cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7921971
title: Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery

description: <strong>Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery</strong> This model release is part of the Doodleverse: https://github.com/Doodleverse These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using an as-yet unpublished dataset of images and associated label images. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference Classes: {0=other, 1=water} File descriptions There are two models; v7 has been trained from scratch, and v8 has been fine-tuned using hyperparameter adjustment. 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, 1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU<br> 2. sample_images.zip contains a few example input files, for model testing <br> References *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<br>
DOI: 10.5281/zenodo.7921970
title: Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery

description: <strong>Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery</strong> This model release is part of the Doodleverse: https://github.com/Doodleverse These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using an as-yet unpublished dataset of images and associated label images. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference Classes: {0=other, 1=water} File descriptions There are two models; v7 has been trained from scratch, and v8 has been fine-tuned using hyperparameter adjustment. 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, 1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU<br> 2. sample_images.zip contains a few example input files, for model testing <br> References *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<br>
DOI: 10.34740/kaggle/dsv/5658305
title: Supplementary Materials
DOI: 10.25345/c5gx4548d
title: MassIVE MSV000091911 - PANAMA enabled high sensitivity dual nanoflow LC/MS metabolomics and proteomics analysis
title: Mass spectrometry raw data files of nLC-MS platform for dual metabolomic and proteomics
DOI: 10.13012/b2idb-3628170_v1
title: Data for the paper "Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data"

description: This is the data used in the paper "Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data". A preprint may be found at https://doi.org/10.48550/arXiv.2212.11367 Code from the Github repository https://github.com/adtonks/mosquito_GNN can be used with the data here to reproduce the paper's results. v1.0.0 of the code is also archived at https://doi.org/10.5281/zenodo.7897830
DOI: 10.5281/zenodo.4609826
title: LamaH-CE | Large-Sample Data for Hydrology and Environmental Sciences for Central Europe – files

description: <strong>Version Discussion: Please aware that this version of the dataset is NOT the final revised one!</strong> <strong>Note: </strong>It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that <strong>NAs</strong> are indicated by the number<strong> -999</strong> in the <strong>runoff time series</strong>. <strong>Disclaimer:</strong> We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (https://www.gkd.bayern.de/en/rivers/discharge/tables) and Baden-Württemberg (https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer). Runoff data from the Czech Republic may not be used to set up operational warning systems (https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf). <strong>License: </strong> This work is licensed with CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original. <strong>Please aware that this version of the dataset is NOT the final revised one!</strong>
DOI: 10.5281/zenodo.4525245
title: LamaH-CE | Large-Sample Data for Hydrology and Environmental Sciences for Central Europe – files

description: <strong>Version Submission:</strong> <strong>Please aware that this version of the dataset is NOT the final revised one!</strong> <strong>Note: </strong>It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that <strong>NAs</strong> are indicated by the number<strong> -999</strong> in the <strong>runoff time series</strong>. <strong>Disclaimer:</strong> We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (https://www.gkd.bayern.de/en/rivers/discharge/tables) and Baden-Württemberg (https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer). Runoff data from the Czech Republic may not be used to set up operational warning systems (https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf). <strong>License: </strong> This work is licensed with CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original. <strong>Please aware that this version of the dataset is NOT the final revised one!</strong>
DOI: 10.5281/zenodo.7921891
title: Zhang-E3SMv2-MixedPhaseClouds-ARM

description: This contains the data used for the paper: Zhang et al. (2023), Evaluating EAMv2 simulated stratiform mixed-phase clouds at Northern and Southern high latitudes using ARM measurement.
DOI: 10.5517/ccdc.csd.cc2cp4v2
title: CCDC 2195074: Experimental Crystal Structure Determination

description: Related Article: Xiaoqi Tian, Kazutaka Shoyama, Bernhard Mahlmeister, Felix Brust, Matthias Stolte, Frank Würthner|2023|J.Am.Chem.Soc.|145|9886|doi:10.1021/jacs.3c03441
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.cc2cp4w3
title: CCDC 2195075: Experimental Crystal Structure Determination

description: Related Article: Xiaoqi Tian, Kazutaka Shoyama, Bernhard Mahlmeister, Felix Brust, Matthias Stolte, Frank Würthner|2023|J.Am.Chem.Soc.|145|9886|doi:10.1021/jacs.3c03441
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.cc2crk7y
title: CCDC 2197380: Experimental Crystal Structure Determination

description: Related Article: Martin Berger, Dengke Ma, Yann Baumgartner, Thomas Hin-Fung Wong, Paolo Melchiorre|2023|Nat. Catal.|6|332|doi:10.1038/s41929-023-00939-y
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.cc2d8h06
title: CCDC 2212687: Experimental Crystal Structure Determination

