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855 results for “model system”
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Data set for the integrated Climate, Land, Energy and Water systems modelling exercise RCLEWs in OSeMOSYS
<p>This dataset refers to the modelling exercise (version01_210616RCLEWs). The dataset contains the OSeMOSYS code used to run the modelling exercise, the model input data, the scenarios model data files, and the results. The code for the results visualization is available at https://github.com/KTH-dESA/teaching-CLEWs_visualization.</p> <p>This is an update of version 01_210827 available at: https://doi.org/10.5281/zenodo.5293834</p>
Data for publication "Statistical characteristics of extreme daily precipitation during 1501 BCE - 1849 CE in the Community Earth System Model".
<p>Here, the data used in Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). "Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model" in <em>Climate of the Past </em>(<a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-6</a>) are provided.</p> <p>Two simulations covering the period 1501 BCE - 2008 CE are performed with CESM 1.2.2: the orbital-only and the full-forcing simulations. The full-forcing transient simulation includes the new long record of volcanic eruptions (<a href="https://doi.org/10.1594/PANGAEA.928646">https://doi.org/10.1594/PANGAEA.928646</a>) that covers the last 3500 years. The output from the simulations is used to examine the long-term variability and characteristics of daily extreme precipitation during 1501BCE-1849 CE.</p> <p>The following files are provided:</p> <ul> <li> <strong>CESM122.transient.PRECT.anom.above99th.1501BCE-1849CE_I and II</strong>: Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the full forcing simulation. The file is split into two parts, with the first file containing the first 50% of extremes (I) and the second file containing the rest 50% (II).</li> <li><strong>CESM122.orbital.PRECT.anom.above99th.1501BCE-1849CE I and II:</strong> Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the orbital-only simulation.</li> <li> <strong>CESM122.transient.variables.mon.1979-2008CE:</strong> monthly precipitation, temperature, and geopotential height at 500 hPa for 1979-2008CE from the full-forcing simulation.</li> <li><strong>CESM122.trans.variable_names.years:</strong> Monthly variables from the full-forcing simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE. The variables are solar insolation (SOLIN), clear-sky net surface shortwave radiation (FSNSC), geopotential height at 500hPa (Z500), and surface temperature (TS).</li> <li><strong>CESM122.orbital.variable_names.years:</strong> Monthly variables from the orbital-only simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE.</li> <li><strong>CESM122.*.log-likelihood-GPDmodel-ExtForcing</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for external forcings.</li> <li> <strong>CESM122.*.log-likelihood-GPDmodel-ModesVar</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for modes of variability.</li> <li><strong>Evolk_EVA_distribution_1501BCE-2015CE</strong>: Distribution of volcanic aerosol for CAM5, produced based on Kim et al. (2021).</li> </ul> <p>If you use this dataset, please cite:</p> <p><em>Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model. Climate of the Past Discussions, 1-38. <a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-61</a></em></p>
Radiocarbon in the land and ocean components of the Community Earth System Model: data to prepare figures
<p>The files contain the data to plot the graphics displayed in the publication by Frischknecht, T., Ekici, A., Joos, F. Radiocarbon in the land and ocean components of the Community Earth System Model, Global Biogeochemical Cycles, 2022, in press.</p>
Global Environmental and Weather data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>cutouts </strong>are spatiotemporal subsets of the Earth weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the <strong>year 2013</strong>. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>). They can be reproduced or extended for other weather years (approx. 40-50 years) around the world by using the <a href="https://github.com/pypsa-meets-africa/pypsa-africa/blob/main/scripts/build_cutout.py">build.cutout.py</a></p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul>
Modeled temperature and Marine heatwaves intensity in the coastal Northern Humboldt Current System
