cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11588/data/qdoc4e/5xduw5
title: opossum_SN209.tar

description: :unav
DOI: 10.11588/data/qdoc4e/9iukc1
title: opossum_SN174.tar

description: :unav
DOI: 10.11588/data/qdoc4e/blc1hf
title: opossum_SN208.tar

description: :unav
DOI: 10.11588/data/qdoc4e/t3utlp
title: opossum_SN203.tar

description: :unav
DOI: 10.11588/data/qdoc4e/zmicwa
title: opossum_SN204.tar

description: :unav
DOI: 10.11588/data/qdoc4e/1hnmkj
title: opossum_SN258.tar

description: :unav
DOI: 10.11588/data/qdoc4e/ayimel
title: opossum_SN210.tar

description: :unav
DOI: 10.11588/data/qdoc4e/curw9q
title: opossum_SN309.tar

description: :unav
DOI: 10.11588/data/qdoc4e/dpaeba
title: mouse_SN006.tar

description: :unav
DOI: 10.11588/data/qdoc4e/gbeljh
title: mouse_SN010.tar

description: :unav
DOI: 10.11588/data/qdoc4e/ryyvl2
title: mouse_SN013.tar

description: :unav
DOI: 10.11588/data/qdoc4e/3vro4g
title: mouse_SN025.tar

description: :unav
DOI: 10.11588/data/qdoc4e/4cb2xa
title: mouse_SN032.tar

description: :unav
DOI: 10.11588/data/qdoc4e/nh4bgn
title: mouse_SN014.tar

description: :unav
DOI: 10.11588/data/qdoc4e/noqhyu
title: mouse_SN016.tar

description: :unav
DOI: 10.11588/data/qdoc4e/pm2zcz
title: mouse_SN017.tar

description: :unav
DOI: 10.11588/data/qdoc4e/wwkq3o
title: mouse_SN015.tar

description: :unav
DOI: 10.11588/data/qdoc4e/ppwibg
title: mouse_SN038.tar

description: :unav
DOI: 10.11588/data/qdoc4e/fvj7x1
title: mouse_SN044.tar

description: :unav
DOI: 10.11588/data/qdoc4e/nnmblr
title: mouse_SN063.tar

description: :unav
DOI: 10.11588/data/qdoc4e/npeotv
title: mouse_SN043.tar

description: :unav
DOI: 10.11588/data/qdoc4e/obb8u9
title: mouse_SN039.tar

description: :unav
DOI: 10.11588/data/qdoc4e/qrgcck
title: mouse_SN069.tar

description: :unav
DOI: 10.11588/data/qdoc4e/yccspr
title: mouse_SN073.tar

description: :unav
DOI: 10.11588/data/qdoc4e/cd0f5f
title: mouse_SN099.tar

description: :unav
DOI: 10.11588/data/qdoc4e/ibxaqc
title: mouse_SN088.tar

description: :unav
DOI: 10.11588/data/qdoc4e/tapygw
title: mouse_SN101.tar

description: :unav
DOI: 10.11588/data/qdoc4e/8ayhjh
title: mouse_SN108.tar

description: :unav
DOI: 10.11588/data/qdoc4e/8pwpyi
title: mouse_SN106.tar

description: :unav
DOI: 10.11588/data/qdoc4e/hhxgnq
title: mouse_SN102.tar

description: :unav
DOI: 10.11588/data/qdoc4e/kqwwcy
title: mouse_SN104.tar

description: :unav
DOI: 10.11588/data/qdoc4e/yjm3qy
title: mouse_SN126.tar

description: :unav
DOI: 10.11588/data/qdoc4e/8v3bw1
title: mouse_SN252.tar

description: :unav
DOI: 10.11588/data/qdoc4e/cxrkm0
title: mouse_SN150.tar

description: :unav
DOI: 10.11588/data/qdoc4e/n4z3cw
title: mouse_SN251.tar

description: :unav
DOI: 10.11588/data/qdoc4e/nxbbrm
title: mouse_SN128.tar

description: :unav
DOI: 10.11588/data/qdoc4e/wpezdd
title: mouse_SN250.tar

description: :unav
DOI: 10.11588/data/qdoc4e/yngcfj
title: mouse_SN127.tar

description: :unav
DOI: 10.11588/data/qdoc4e
title: Cellular development and evolution of the mammalian cerebellum [Research Data]