description: Related Article: Xiaoqi Tian, Kazutaka Shoyama, Bernhard Mahlmeister, Felix Brust, Matthias Stolte, Frank Würthner|2023|J.Am.Chem.Soc.|145|9886|doi:10.1021/jacs.3c03441
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.cc2d8m5h
title: CCDC 2212816: Experimental Crystal Structure Determination

description: Related Article: Jiao-Jiao Zhang, Ping-Xia Guo, Zhong-Hui Yang, Dan Cai, Wei-Feng Dai, Qing Lu, Yong-Xian Cheng|2023|Fitoterapia|167|105505|doi:10.1016/j.fitote.2023.105505
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.cc2dc2mh
title: CCDC 2215186: Experimental Crystal Structure Determination

description: Related Article: Matteo Balletti, Tommy Wachsmuth, Antonio Di Sabato, Will C. Hartley, Paolo Melchiorre|2023|Chemical Science|14|4923|doi:10.1039/D3SC01347B
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: 10.3886/e191361
title: Code for: On the Distributional Effects of International Tariffs

description: Programs required for replicating analysis, tables, and figures in “On the Distributional Effects of International Tariffs" by Daniel Carroll and Sewon Hur<br>
DOI: 10.3886/e191361v1
title: Code for: On the Distributional Effects of International Tariffs

description: Programs required for replicating analysis, tables, and figures in “On the Distributional Effects of International Tariffs" by Daniel Carroll and Sewon Hur<br>
DOI: 10.5255/ukda-sn-856191
title: Connecting Agropastoral Food Culture Research to Livestock Commercialisation Policy, 2021-2023

description: This research project aimed to define a new research agenda for connecting policy on livestock sector development to research on agropastoralist food cultures. The project brought together a group of relevant stakeholders - policy makers and implementers, researchers, development actors, and local community representatives - from East and West Africa. Researchers performed a rapid literature review on policy and cultural aspects relevant to milk production and consumption in the study locations, policy actors explained their activities and some community members took part in participatory photography. The group reviewed the data in a seminar based on the local ‘Baraza’ deliberation format. The data set comprises: A transcript of an interview with a policy actor from Baringo county Kenya, on the dairy value chain and the meaning of milk, in November 2021. A transcript of an interview with elders of the Arror community on rituals that use milk, in November 2021. Transcripts of a set of interviews with milk value chain actors in Ghana in November 2021. Transcripts of two focus groups held in November and December 2021 where participants from the Arror and Ilchamus ethnic groups who had taken part in a participatory photography process explain their photographs and the issues they illustrated to do with milk and culture in their communities. These transcripts are accompanied by the consent form used in the photography exercise. A report from a baraza meeting held in Baringo county, Kenya, where photographers displayed their photographs and explained them, and used these to open dialogue with policy actors at county level, in April 2022. A literature review on cultures of milk in Northern Ghana, associated with the Fulani ethnic group, and Baringo county, Kenya, associated with the Arror and Ilchamus ethnic groups, and also on policies relevant to the dairy sector, and on the state of milk markets, in Ghana and Kenya. The aim of the data collection was to provide material which provided an entry point to understanding the perspectives of each actor group, that each of the stakeholders could react to in a Baraza in order to co-create meaning about the subject matter. The other aim of the data set is to provide the researchers with material they can use to propose the role of culture in contemporary dairy markets, and policy to do with these, in the study regions.
DOI: 10.5255/ukda-sn-856128
title: Pan-African Network for the Arts in Environmentally Sustainable Development, 2021-2023

description: The project aimed to test a co-creative method whereby policy actors, citizens, artists and researchers co-created artworks about environmental issues. The project aimed to see if such an exercise was possible in multiple contexts, and to explore the effect of working in such an activity on the co-creation of understanding between these groups, for example about their differnet viewpoints on a common issue. The experiment was carried out in country-level workshops in five African countrise. A report was produced on each workshop. Part of the data set consists of original artwork, created by the project partners in the workshops. This includes paintings, photographs, songs, lyrics and videos. Participants in Ghana created one song, with sections in each of 5 languages, about the relationship between livestock herding, peace and environmetal protection. Participants in Kenya worked in small groups to create drawings expressing their views about how milk could be commercialised, and were encouraged by the facilitator to draw containers milk could be sold in. An artist used these drawings to create a painting about the cultural aspects of livestock keeping. Participants in Senegal co-created two paintings, using a collage method, about coastal problems in St. Louis, Senegal. One painting focused on the unequal effects on richer and poorer inhabitants of sea level rise, induced by climate change. One picture focused on the problem of coastal pollution. Participants in Mali created a poem and a painting about water resource depletion. Participants in Mauritania contributed ideas on climate change to a musician who created a song about social cohesion and action on climate change. A music video accompanies the song. Simultaneously, an artist painted a painting about climate change in Mauritania. Part of the data collection includes workshop reports in English and French which show the participants' commentary about the art works they created and describe the process of creating them.
DOI: 10.25380/iastate.19719554
title: Global 1 km resolution daily near-surface air temperature for 2011