<p>This dataset includes the tridimensional modeled temperature and associated research data that support the results of the article "<strong><em>Comprehensive characterization of Marine Heatwaves in a coastal Northern Humboldt Current System regional model over recent decades</em></strong>".</p> <p>Specifically, it consists of three files (NetCDF format):<br> i) Northern_MHWs.nc, this file contains the daily modeled temperature (from 2000 to 2019) within the northern domain of analysis (3-8°S) within the 250 km nearshore band for each vertical layer ranging from 0 to 250m depth. In addition, daily snapshots of MHW intensity are also included by depth.<br> ii) Central_MHWs.mat, similar to the previous file, but for the central domain of analysis, from 8 to 13°S.<br> iii) Southern_MHWs.mat, similar to the previous file, but for the southern domain of analysis, from 13 to 18°S.</p>
Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions
<p>Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios.</p> <p>These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):</p> <p> </p> <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre> <p> </p> <p>This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p> </p>
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
Reference Data Set: Electricity, Heat, and Gas Sector Data for Modeling the German System
<p>This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector).</p> <p>While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references).</p> <p>The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D.</p>
iGEM: a model system for team science and innovation
<p>This dataset is extracted from the <strong>international Genetically Engineered Machine (iGEM) competition </strong>between years 2008 and 2018, and can be used as a model system for studying team science and innovation. It is described at length in <a href="https://arxiv.org/abs/2310.19858">this article</a>.</p> <p>The dataset encompass detailed records from the iGEM competition, capturing various aspects of team participation and achievements. Specifically, the <strong>Team Information</strong> dataset (<strong>teams_table.csv</strong>) provides insights into team characteristics and achievements, including medal status and region of origin. <strong>User Information</strong> (<strong>users_table.csv</strong>) offers a look into individual participants, detailing their roles in the team. <strong>Awards Information</strong> (<strong>awards_table.csv</strong>) and <strong>Medal Criteria</strong> (<strong>medals_criteria.csv</strong>) lay out the awards teams have garnered and the standards for medal attainment. The <strong>BioBricks Information</strong> (<strong>biobricks_table.csv</strong>) corresponds to the BioBrick sequences associated with each team, while <strong>Wiki Edits</strong> (<strong>wikis_table.csv</strong>) tracks the changes made by users on their team's (wiki) lab notebook. Finally, the <strong>Collaboration Network</strong> (<strong>collaboration_network.csv</strong>) corresponds to the weighted directed inter-team collaboration network collected using team mentions across team wikis.</p> <p>In addition to the structured dataframes above, we provide in <strong>team_wikis_full_text.zip</strong> the full texts of the wiki pages from the digital laboratory notebooks collaboratively edited by iGEM teams in the forms of wiki instances. There is a folder for each year from 2008-2018 and within which there are individual folders for each team. Each team folder has a file denoting the pagelist and two files for each page. One file is the html content, and the other the text content, extracted using the "KeepEverythingExtractor" option in the <em>boilerpipe.extract</em> library for processing and removing boilerplate content after webscraping.</p> <p> </p>
A Role-Based Access Control model in Modbus Scada systems. A centralized model approach
<p>A Role-Based Access Control model in Modbus Scada systems. A centralized model approach. The files included are:</p> <ul> <li>ASA configuration</li> <li>Router1 configuration</li> <li>Router2 configuration</li> <li>Router3 configuration</li> <li>RoleDB</li> <li>openssl.cnf arbitrary extension file</li> </ul>
Open and Lite Techno-economic Dataset for Long-term Energy Systems Modelling in the Republic of South Africa
<p>An open-source lite techno-economic dataset for long term energy systems modelling in the Republic of South Africa. Includes data on electricity generation and demand, electricity imports and exports, power transmission and distribution, residual capacity, capacity factor, operational lifetime, and fixed, variable and capital costs of electricity generation technologies. It also contains estimates for renewable potential and fossil fuel reserves in South Africa.</p>
Transformers for Modeling Physical Systems
