cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8308312
title: Haiti Landslide Dataset

description: A strong magnitude earthquake (Mw 7.2) struck Haiti on August 14, 2021, triggered massive landslides in the island nation. The epicenter of the earthquake was located at 18.434° N, 73.482° W, at a depth of 10 km in the Nippes region. We mapped all the coseismic landslides triggered during the 2021 event from Landsat 8 OLI, Sentinel-2 and Google Earth archived images. A total of 4178 landslide polygons, equaling 26.84 km<sup>2 </sup>area were delineated.
DOI: 10.18712/nsd-nsd2487-v2
title: Effects of Changes in Leadership and Management Structures in Nordic Higher Education, 2016

description: There is a general assumption amongst policy makers and university managers that reforms in the internal management and leadership structures of universities positively contribute to increasing performance. Yet, few studies have so far shed light on this relationship, despite the continuing external calls for internal reform and "modernization" of Nordic universities. This study (2014-2017) investigated the performance effects - in both teaching and research - of changes in leadership and managerial structures of public universities across four Nordic countries: Norway, Finland, Denmark and Sweden. The study is comparative in nature and focused on three key aspects: (i) drivers, e.g. governmental mandated reforms; (ii) actors, both within universities and at the system level; and (iii) effects, both direct and indirect. The study aimed at providing clarity on the link between internal change and performance within public universities, as well as shed light on the consequences (including unintended ones) associated with the modernization of Nordic higher education. The research problem driving the inquiry was the following: To what extent are changes in leadership and management structures related to shifts in teaching and research performance in public universities across the Nordic countries in the last decade?
DOI: 10.6084/m9.figshare.24072963
title: Cusp Fitting Results of Seeker-Contributor Interactions

description: These two files are cusp fitting results of seeker-contributor interactions in online Q&A communityIn 'cusp fitting result for Zhihu community.xlsx' , we use answer data from Zhihu communityIn 'cusp fitting result for simulation data.xlsx' , we use answer data from our two-layer network multi-agent simulation model
DOI: 10.6084/m9.figshare.24072963.v1
title: Cusp Fitting Results of Seeker-Contributor Interactions

description: These two files are cusp fitting results of seeker-contributor interactions in online Q&A communityIn 'cusp fitting result for Zhihu community.xlsx' , we use answer data from Zhihu communityIn 'cusp fitting result for simulation data.xlsx' , we use answer data from our two-layer network multi-agent simulation model
DOI: 10.5285/03962d9a-c1b1-15fc-e063-6c86abc05b93
title: Colour corrected seafloor BioCam images collected from the Darwin Mounds Marine Protected Area during RRS Discovery cruise DY108/109 (2019).

description: The dataset consists of georeferenced colour corrected images collected from the Darwin Mounds Marine Protected Area (MPA) during RRS Discovery cruise DY108/109 in 2019. Images were acquired using the BioCam seafloor imaging device of the University of Southampton mounted on the Autosub6000 Autonomous Underwater Vehicle (AUV) of the National Oceanography Centre. Two strobes, one mounted at the front and one at the back of the AUV, illuminated the seafloor when the colour camera of BioCam, mounted at the centre of the AUV, acquired those images once every 3 s. Images were stored in raw format along with timestamped navigation data (DVL, USBL, GPS). Data from different navigation sensors were fused with the images in post-processing using an extended Kalman smoother (EKS) implemented in oplab-pipeline (https://doi.org/10.5281/zenodo.6623369). The images were then colour corrected using an algorithm implemented in oplab-pipeline. The imaging survey was undertaken to demonstrate the BioCam technology developed by University of Southampton scientists under the NERC OCEANIDS programme's BioCam project (NE/P020887/1) to generate wide area 3D visual reconstructions of seafloor features.
DOI: 10.34740/kaggle/dsv/6399242
title: SagaGameAnalyst
DOI: 10.5281/zenodo.8308367
title: Profitability of alternative battery operation strategies in pho-tovoltaic self-consumption systems under current regulatory framework and electricity prices in Spain

description: The decreasing costs of solar photovoltaic (PV) technology have led to an exponential growth in the use of PV self-consumption systems. This development has encouraged the consideration of battery energy storage systems (BESS) as a potential means of achieving even more independence from the fluctuating grid electricity prices. As PV technology and energy storage costs continue to decline, both technologies will likely play an increasingly important role in the renewable energy sector. The profitability of batteries in PV self-consumption systems is largely influenced by the price of consumed electricity and the price at which surplus energy is remunerated. However, strategies in PV-BESS self-consumption systems typically do not take electricity prices into consideration as a variable for decision-making. This study simulates and analyzes battery operation strategies that take into account electricity prices. The simulations are performed using real industrial consumption data and real electricity prices and tariffs, they cover the entire lifespan of the batteries, and include aging and degradation due to use and cycling. A techno-economic model is used to evaluate the advantages of incorporating these battery operational strategies into an actual PV-BESS system. The results demonstrate that the proposed strategies enhance the savings that batteries can provide.
DOI: 10.5281/zenodo.8308366
title: Profitability of alternative battery operation strategies in pho-tovoltaic self-consumption systems under current regulatory framework and electricity prices in Spain

