cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7613212
title: Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/buildings in RGB 768x768 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/buildings in RGB 768x768 high-res. images</strong></em> Models have been created using Segmentation Gym* using the following dataset**: https://arxiv.org/abs/1911.09296 These <em><strong>SegFormer </strong></em>model data are based on 1m spatial footprint images and associated labels of buildings. Image size used by model: 768 x 768 x 3 pixels classes:<br> other<br> building File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References<br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston. Creating xbd: A dataset for assessing building damage from satellite imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019. https://arxiv.org/abs/1911.09296
DOI: 10.5281/zenodo.7613211
title: Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/buildings in RGB 768x768 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/buildings in RGB 768x768 high-res. images</strong></em> Models have been created using Segmentation Gym* using the following dataset**: https://arxiv.org/abs/1911.09296 These <em><strong>SegFormer </strong></em>model data are based on 1m spatial footprint images and associated labels of buildings. Image size used by model: 768 x 768 x 3 pixels classes:<br> other<br> building File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References<br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston. Creating xbd: A dataset for assessing building damage from satellite imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019. https://arxiv.org/abs/1911.09296
DOI: 10.6084/m9.figshare.22080860
title: Regional Water-Stress Identification and Crop Yield Estimation in Senegal based on a Novel Water Demand Index

description: Soil moisture, water demand, vegetation indexes and growing degree days values of Senegal between 2015 and 2021.<br> Spatial resolution: district level;<br> Temporal resolution: year; SMI: Soil Moisture Index;<br> WDI: Water Demand Index;<br> VGI: Vegetation Index;<br> GDD: Growing Degree Day; <br> Methodology used available at: http://dx.doi.org/10.1016/j.scitotenv.2022.155893
DOI: 10.6084/m9.figshare.22080860.v2
title: Regional Water-Stress Identification and Crop Yield Estimation in Senegal based on a Novel Water Demand Index

description: Indexes and precipitation values of Senegal between 2015 and 2021. Spatial resolution: district level; Temporal Resolution: Year
DOI: 10.5281/zenodo.7636060
title: 7.8 Earthquake Damage Mapping in Turkey and Syria

description: In the early hours of February 6th, 2023, a devastating earthquake of magnitude 7.8 in Gaziantep and magnitude 7.5 in Ekinözü struck Turkey and Syria, causing widespread loss of life and property. In the aftermath of the quake, more than 36,000 fatalities and numerous injuries were reported in one week. The unprecedented magnitude of the earthquake highlights the delicate nature of human life, yet it also underscores the resilience of the human spirit. To assist in the aftermath of this disaster, the present study aims to assess the damage caused by the earthquake through the analysis of Sentinel-1 satellite data. We adopt a combination of coherence and displacement parameters to create a damage map of the affected areas. First, we calculate the difference in coherence between pre- and post-earthquake images and retain only pixels with a coherence value higher than 0.25 and a negative change in coherence. The resulting change in coherence is expressed as a percentage (diff_coherence_percent) after converting it to a percentage by multiplying it by 100. Next, we enhance positive displacement values and take the absolute value of all displacement values, expressing the magnitude of displacement as a percentage (disp_percent) by dividing it by a threshold of 0.9 m and multiplying it by 100. Finally, we compute the damage percentage as the weighted average of both “diff_coherence_percent” and “disp_percent”, with equal weighting. The results of our study can provide valuable insights into the impact of the earthquake and help guide recovery efforts by providing a map of the damage caused by the quake. Advanced radar technology and satellite imagery highlight the importance of utilizing cutting-edge tools in studying natural disasters and their impacts on communities.
DOI: 10.5281/zenodo.7636059
title: 7.8 Earthquake Damage Mapping in Turkey and Syria