<p>Data set associated with the publication <a href="https://arxiv.org/abs/2010.03957">Transformers for Modeling Physical Systems</a>. Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performance for many language related tasks, their applicability outside of the natural language processing field has been minimal. In this work, we propose the use of transformer models for the prediction of dynamical systems representative of physical phenomena. </p> <p>This data set includes data in HDF5 files for:</p> <p>Lorenz ODE:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_training_rk.tar.gz?versionId=c4bd1230-3b22-4d2e-83f0-357146a90423">lorenz_training_rk.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_valid_rk.tar.gz?versionId=3cf95dac-a75d-42d8-85ab-615f7a2ad67f">lorenz_valid_rk.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_test_rk.tar.gz?versionId=bbb4bd3d-33c9-4903-98ed-f7a926dc95db">lorenz_test_rk.tar.gz</a></li> </ul> <p>Flow Around a Cylinder:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_training.tar.gz?versionId=25bd1f3a-03b7-44d0-aa3f-afaffa6cd706">cylinder_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_valid.tar.gz?versionId=58a6e98d-be41-4603-91cb-9522d848cf57">cylinder_valid.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_test.tar.gz?versionId=a3c03293-0278-40ee-b181-2eb53a8d0b47">cylinder_test.tar.gz</a></li> </ul> <p>Gray-Scott Reaction-Diffusion:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_training.tar.gz">grayscott_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_valid.tar.gz?versionId=3bb8aa25-c9c8-494e-a206-e8d1fdb8a88f">grayscott_valid.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_test.tar.gz?versionId=d9cee8a6-b22f-44b9-ae1b-2433caf77e34">grayscott_test.tar.gz</a></li> </ul> <p>Rossler ODE:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/rossler_training.tar.gz?versionId=d44be9cf-8fa7-4eb2-8b6e-8ac129822f51">rossler_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/rossler_valid.tar.gz?versionId=93c658e3-235a-4150-b233-ed514d6c7467">rossler_valid.tar.gz</a></li> </ul> <p>As well as several pretrained embedding models for the Google Collab notebooks on <a href="https://github.com/zabaras/transformer-physx/">Github</a>:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_lorenz_pretrained.pth?versionId=cd30ff4e-34b3-4070-b346-718fb8526dac">embedding_lorenz_pretrained.pth</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_cylinder_pretrained.pth?versionId=69552efb-09e4-49d9-a224-ccc7cff91b86">embedding_cylinder_pretrained.pth</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_rossler_pretrained.pth?versionId=e7d1f4aa-3f31-45fe-91e4-8411e9cd634f">embedding_rossler_pretrained.pth</a></li> </ul> <p>See the Github repository for code base: <a href="https://github.com/zabaras/transformer-physx/">https://github.com/zabaras/transformer-physx/</a></p>
Dataset for simulation of a low-carbon urban energy system using the Backbone model
<p>The dataset contains the input data for cost optimization of an urban energy system. The case study has been described in the article "Impact of power-to-gas on the cost and design of the future low-carbon urban energy system" of Applied Energy.</p> <p>The dataset is in Microsoft Excel format. To make it available for GAMS, one should use e.g. the attached shell script (requires GAMS installation) to convert it to *.gdx file. The generation expansion model is available in the Git repository https://gitlab.vtt.fi/backbone/backbone (under branch projik/planet).</p>
Accompanying data for the open-source book Modeling of Hydrological Systems in Semi-Arid Central Asia
<p>This data set is used to reproduce examples in the open-source book <a href="https://hydrosolutions.github.io/caham_book/">"Modeling of Hydrological Systems in Semi-Arid Central Asia"</a> which is part of a free course on hydrological modeling in Central Asia. The course teaches how to use publicly available data to implement a hydrological model for climate impact studies (Marti et al., 2023). </p> <p>To use the data set to reproduce the examples in the book: Download the book from https://doi.org/10.5281/zenodo.6350042 and this data set to the same hierarchical level in your file system: </p> <p>|- caham_book<br> |- caham_data<br> |- AmuDarya<br> |- central_asia_domain<br> |- student_case_study_basins<br> |- SyrDarya</p> <p>You will need a working installation of R (https://www.r-project.org/) and a GUI (e.g. Posit, formerly RStudio https://posit.co/) to reproduce the scripted examples in the book. Once your software is set up, you can proceed to run the examples. </p> <p> </p>
Delphi Study: Exploring the Implications of Large Language Models on the Science System