description: The decreasing costs of solar photovoltaic (PV) technology have led to an exponential growth in the use of PV self-consumption systems. This development has encouraged the consideration of battery energy storage systems (BESS) as a potential means of achieving even more independence from the fluctuating grid electricity prices. As PV technology and energy storage costs continue to decline, both technologies will likely play an increasingly important role in the renewable energy sector. The profitability of batteries in PV self-consumption systems is largely influenced by the price of consumed electricity and the price at which surplus energy is remunerated. However, strategies in PV-BESS self-consumption systems typically do not take electricity prices into consideration as a variable for decision-making. This study simulates and analyzes battery operation strategies that take into account electricity prices. The simulations are performed using real industrial consumption data and real electricity prices and tariffs, they cover the entire lifespan of the batteries, and include aging and degradation due to use and cycling. A techno-economic model is used to evaluate the advantages of incorporating these battery operational strategies into an actual PV-BESS system. The results demonstrate that the proposed strategies enhance the savings that batteries can provide.
DOI: 10.6084/m9.figshare.24071253.v1
title: China's city level CPU index

description: CPU dataset of 293 cities in China (excluding Hong Kong, Macao, and Taiwan data.2412000-2022), including monthly and annual data (“China's city level CPU index, 2000-2022”).
DOI: 10.5281/zenodo.8222778
title: Datasets and trained diffusion models for "Diffusion Models for Interferometric Satellite Aperture Radar"

description: A set of trained Probabilistic Diffusion Models (PDMs) and corresponding training datasets for the paper "Diffusion Models for Interferometric Satellite Aperture Radar", by Tuel, Kerdreux et al. The code for this paper can be found at this link. <strong>Training datasets</strong> - "InSAR_noise_32x32.zip": a dataset of 32x32 ground deformation scenes obtained from InSAR interferograms over New Mexico with the small baseline subset (SBAS) algorithm. Images were normalised to [0, 1]. - "insar_unwrapped_phase_normalised.zip": a dataset of 128x128 InSAR interferograms obtained from Sentinel-1 acquisitions over Nex Mexico. Images were normalised to [0, 1]. <strong>Trained Models</strong> We provide 6 trained PDMs in separate .zip files. Each .zip file contains the model weights (in *.pt format) and the model metadata file (in *.json format). - "mnist_32_cond_sigma_100.zip": a class-conditional model trained with 100 diffusion time steps on 32x32 MNIST images; - "mnist_32_no_cond_sigma_100.zip": an unconditional model trained with 100 diffusion time steps on 32x32 MNIST images; - "SAR_lowres_128_cond_sigma_2000.zip": a low-resolution (256 to 128) model trained with 2000 diffusion time steps on 128x128 TenGeoP-SARwv images; - "SAR_superres_128_to_256_cond_sigma_2000.zip": a super-resolution (128 to 256) model trained with 2000 diffusion time steps on TenGeoP-SARwv images; - "insar_phase_128_sigma_2000.zip": an unconditional model trained with 2000 diffusion time steps on 128x128 Sentinel-1 InSAR interferograms over New Mexico; - "insar_noise_32_sigma_1000.zip": an unconditional model trained with 1000 diffusion time steps on 32x32 Sentinel-1 InSAR ground deformation scenes over New Mexico.
description: This work was funded by NASA under the NSPIRES grant 80NSSC22K1282.
description: {"references": ["Tuel, Kerdreux et al, Diffusion Models for Interferometric Satellite Aperture Radar, arXiv. doi:10.48550/arXiv.2308.16847"]}
DOI: 10.5281/zenodo.8222779
title: Datasets and trained diffusion models for "Diffusion Models for Interferometric Satellite Aperture Radar"