description: In the early hours of February 6th, 2023, a devastating earthquake of magnitude 7.8 in Gaziantep and magnitude 7.5 in Ekinözü struck Turkey and Syria, causing widespread loss of life and property. In the aftermath of the quake, more than 36,000 fatalities and numerous injuries were reported in one week. The unprecedented magnitude of the earthquake highlights the delicate nature of human life, yet it also underscores the resilience of the human spirit. To assist in the aftermath of this disaster, the present study aims to assess the damage caused by the earthquake through the analysis of Sentinel-1 satellite data. We adopt a combination of coherence and displacement parameters to create a damage map of the affected areas. First, we calculate the difference in coherence between pre- and post-earthquake images and retain only pixels with a coherence value higher than 0.25 and a negative change in coherence. The resulting change in coherence is expressed as a percentage (diff_coherence_percent) after converting it to a percentage by multiplying it by 100. Next, we enhance positive displacement values and take the absolute value of all displacement values, expressing the magnitude of displacement as a percentage (disp_percent) by dividing it by a threshold of 0.9 m and multiplying it by 100. Finally, we compute the damage percentage as the weighted average of both “diff_coherence_percent” and “disp_percent”, with equal weighting. The results of our study can provide valuable insights into the impact of the earthquake and help guide recovery efforts by providing a map of the damage caused by the quake. Advanced radar technology and satellite imagery highlight the importance of utilizing cutting-edge tools in studying natural disasters and their impacts on communities.
DOI: 10.5683/sp2/x5z6qc
title: Judging a Book by Its Cover

description: The world can be represented by two layers of information: how it appears on the outside (outward appearance) and what it is on the inside (inner state). To what extent an outward appearance is assumed to reflect the inner state is fundamental to social inference and judgments. Conceptualizing inference in terms of the relationship between the outward appearance and the inner state generates an integrative interpretation for a wide range of phenomena. We showed that Chinese were more likely than Euro-Canadians to make inference of inner state that deviated from outward appearance, whereas Euro-Canadians were more likely than Chinese to infer a convergence between outward appearance and inner state (Studies 1-5). We observed these cross-cultural patterns in various contexts involving people or physical structures. Individual differences in correspondence bias or response bias did not explain these patterns. The lay belief that outward appearance can be misleading mediated the cultural effects (Study 4). To probe the underlying process, two additional experiments showed that highlighting the misleading nature of appearance, but not highlighting the power of the situation, reduced Americans’ beliefs (Study 6) and inference (Study 7) that the outward appearance reflects the inner state. By focusing on the assumed relationship between the outward appearance and inner state, these findings provide a unique angle for understanding cross-cultural phenomena and have practical implications in daily life.
DOI: 10.25403/upresearchdata.21977534
title: South Africa's illegal wildlife trade policies index

description: The dataset lists illegal wildlife trade policies that affect South Africa at international, regional, national and international levels. It also incorporates ten indicators of political will, which assess the motivation to implement these policy provisions. These indicators are: The importance and prominence of decision makers involved in policy discussions Whether a policy enforcement mechanism with incentives and disincentives for implementing the policy was established If human resources were committed to the policy’s implementation Whether suitable financial resources were committed to the policy’s implementation If milestones were integrated into the policy implementation initiative The presence of a monitoring and evaluation mechanism If this monitoring mechanism was suitably funded Whether regular reports were received from the monitoring team If new policy recommendations were debated, and, Whether new policy recommendations were enacted. The presence of these ten indicators, or lack thereof, was then inputted into a series of causal loop diagrams (CLDs) to explore how each factor shaped the degree and direction of political will within the system. <br>
DOI: 10.25403/upresearchdata.21977534.v1
title: South Africa's illegal wildlife trade policies index

