/8013071">TariffRegister </a>datasets.

Input and output data for the paper "Evaluating the German onshore wind auction programme: An analysis based on individual bids"

Batz Liñeiro, T., Müsgens, F., (2023). Energy Policy <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> ABSTRACT Auctions are a highly demanded policy instrument for the promotion of renewable energy sources. Their flexible structure makes them adaptable to country-specific conditions and needs. However, their success depends greatly on how those needs are operationalised in the design elements. Disaggregating data from the German onshore wind auction programme into individual projects, we evaluated the contribution of auctions to the achievement of their primary (deployment at competitive prices) and secondary (diversity) objectives and have highlighted design elements that affect the policy's success or failure. We have shown that, in the German case, the auction scheme is unable to promote wind deployment at competitive prices, and that the design elements used to promote the secondary objectives not only fall short at achieving their intended goals but create incentives for large actors to game the system. Description The data package offered in this publication comprises input, processing, and output files, accompanied by the corresponding R-codes used for data processing at different stages. Among the various data outputs, the "Auctions" sheet within the file "3 Auction Realizations Onshore Wind" holds particular significance for users. Within this sheet, users can identify the realized projects, their respective IDs, and the reported individual bid values (BV). However, it is recommended to refer to the attached publication to gain a comprehensive understanding of the bid-value identification process. For users seeking to update the results, the input files can be easily updated by referring to partner publications that share the same file names. These partner publications include the <a href="https://zenodo.org/record/7945029">UnitRegister</a>, <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and <a href="https://zenodo.org/record/8013071">TariffRegister </a>datasets.

Vortex input files for PVA on Southern Resident Killer Whales

Input files for publication: Lacy, R.C., R. Williams, E. Ashe, K.C. Balcomb III, L.J.N. Brent, C.W. Clark, D.P. Croft, D.A. Giles, M. MacDuffee, and P.C. Paquet. 2017. Evaluating anthropogenic threats to endangered killer whales to inform effective recovery plans. Scientific Reports 7:14119. doi: 10.1038/s41598-017-14471-0.

Results and input data related to the case study 3 of the openENTRANCE project - version 20062023

This dataset comprises the main results of case study 3 of the openENTRANCE project. Additionally, input data related to the power generation system and renewables profiles are included in the dataset.

openapgl-v3Jun 2023View details →

coSIF Input Data

This compressed file contains all input data needed to reproduce the results in Jacobson et al. (2023) Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data. In particular, the following are included: <ul> <li> The directory `OCO2_L2_Lite_SIF_10r` is a subset of daily OCO-2 SIF Lite files (version 10r) for February, April, July, and October 2021. The full dataset is available at https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_SIF_10r/summary. </li> <li> The directory `OCO2_L2_Lite_FP_10r` is a subset of daily OCO-2 XCO2 Lite files (version 10r) for March, May, August, and November 2021. The full dataset is available at https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_10r/summary. </li> <li> The directory `MCD12C1v061` contains the MODIS Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6.1 annual data product for 2021 only. The full dataset is available at https://lpdaac.usgs.gov/products/mcd12c1v061/. </li> </ul>

ICPP23 Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication Input Graphs

Input graphs and checkpoints used for generating results in the ICPP 23 paper "Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication".

opencc-by-4.0Mar 2023View details →

mitgcm_code_input_and_output_for_amundsen_runoff_study

This repository contains all inputs and code to carry out subglacial runoff-forced experiments of the Amundsen Sea Embayment using the modelling framework MITgcm. expt_code/ contains specialised code files for the experiment modified_code/ contains main code trunk files that were modified from checkpoint 67v input/ contains experiment-specific binary and parameter files outputs/ contains .mat files which contain all relevant output for analysis