description: The expansion of the neocortex, a hallmark of mammalian evolution1,2, was accompanied by an increase in cerebellar neuron numbers3. However, little is known about the evolution of the cellular programs underlying cerebellum development in mammals. In this study, we generated single-nucleus RNA-sequencing data for ~400,000 cells to trace cerebellum development from early neurogenesis to adulthood in human, mouse, and the marsupial opossum. We established a consensus classification of the cellular diversity in the developing mammalian cerebellum and validated it by spatial mapping in the fetal human cerebellum. Our cross-species analyses revealed a largely conserved developmental dynamics of cell type generation, except for Purkinje cells, where we observed an expansion of early-born subtypes in the human lineage. Global transcriptome profiles, conserved cell state markers, and gene expression trajectories across neuronal differentiation show that cerebellar cell type-defining programs have been overall preserved for at least ~160 million years. However, we also identified many orthologous genes that gained or lost expression in cerebellar neural cell types in one of the species, or evolved new expression trajectories during neuronal differentiation, indicating widespread gene repurposing at the cell type level. Altogether, our study unveils shared and lineage-specific gene expression programs governing the development of cerebellar cells, and expands our understanding of mammalian brain evolution.
DOI: 10.11588/data/qdoc4e/1hgyap
title: mouse_SN044.tar

description: :unav
DOI: 10.11588/data/qdoc4e/6blxdu
title: mouse_SN043.tar

description: :unav
DOI: 10.6084/m9.figshare.24581740
title: Data analysis files with R code from Maths anxiety and subjective perception of control, value and success expectancy in mathematics

description: please rename the file to have ZIP extension and extract
DOI: 10.6084/m9.figshare.24581746
title: Data preprocessing files with Matlab code from Maths anxiety and subjective perception of control, value and success expectancy in mathematics

description: please rename the file to have ZIP extension and extract
DOI: 10.6084/m9.figshare.24581746.v2
title: Data preprocessing files with Matlab code from Maths anxiety and subjective perception of control, value and success expectancy in mathematics

description: please rename the file to have ZIP extension and extract
DOI: 10.6084/m9.figshare.24581740.v1
title: Data analysis files with R code from Math anxiety and subjective perception of control, value and success expectancy in mathematics

description: please rename the file to have ZIP extension and extract
DOI: 10.6084/m9.figshare.24581746.v1
title: Data preprocessing files with Matlab code from Math anxiety and subjective perception of control, value and success expectancy in mathematics

description: please rename the file to have ZIP extension and extract
DOI: 10.5281/zenodo.10223755
title: Supplementary Material - Systematic Mapping Study - Information and Software Technology

description: Supplementary material - Article title: "Constructing the structure of expert-based Bayesian networks in the context of software engineering: A systematic mapping study" - Information and Software Technology. This file contains the detailed procedure employed in the systematic mapping.
DOI: 10.25984/2222585
title: Error-Level-Controlled Synthetic Forecasts for Renewable Generation

description: Renewable energy resources, including solar and wind energy, play a significant role in sustainable energy systems. However, the inherent uncertainty and intermittency of renewable generation pose challenges to the safe and efficient operation of power systems. Recognizing the importance of short-term (hours ahead) renewable generation forecasting in power systems operation, it becomes crucial to address the potential inaccuracies in these forecasts. To systematically evaluate the performance of controllers in the presence of imperfect forecasts, we generate synthetic forecasts using actual renewable generation profiles (one from solar and one from wind). These synthetic forecasts incorporate different levels of statistical error, allowing us to control and manipulate the accuracy of the predictions. The primary objective is to employ synthetic forecasts with controlled yet realistic error levels to systematically investigate how controllers adapt to variations in forecast accuracy, providing valuable insights into their robustness and effectiveness under real-world conditions.
DOI: 10.25984/2222586
title: Airfoil Computational Fluid Dynamics - 2k shapes, 25 AoA's, 3 Re numbers