description: The dataset lists illegal wildlife trade policies that affect South Africa at international, regional, national and international levels. It also incorporates ten indicators of political will, which assess the motivation to implement these policy provisions. These indicators are: The importance and prominence of decision makers involved in policy discussions Whether a policy enforcement mechanism with incentives and disincentives for implementing the policy was established If human resources were committed to the policy’s implementation Whether suitable financial resources were committed to the policy’s implementation If milestones were integrated into the policy implementation initiative The presence of a monitoring and evaluation mechanism If this monitoring mechanism was suitably funded Whether regular reports were received from the monitoring team If new policy recommendations were debated, and, Whether new policy recommendations were enacted. The presence of these ten indicators, or lack thereof, was then inputted into a series of causal loop diagrams (CLDs) to explore how each factor shaped the degree and direction of political will within the system. <br>
DOI: 10.5283/epub.45631
title: Data archive of "Very weak bonds to artificial atoms formed by quantum corrals"
DOI: 10.17638/datacat.liverpool.ac.uk/1741
title: Control of Ionic Conductivity by Lithium Distribution in Cubic Oxide Argyrodites Li6+xP1–xSixO5Cl
DOI: 10.5281/zenodo.7608822
title: Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2018.9-2018.12)

description: This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W). This is the LST&E dataset in 2018.09-2018.12 <strong>Dataset Characteristics:</strong> Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E) Temporal Coverage: 2018.09-2018.12 Spatial Resolution: 0.02 ° Temporal Resolution: one hour Projection: GCS_WGS_1984 Data Format: NetCDF <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124 Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40 If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).
DOI: 10.5281/zenodo.7608821
title: Himawari-8/AHI hourly clear-sky land surface temperature and emissivity dataset (2018.9-2018.12)

description: This is the clear-sky LST and LSE dataset (0.02°, hourly) derived by the iTES algorithm (Zhou and Cheng, 2020) from the Himawari-8 AHI thermal infrared data. A broadband emissivity (BBE, 8-13.5μm) dataset was also produced using the derived AHI narrowband LSEs (Cheng et al. 2013). The in-situ validation results show that the bias and RMSE of the retrieved AHI LST are 0.19 and 2.93 K in the daytime, and −0.43 and 1.95 K in the nighttime, respectively. The bias and RMSE of the retrieved LSE are less than 0.005 and 0.014, respectively, compared with the latest MYD21 LSE. The time period of this dataset is 2016-2021, covering the AHI 0.02° nominal fixed grid (60°N∼60°S, 80°E∼160°W). This is the LST&E dataset in 2018.09-2018.12 <strong>Dataset Characteristics:</strong> Spatial Coverage: East Asia and Western Pacific regions (60°N∼60°S, 80°E-140°E) Temporal Coverage: 2018.09-2018.12 Spatial Resolution: 0.02 ° Temporal Resolution: one hour Projection: GCS_WGS_1984 Data Format: NetCDF <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Zhou, S., & Cheng, J. (2020). An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager. <em>IEEE Transactions on Geoscience and Remote Sensing, 58</em>, 7105-7124 Cheng, J., et al. (2013). Estimating the Optimal Broadband Emissivity Spectral Range for Calculating Surface Longwave Net Radiation. <em>IEEE Geoscience and Remote Sensing Letters, 10</em>, 401-40 If you have any questions, please contact Prof. Jie Cheng (Jie_Cheng@bnu.edu.cn).
DOI: 10.5281/zenodo.7636080
title: Data for the "Usability Evaluation of a Method to Analyze Data Intensive Domains" paper

description: Data and results for the "Usability Evaluation of a Method to Analyze Data Intensive Domains" paper
DOI: 10.5281/zenodo.7636079
title: Data for the "Usability Evaluation of a Method to Analyze Data Intensive Domains" paper

description: Data and results for the "Usability Evaluation of a Method to Analyze Data Intensive Domains" paper
DOI: 10.57745/bqmiaz
title: Exemple_de_presentation_pour_bilan_saisonnier.pdf