<p><strong>Sample description:</strong> Our target audience consisted of researchers working in the fields of science, technology, and society with a specific interest in Large Language Models (LLMs).</p> <p><strong>Collection method: </strong>Participants were recruited through the professional and personal networks of the authors, as well as the Alexander von Humboldt Institute (HIIG), using a combination of generic emails via LimeSurvey and personal contacts.</p> <p><strong>Description. </strong>The aim of this study was to explore the impact of large language models, specifically ChatGPT, on scholarly practice and academic writing, targeting researchers and experts in the fields of artificial intelligence, science, and technology who publish their research and scientific work. The two-stage Delphi survey sought to identify and assess the potential opportunities and challenges associated with the use of ChatGPT in academic work and scientific writing, with a specific focus on research impact rather than university teaching. Phase 1 yielded 72 responses, while Phase 2 had 52 responses.</p> <p>To conduct our analysis, we developed two distinct codebooks (see Files ChatGPT Delphi Codebook Phase 1.csv and ChatGPT Delphi Codebook Phase 2.csv) for the Delphi study. The first codebook was created by examining approximately half of the responses, extracting relevant information, and generating codes through inductive reasoning. We then categorized and developed subcodes based on these initial codes, assigning them to each participant's answers using deductive reasoning. For example, when addressing the potential applications of ChatGPT and other language models (LLMs), we identified six subcategories with precise definitions and illustrative examples. The analysis in Phase 1 led to the formulation of ranking questions for Phase 2, focusing on determining the most frequently utilized applications of ChatGPT and other LLMs based on the established codes.</p> <p>During Phase 2, we introduced two additional open-ended questions to explore the impact of ChatGPT and LLMs on the scientific system and society, aiming to envision future scenarios. The analysis of these questions in the second codebook followed a similar approach to Phase 1, including inductive reasoning for code generation and deductive reasoning for assigning codes to the answers. We observed overlapping codes with the Phase 1 codebook and assigned them to the second codebook. Additionally, we noted a shift in the connotation of certain answers from neutral in Phase 1 to being perceived as either positive or negative consequences of ChatGPT and other LLMs. This observation prompted the bifurcation of specific codes to capture the nuanced perspectives. For instance, applications such as reducing administrative tasks initially seen as valuable aids for researchers were sometimes viewed as potential causes for job replacement, implying negative outcomes.</p> <p>For detailed information on the analytical approach employed, including references to these methodologies, please refer to the methodology chapter in the official publication.</p> <p><strong>Content</strong></p> <ol> <li> <p>Questionaire-ChatGPT-Delphi-Phase1-Limesurvey-Export.pdf – This file file is an exported version of the Phase 1 questionnaire from Limesurvey. It includes the description, socio demographic questions, content questions, and a request for participant naming.</p> </li> <li> <p>Questionaire-ChatGPT-Delphi-Phase2-Limesurvey-Export.pdf – This file file is an exported version of the Phase 2 questionnaire from Limesurvey. It includes the description, socio demographic questions, content questions, and a request for participant naming.</p> </li> <li> <p>ChatGPT Delphi - Results Phase 1.pdf – This file contains the responses and corresponding questions from Phase 1 of the Delphi study. The responses provided by the participants are in the form of open-ended answers. As part of this publication, we have ensured the anonymity of the participants.</p> </li> <li> <p>ChatGPT Delphi - Results Phase 2. pdf – This file contains the responses and corresponding questions from Phase 2 of the Delphi study. It encompasses the ranking answers provided by the participants, as well as two open-ended answers. To maintain anonymity consistently, all participants have been anonymized again in this publication of our results.</p> </li> <li> <p>ChatGPT Delphi Codebook Phase 1.pdf – This file contains the Phase 1 codebook, which presents the primary codes, their respective subcodes, detailed definitions, and noteworthy examples.</p> </li> <li> <p>ChatGPT Delphi Codebook Phase 2.pdf – This file contains the Phase 1 codebook, which provides a comprehensive overview of the primary codes within the given scenario. It includes their corresponding subcodes, detailed definitions, and notable examples to enhance understanding and interpretation.</p> </li> </ol>
Representing Socio-Economic Uncertainty in Human System Models
<p>This data repository is associated with the paper:<br> Morris,J., J. Reilly, S. Paltsev, A. Sokolov and K. Cox (2022): Representing socio-economic uncertainty in human system models. <em>Earth's Future</em>, In press.</p>
Data and code for gmd-2023-113 "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"