description: This dataset contains aerodynamic quantities - including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) - for 1,830 airfoil shapes computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes were designed using the separable shape tensor parameterization that encodes two-dimensional shapes as elements of the Grassmann manifold. This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first captures large-scale, linear perturbations, and the second defines small-scale, higher-order perturbations. For this dataset, we used the G2Aero database of over 19,000 airfoil shapes to learn a parameter space that captured a wide array of shape characteristics. We sampled airfoil designs over both parameter spaces to explore the full range of possible shape variations. The aerodynamic quantities for the generated airfoil were obtained using the HAM2D code, which is a finite-volume Reynolds-averaged Navier-Stokes (RANS) flow solver. We employ a fifth-order WENO scheme for spatial reconstruction with Roe's flux difference scheme for inviscid flux and second-order central differencing for viscous flux. A preconditioned GMRES method is applied for implicit integration. The Spalart-Allmaras 1-eq turbulence model is used for the turbulence closure, and the Medida-Baeder 2-eq transition model is applied to account for the effects of laminar turbulent transition. The airfoil grid is generated with a total of 400 points on the airfoil surface, the initial wall-normal spacing of y+ = 1, and an outer boundary located at 300 chord lengths away from the wall. The CFD simulations are performed at a freestream Mach number of 0.1, for or three different Reynolds' numbers (3M, 6M, and 9M), and for 25 angles of attack from -4 deg. to 20 deg. with 1 degree increments. Across all these various parameters, this dataset includes the results from over 250,000 CFD simulations. The simulations were performed using the Bridges-2 system at the Pittsburgh Supercomputing Center in February 2023 as part of the INTEGRATE project funded by the Advanced Research Projects Agency - Energy, in the U.S. Department of Energy. The data was collected, reformatted, and preprocessed for this OEDI submission in July 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub Repository resource under explore_airfoil_2k_data.ipynb.
DOI: 10.25984/2222587
title: Airfoil Computational Fluid Dynamics - 9k shapes, 2 AoA's

description: This dataset contains aerodynamic quantities - including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) - for 8,996 airfoil shapes, computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes were designed using the separable shape tensor parameterization that encodes two-dimensional shapes as elements of the Grassmann manifold. This data-driven approach learns two independent spaces of parameter from a collection of sample airfoils. The first captures large-scale, linear perturbations, and the second defines small-scale, higher-order perturbations. For this data, we used the G2Aero database of over 19,000 airfoil shapes to learn a parameter space that captured a wide array of shape characteristics. We fixed the linear deformations to be the mean over the database and sampled new shapes over a four-dimensional parameter space of higher-order perturbation. This sampling approaches allows for isolated analysis of non-linear airfoil shape deformations while holding other aspects (e.g., airfoil thickness) approximately constant. The aerodynamic quantities for the generated airfoil were obtained using the HAM2D code, which is a finite-volume Reynolds-averaged Navier-Stokes (RANS) flow solver. We employ a fifth-order WENO scheme for spatial reconstruction with Roe's flux difference scheme for inviscid flux and second-order central differencing for viscous flux. A preconditioned GMRES method is applied for implicit integration. The Spalart-Allmaras 1-eq turbulence model is used for the turbulence closure, and the Medida-Baeder 2-eq transition model is applied to account for the effects of laminar turbulent transition. The airfoil grid is generated with a total of 400 points on the airfoil surface, the initial wall-normal spacing of y+ = 1, and an outer boundary located at 300 chord lengths away from the wall. The CFD simulations are performed at a freestream Mach number of 0.1, Reynolds number of 9M, and at two angles of attack, 4 deg. and 12 deg. The simulations were performed using the Bridges-2 system at the Pittsburgh Supercomputing Center in February 2023 as part of the INTEGRATE project funded by the Advanced Research Projects Agency - Energy in the U.S. Department of Energy. The data was collected, reformatted, and preprocessed for this OEDI submission in July 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub Repository resource under explore_airfoil_9k_data.ipynb.
DOI: 10.25984/2222588
title: Flow Redirection and Induction in Steady State (FLORIS) Wind Plant Power Production Data Sets