description: :unav
DOI: 10.57745/ultunz
title: Saclay_4_5.tab

description: :unav
DOI: 10.57745/aepe0g
title: Bilan_annuel_saclay.tab

description: :unav
DOI: 10.57745/aikmg8
title: Saclay_indicateurs_sol.tab

description: :unav
DOI: 10.57745/gn05to
title: Script_French_exemple_Saclay.R

description: :unav
DOI: 10.57745/nj7x7h
title: Translation.tab

description: :unav
DOI: 10.57745/oxdase
title: Read-me_ENG.txt

description: :unav
DOI: 10.57745/0hiynd
title: Table_bilan_annuel_trame.tab

description: :unav
DOI: 10.57745/6dcets
title: Lisez-moi_FR.txt

description: :unav
DOI: 10.57745/dg5xcv
title: Saclay_4_5.txt

description: :unav
DOI: 10.57745/nib5ve
title: Table_indicateurs_trame.tab

description: :unav
DOI: 10.57745/thqjdu
title: Bilan_saisonnier_saclay.tab

description: :unav
DOI: 10.57745/84qs0s
title: Script_R_English_example_Saclay.txt

description: :unav
DOI: 10.57745/ljedku
title: Saclay_historique.tab

description: :unav
DOI: 10.57745/mrrtpv
title: Saclay_historique.txt

description: :unav
DOI: 10.57745/u3xldr
title: Script_R_Francais_exemple_Saclay.txt

description: :unav
DOI: 10.25403/upresearchdata.22068350
title: MTT cell viability, apoptosis detection and xCelligence real-time cell analysis

description: This dataset includes MS Excel spreadsheets of cell viability data in HeLa, CaSki and Vero cells after treatments with plant compounds. This was before the final IC<sub>50</sub> values were determined using the GraphPad prism software. Other excel files include quantitaive results from in silico bioactivity and molecular analysis using moleinspiration. PLT files are for RTCA results of CAE21 and EGF25 co-treatments as well as RAR2 IC<sub>50</sub> determination which are viewable in the xCelligence RTCA software. PNG files are images obtained from Kaluza analysis during apoptotis detection in HeLa cells after 24h individual and co-treatments with CAE21 and EGF25 as well as an untreated control.
DOI: 10.5281/zenodo.7636078
title: Supplementary data for the paper "Future of artificial intelligence applications in cancer care: a 2 global cross-sectional survey of researchers"

description: Supplementary data for the paper "Future of artificial intelligence applications in cancer care: a 2 global cross-sectional survey of researchers"
DOI: 10.5281/zenodo.7636077
title: Supplementary data for the paper "Future of artificial intelligence applications in cancer care: a 2 global cross-sectional survey of researchers"

description: Supplementary data for the paper "Future of artificial intelligence applications in cancer care: a 2 global cross-sectional survey of researchers"
DOI: 10.6084/m9.figshare.21802626
title: Chitosan-Coated-Alginate encapsulation of HepG2 cells for enhanced cellular functions

description: Hepatocyte encapsulation was proposed as a promising strategy, combined with the activation of liver regeneration. By preventing acute and chronic liver failure, it bridges the gap between patients on the waiting list and liver transplantation. Alginate is one of the most commonly employed hydrogels for encapsulating hepatocytes. Chelating ions, on the other hand, decrosslink alginate <i>in vivo</i> and ultimately induce cell death. This work aimed to increase the mechanical stability of alginate capsules and the specific cellular functions of hepatocytes <i>in vivo</i> and clinical applications by coating them with a chitosan shell. A one-step preparation process was used to produce chitosan-coated-alginate capsules. The liver-specific functions of encapsulated hepatocytes were evaluated. The results revealed that Alginate-25% chitosan (Chs25)-encapsulated hepatocytes had enhanced liver-specific functions, particularly urea and albumin secretion, compared to the control samples. In addition, reduced secretion of aspartate aminotransferase (AST) and lactate dehydrogenase (LDH) has been observed. A live/dead fluorescence assay was used to determine the viability of encapsulated hepatocytes, and the results showed that chitosan-coated-alginate capsules could provide a suitable environment for hepatocyte transport. Our findings suggest that Chs25 capsules maintained a larger population of functionally active hepatocytes for potential therapeutic applications, particularly hepatocyte transplantation in severe liver failure.