<p>Data and code for the paper "Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast"</p> <p>includes: </p> <p>The model is Community Earth System Model (v1.2.1) (provided by www.cesm.ucar.edu)</p> <p>Data assimilation code is initially provided by Data Assimilation Research Testbed (DART) (https://dart.ucar.edu/), some modifications are made to enable parameter estimation function of ocean background vertical diffusivity coefficients. And the programs and scripts for deal with OISST and EN4 profiles are also developed.</p> <p>The parameter sensitivity experiment results are saved as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012.nc">sensitive2008-2012.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/sensitive2008-2012salt.nc">sensitive2008-2012salt.nc</a> for temperature and salinity, respectively. And the python script to draw the results is </p> <p>The state estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/Temp_05-17.nc">Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>The parameter estimation results are provided as <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Temp_05-17.nc</a> and <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/PE_Temp_05-17.nc">PE_Salt_05-17.nc</a> for temperature and salinity, respectively.</p> <p>the estimated paremeter ensemble is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/parameters.nc">parameters.nc</a></p> <p>the python script for comparing the SE and PE results is <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/plot_analysis.py">plot_analysis.py</a></p> <p>the nino3.4 indices computed by the forecast experiment is saved in <a href="https://zenodo.org/api/files/6e5b4bfa-61cf-44bc-8e15-e62fab467ea6/fcst_correlation.nc">fcst_correlation.nc</a></p> <p> </p>
The coupled ice sheet-Earth system model Bern3D v3.0: Model output
<p>This dataset contains model output of climate and ice sheet variables for the simulations performed in the study:</p> <p>Pöppelmeier, F., Joos, F., Stocker, T. F. (2023). The coupled ice sheet-Earth system model Bern3D v3.0. Journal of Climate.</p> <p>2D and 3D output variables are available for the preindustrial (PI) and Last Glacial Maximum (LGM) control simulations. Timeseries output is provided for CO<sub>2</sub> experiments for which CO<sub>2</sub> concentrations were increased to 2 and 4 times PI concentrations with rates of 0.5, 1, and 2% per year. Timeseries output is also provided for the simulation of the entire last glacial cycle in the standard setup and with logarithmically scaled dust for the aerosol radiative forcing. More details are provided in the above mentioned manuscript.</p>
Bern3D model output data from idealized co2 increase-decrease simulations to investigate reversibility in the Earth system
<p>The data described below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations to investigate reversibilty and hysteresis for different maximum co2 forcings.</p> <p><br> The data are provided as .csv and .nc files<br> The first row in the .csv files contains the header, which describes the variable. The naming convention is as follows:</p> <p>c#k#_VARIABLE</p> <p>c# indicates the maximum co2 as times pre-industrial (c2 to c5)<br> k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5)<br> and VARIABLE indicates the value of the respective variable, which are:<br> co2: change in atmospheric co2 concentration in [ppm]<br> amoc: change in maximum of the Atlantic meridional overturning circulation in [Sv]<br> ohc: change in ocean heat content in [10^24 J]<br> seaice: sea-ice area remaining as fraction of the pre-industrial cover<br> Om_arag: fraction of water with Omega_arag > 3 in the upper 175 m<br> o2_thermo: change in thermocline (200-600 m) oxygen concentration in [mmol m^-3]<br> for each variable a separate file exists where the variable and co2 are provided.</p> <p><br> Spatial data to create the maps of hysteresis on a grid-cell basis are provided for the two scenarios as .nc files. The naming is as follows:</p> <p>c#k#_hyst_o2thermo.nc</p> <p>where c# corresponds again to maximum co2 as times pre-industrial and k# to the equilibrium climate sensitivity. The .nc files contain the coordinate (latitude, longitude) centers (lat_t, lon_t) and edges (lat_u, lon_u) as well as the hysteresis area (hystA_o2thermo) in [mmol m^-3].</p> <p><br> The files can be readily importet in python, for example, by:<br> import pandas as pd<br> import xarray as xr<br> <br> # for the .csv files<br> df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)<br> <br> # for the .nc files<br> ds = xr.open_dataset('path+filename')</p> <p><br> For additional information or in case of questions please contact:<br> Aurich Jeltsch-Thömmes<br> aurich.jeltsch-thoemmes@unibe.ch</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.