description: A set of trained Probabilistic Diffusion Models (PDMs) and corresponding training datasets for the paper "Diffusion Models for Interferometric Satellite Aperture Radar", by Tuel, Kerdreux et al. The code for this paper can be found at this link. <strong>Training datasets</strong> - "InSAR_noise_32x32.zip": a dataset of 32x32 ground deformation scenes obtained from InSAR interferograms over New Mexico with the small baseline subset (SBAS) algorithm. Images were normalised to [0, 1]. - "insar_unwrapped_phase_normalised.zip": a dataset of 128x128 InSAR interferograms obtained from Sentinel-1 acquisitions over Nex Mexico. Images were normalised to [0, 1]. <strong>Trained Models</strong> We provide 6 trained PDMs in separate .zip files. Each .zip file contains the model weights (in *.pt format) and the model metadata file (in *.json format). - "mnist_32_cond_sigma_100.zip": a class-conditional model trained with 100 diffusion time steps on 32x32 MNIST images; - "mnist_32_no_cond_sigma_100.zip": an unconditional model trained with 100 diffusion time steps on 32x32 MNIST images; - "SAR_lowres_128_cond_sigma_2000.zip": a low-resolution (256 to 128) model trained with 2000 diffusion time steps on 128x128 TenGeoP-SARwv images; - "SAR_superres_128_to_256_cond_sigma_2000.zip": a super-resolution (128 to 256) model trained with 2000 diffusion time steps on TenGeoP-SARwv images; - "insar_phase_128_sigma_2000.zip": an unconditional model trained with 2000 diffusion time steps on 128x128 Sentinel-1 InSAR interferograms over New Mexico; - "insar_noise_32_sigma_1000.zip": an unconditional model trained with 1000 diffusion time steps on 32x32 Sentinel-1 InSAR ground deformation scenes over New Mexico.
description: This work was funded by NASA under the NSPIRES grant 80NSSC22K1282.
description: {"references": ["Tuel, Kerdreux et al, Diffusion Models for Interferometric Satellite Aperture Radar, arXiv. doi:10.48550/arXiv.2308.16847"]}
DOI: 10.6084/m9.figshare.24073005
title: Illumina data for "Identification of genomic regions associated with color-related traits in durum wheat"

description: These data are the Illumina data for two bi-parental populations.
DOI: 10.6084/m9.figshare.24073005.v1
title: Illumina data for "Identification of genomic regions associated with color-related traits in durum wheat"

description: These data are the Illumina data for two bi-parental populations.
DOI: 10.5281/zenodo.8308346
title: Data supplement to 'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150'

description: This is a data supplement to <strong>'Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150</strong>'. It presents a global-scale Vertical Land Motion (VLM) reconstruction that resolves height changes in the period 1995-2020. It is based on the joint probabilistic analysis of an extensive network of more than 11,000 GNSS stations, tide gauges, and satellite altimetry. The approach used to derive this reconstruction is described in the paper. The dataset variables are explained in the .pdf file.
description: Please acknowledge the sources of the underlying GNSS, tide gauge, and altimetry data (as mentioned in the *netcdf-files).
description: {"references": ["Oelsmann, J., Marcos, M., Passaro, M., S\u00e1nchez, L., Dettmering, D., Dangendorf, S., and Seitz, F. (2023): Vertical land motion reconstruction unveils non-linear effects on relative sea level changes from 1900-2150.", "Oelsmann, J.; Passaro, M.; S\u00e1nchez, L.; Dettmering, D.; Schwatke, C.; Seitz, F.; Bayesian modelling of piecewise trends and discontinuities to improve the estimation of coastal vertical land motion. Submitted to Journal of Geodesy, 2021", "Blewitt G, Kreemer C, Hammond WC, Gazeaux J (2016) Midas robust trend estimator for accurate gps station velocities without step detection. Journal of Geophysical Research: Solid Earth 121(3):2054\u20132068, DOI 10.1002/2015JB01255", "Holgate SJ, Matthews A, Woodworth PL, Rickards LJ, Tamisiea ME, Bradshaw E, Fo-den PR, Gordon KM, Jevrejeva S, Pugh J (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research pp 493\u2013504, DOI 10.2112/JCOASTRES-D-12-00175.1, URLhttps://doi.org/10.2112/JCOASTRES-D-12-00175.1"]}
DOI: 10.5281/zenodo.6474008
title: Data and scripts for collective intelligence research (arXiv:2204.13424)

description: This is the data and scripts for the study <strong>From Prediction Markets to Interpretable Collective Intelligence</strong> by Alexey V. Osipov and Nikolay N. Osipov (arXiv:2204.13424 [cs.GT])
DOI: 10.5281/zenodo.8308376
title: Data and scripts for collective intelligence research (arXiv:2204.13424)

description: This is the data and scripts for the study <strong>From Prediction Markets to Interpretable Collective Intelligence</strong> by Alexey V. Osipov and Nikolay N. Osipov (arXiv:2204.13424 [cs.GT])
DOI: 10.6084/m9.figshare.22337722.v1
title: Data

description: <br> The data folder contains: - raw (clinical_Original.csv) and preprocessed clinical data (clinical_data.csv) - raw transcriptomic data (TCGA-noise-ENSG.csv) - breast Gtext data (Gtex-noiso-ENSG.csv) - fluxomic data generated from genome-scale metabolic model (flux-test.csv)
DOI: 10.5281/zenodo.8308081
title: Labels for land cover classification for Sentinel 2 images in livestock farms