description: A gridded 1 km resolution global (50°S ~79°N) daily maximum and minimum near-surface air temperature dataset for 2011 that was generated using a seamless 1 km resolution land surface temperature dataset, a 30-arc second (~1 km) resolution digital elevation model (DEM) data, and air temperature observations at weather stations and a spatially varying coefficient model with sign preservation (SVCM-SP) algorithm. The gridded air temperature dataset is of great use in global studies of urban, climate, and hydrology. Documentation for this dataset can be found with the 2003 data. Data from other years is available at: https://doi.org/10.25380/iastate.c.6005185 <br> Changed log: replaced corrupted file EuropeanAsia_Ta_SVCMsp_2011TMIN_264.tif <br> All 2011 files updated 5/10/23
DOI: 10.25380/iastate.19719554.v4
title: Global 1 km resolution daily near-surface air temperature for 2011

description: A gridded 1 km resolution global (50°S ~79°N) daily maximum and minimum near-surface air temperature dataset for 2011 that was generated using a seamless 1 km resolution land surface temperature dataset, a 30-arc second (~1 km) resolution digital elevation model (DEM) data, and air temperature observations at weather stations and a spatially varying coefficient model with sign preservation (SVCM-SP) algorithm. The gridded air temperature dataset is of great use in global studies of urban, climate, and hydrology. Documentation for this dataset can be found with the 2003 data. Data from other years is available at: https://doi.org/10.25380/iastate.c.6005185 <br> Changed log: replaced corrupted file EuropeanAsia_Ta_SVCMsp_2011TMIN_264.tif <br> All 2011 files updated 5/10/23
DOI: 10.25380/iastate.19719554.v3
title: Global 1 km resolution daily near-surface air temperature for 2011

description: A gridded 1 km resolution global (50°S ~79°N) daily maximum and minimum near-surface air temperature dataset for 2011 that was generated using a seamless 1 km resolution land surface temperature dataset, a 30-arc second (~1 km) resolution digital elevation model (DEM) data, and air temperature observations at weather stations and a spatially varying coefficient model with sign preservation (SVCM-SP) algorithm. The gridded air temperature dataset is of great use in global studies of urban, climate, and hydrology. Documentation for this dataset can be found with the 2003 data. Data from other years is available at: https://doi.org/10.25380/iastate.c.6005185 <br> Changed log: replaced corrupted file EuropeanAsia_Ta_SVCMsp_2011TMIN_264.tif
DOI: 10.6084/m9.figshare.22798487
title: FM01.zip

description: Barcoding datasets for FateMap 01 where melanoma cells were treated with therapy drug 1uM PLX 4032.
DOI: 10.6084/m9.figshare.22798487.v1
title: FM01.zip

description: Barcoding datasets for FateMap 01 where melanoma cells were treated with therapy drug 1uM PLX 4032.
DOI: 10.6084/m9.figshare.22798487.v2
title: FM01.zip

description: Barcoding datasets for FateMap 01 where melanoma cells were treated with therapy drug 1uM PLX 4032.
DOI: 10.5281/zenodo.7901741
title: Why Is Build Outcome Prediction So Easy? Recalibrating to Account for Outcome Tendencies (Replication Package)

description: A replication package for "Why Is Build Outcome Prediction So Easy? Recalibrating to Account for Outcome Tendencies" submitted to ASE 2023
DOI: 10.5281/zenodo.7901740
title: Why Is Build Outcome Prediction So Easy? Recalibrating to Account for Outcome Tendencies (Replication Package)

description: A replication package for "Why Is Build Outcome Prediction So Easy? Recalibrating to Account for Outcome Tendencies" submitted to ASE 2023
DOI: 10.5281/zenodo.7916476
title: Why Is Build Outcome Prediction So Easy? Recalibrating to Account for Outcome Tendencies (Replication Package)

description: A replication package for "Why Is Build Outcome Prediction So Easy? Recalibrating to Account for Outcome Tendencies" submitted to ASE 2023