description: This dataset contains turbine- and plant-level power outputs for 252,500 cases of diverse wind plant layouts operating under a wide range of yawing and atmospheric conditions. The power outputs were computed using the Gaussian wake model in NREL's FLOw Redirection and Induction in Steady State (FLORIS) model, version 2.3.0. The 252,500 cases include 500 unique wind plants generated randomly by a specialized Plant Layout Generator (PLayGen) that samples randomized realizations of wind plant layouts from one of four canonical configurations: (i) cluster, (ii) single string, (iii) multiple string, (iv) parallel string. Other wind plant layout parameters were also randomly sampled, including the number of turbines (25-200) and the mean turbine spacing (3D-10D, where D denotes the turbine rotor diameter). For each layout, 500 different sets of atmospheric conditions were randomly sampled. These include wind speed in 0-25 m/s, wind direction in 0 deg.-360 deg., and turbulence intensity chosen from low (6%), medium (8%), and high (10%). For each atmospheric inflow scenario, the individual turbine yaw angles were randomly sampled from a one-sided truncated Gaussian on the interval 0 deg.-30 deg. oriented relative to wind inflow direction. This random data is supplemented with a collection of yaw-optimized samples where FLORIS was used to determine turbine yaw angles that maximize power production for the entire plant. To generate this data, a subset of cases were selected (50 atmospheric conditions from 50 layouts each for a total of additional 2,500 cases) for which FLORIS was re-run with wake steering control optimization. The IEA onshore reference turbine, which has a 130 m rotor diameter, a 110 m hub height, and a rated power capacity of 3.4 MW was used as the turbine for all simulations. The simulations were performed using NREL's Eagle high performance computing system in February 2021 as part of the Spatial Analysis for Wind Technology Development project funded by the U.S. Department of Energy Wind Energy Technologies Office. The data was collected, reformatted, and preprocessed for this OEDI submission in May 2023 under the Foundational AI for Wind Energy project funded by the U.S. Department of Energy Wind Energy Technologies Office. This dataset is intended to serve as a benchmark against which new artificial intelligence (AI) or machine learning (ML) tools may be tested. Baseline AI/ML methods for analyzing this dataset have been implemented, and a link to their repository containing those models has been provided. The .h5 data file structure can be found in the GitHub repository under explore_wind_plant_data_h5.ipynb.
DOI: 10.25582/data.2022-09.2607052/2222703
title: Data for EMSL Project 60285 from September 2022
DOI: 10.25582/data.2022-09.2598768/2222704
title: Data for EMSL Project 60285 from September 2022
DOI: 10.25582/data.2022-09.2598778/2222705
title: Data for EMSL Project 60285 from September 2022
DOI: 10.25582/data.2022-09.2606944/2222706
title: Data for EMSL Project 60285 from September 2022
DOI: 10.25582/data.2022-09.2606930/2222707
title: Data for EMSL Project 60285 from September 2022
DOI: 10.25582/data.2022-09.2598269/2222708
title: Data for EMSL Project 60285 from September 2022
DOI: 10.25582/data.2022-09.2598260/2222709
title: Data for EMSL Project 60285 from September 2022
DOI: 10.25582/data.2022-11.2650460/2222710
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650502/2222711
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650684/2222712
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650810/2222713
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650745/2222714
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650536/2222715
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650733/2222716
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650503/2222717
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650453/2222718
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650736/2222719
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650767/2222720
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650464/2222721
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650747/2222722
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2651062/2222723
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650785/2222724
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650755/2222725
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650857/2222726
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650693/2222727
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2651065/2222728
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650477/2222729
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650529/2222730
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650688/2222731
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650538/2222732
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650803/2222733
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650787/2222734
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650700/2222735
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650715/2222736
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650673/2222737
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650669/2222738
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650758/2222739
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650454/2222740
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650895/2222741
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650480/2222742
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650484/2222743
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2651009/2222744
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650725/2222745
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650813/2222746
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650525/2222747
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650531/2222748
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650485/2222749
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650542/2222750
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650675/2222751
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650686/2222752
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650880/2222753
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2651087/2222754
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650526/2222755
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650519/2222756
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650868/2222757
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650899/2222758
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650466/2222759
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650689/2222760
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650664/2222761
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650682/2222762
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650860/2222763
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650984/2222764
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650520/2222765
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650687/2222766
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650764/2222767
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650782/2222768
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650948/2222769
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2651015/2222770
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650532/2222771
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650727/2222772
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650540/2222773
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650513/2222774
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650917/2222775
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650718/2222776
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650449/2222777
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650703/2222778
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650523/2222779
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650886/2222780
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2651090/2222781
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650871/2222782
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650955/2222783
title: Data for EMSL Project 51127 from November 2022
DOI: 10.25582/data.2022-11.2650470/2222784
title: Data for EMSL Project 51127 from November 2022