description: Land cover data labeling for 2022 Sentinel 2 images in livestock farms, mostly in different agroclimatic regions of Spain. The data has a resolution of 10m and the label values correspond to: 0: Unproductive. 1: Woodland. 2: Pasture. Dataset produced within the AI4Copernicus European H2020 project.
DOI: 10.5281/zenodo.8308080
title: Labels for land cover classification for Sentinel 2 images in livestock farms

description: Land cover data labeling for 2022 Sentinel 2 images in livestock farms, mostly in different agroclimatic regions of Spain. The data has a resolution of 10m and the label values correspond to: 0: Unproductive. 1: Woodland. 2: Pasture. Dataset produced within the AI4Copernicus European H2020 project.
DOI: 10.48366/r609271
title: A Catalog of DeepMind's Large Language Models including their Seminal Chinchilla Work

description: Coming from researchers at DeepMind, in their 2022 paper "Training Compute-Optimal Large Language Models," often called the "Chinchilla paper," it explored the ideal size and training data volume for language models within a set compute budget. The study suggested that many 100B parameter models, like GPT-3, might be over-parameterized and under-trained. They proposed that smaller models could match the performance of larger ones if trained on more extensive datasets. The Chinchilla paper highlights that the best training dataset size for a model is roughly 20 times its parameter count. For instance, a 70B parameter model needs 1.4 trillion tokens for optimal training. Models like GPT-3, OPT, and BLOOM, with 175B parameters, were trained on suboptimal dataset sizes, potentially under-training them. In comparison, LLaMA 65B was trained near the Chinchilla's recommended size. Notably, the compute-optimal Chinchilla model surpassed models like GPT-3 in various tasks. Given these findings, there's a shift towards developing efficient smaller models, challenging the previous "bigger is always better" trend. This comparison characteristically describes Chinchilla as well as other models from DeepMind on certain salient properties of LLMs.
DOI: 10.7488/ds/7508
title: Multiscale modelling of lubrication flows under rarefied gas conditions

description: A multiscale method is presented for simulating non-equilibrium lubrication flows. The effect of low pressure or tiny lubricating geometries that gives rise to rarefied gas effects, means that standard Navier-Stokes solutions are invalid, while the large lateral size of the systems that need to be investigated is computationally prohibitive for Boltzmann solutions, such as the direct simulation Monte Carlo method (DSMC). The multiscale method we propose is applicable to time-varying, low-speed, rarefied gas flows in quasi-3D geometries that are now becoming important in various applications, such as next-generation microprocessor chip manufacturing, aerospace, sealing technologies and MEMS devices. Our multiscale simulation method provides accurate solutions, with errors of less than 1% compared to the DSMC benchmark results when all modelling conditions are met. It also shows computational gains over DSMC that increase when the lateral size of the systems increases, reaching 2-3 orders of magnitude even for relatively small systems, making it an effective tool for simulation-based design. This dataset contains average pressure results as a function of time for the simulations cases of filling and venting of micro channels we present in the manuscript entitled: "Multiscale modelling of lubrication flows under rarefied gas conditions" by G. Tatsios, L. Gibelli, D. Lockerby and M. Borg.
DOI: 10.5517/ccpbq90
title: CCDC 635912: Experimental Crystal Structure Determination

description: Related Article: H.Kooijman, A.L.Spek, N.Feiken, K.Vrieze|2007|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccs4jkz
title: CCDC 719341: Experimental Crystal Structure Determination

description: Related Article: U.Monkowius, M.Zabel, M.Fleck, H.Yersin|2009|Z.Naturforsch.,B:Chem.Sci.|64|1513|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccsy21t
title: CCDC 742916: Experimental Crystal Structure Determination

description: Related Article: D.Benito-Garagorri, K.Kirchner, K.Mereiter|2009|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1nfm0q
title: CCDC 1532423: Experimental Crystal Structure Determination

description: Related Article: T.D. Roberts, M.A. Halcrow|2017|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27hz7z
title: CCDC 2070931: Experimental Crystal Structure Determination

description: Related Article: Chen-Lin Luo, Chu-Xing Hu, Ping Shang, Guan-Zhao Wen, Jia-Jun Zhu, Ya-Hui Xuan, Bang-Lian Xia, Yu-Chen Liu, Zi-Hao Jiang, Geng Dong, Wei Zhang, Liu-Cheng Gui, Xuan-Feng Jiang|2021|New J.Chem.|45|8910|doi:10.1039/D0NJ06095J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28mndz
title: CCDC 2104261: Experimental Crystal Structure Determination

description: Related Article: Luca Münzfeld, Adrian Hauser, Pauline Hädinger, Florian Weigend, Peter W. Roesky|2021|Angew.Chem.,Int.Ed.|60|24493|doi:10.1002/anie.202111227
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.