cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17909/g3nt-a370
title: Data described in Valentino, Brammer, Gould et al. 2023, "An atlas of color-selected quiescent galaxies at z>3 in public JWST fields", The Astrophysical Journal. 

description: JWST and HST images used to create the mosaics in the 11 fields described in Valentino, Brammer, Gould+23 "An atlas of color-selected quiescent galaxies at z>3 in public JWST fields" (The Astrophysical Journal). For further information or details, please contact the corresponding author.
DOI: 10.6073/pasta/ef250f20152f21b01a1eab4f44808e87
title: Sediment organic matter at two stations in West Falmouth Harbor between 2007 and 2016

description: West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000’s. As part of a long-term study into the effects of this nitrogen enrichment, we have measured multiple biogeochemical processes at two locations within the harbor at either end of the eutrophication gradient - one in the well-flushed outer harbor (OH) and one in the inner basin closer to the dominant groundwater N source (Snug Harbor, SH). Samples of the top 4cm of the sediment profile were taken each year and analyzed for organic matter content via loss on ignition in the top 1cm and the sediment from 3-4cm deep. In a subset of years, intermediate depths were quantified, as well as samples below 4cm. Note, calculations for loss on ignition do not account for the weight of salt from porewater dried with the samples. A separate analysis found that salt accounts for between 3 and 19% of the dry weight of the sediment, depending on porosity. A calibration was done with percent carbon in 2009, showing a tight linear correlation between carbon content and organic matter across the entire site (%LOI = 2.71*%C + 0.2471).
DOI: 10.6084/m9.figshare.21785882.v1
title: Supplementary data.xlsx

description: Supplementary data related to the study "A method to analyze the food-water-energy nexus for data-sparse cities: A comparison of green roofs in São José dos Campos, Brazil and Johannesburg, South Africa".
DOI: 10.5519/qd.5cp24gh5
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5391371 records)
DOI: 10.3886/e184605v1
title: Bayesian Structural Equation Modelling for Research in Retailing

description: This paper discusses the Bayesian structural equation modelling (SEM) with the purpose of popularising the Bayesian method among researchers in the retail domain. We attempt to explain every Bayesian concept in a lucid manner. In order to explain, we collected data from 160 respondents and analyzed using AMOS software, which has a graphical user interface and is so easy to use for researchers. We performed the Bayesian confirmatory factor analysis, Bayesian SEM, and Bayesian mediation analysis. The paper discusses the various concepts of Bayesian SEM, such as the MCMC algorithm, posterior predictive probability (PPP), deviance information criterion (DIC), etc. This is the first paper to discuss the Bayesian structural equation modelling in the retailing domain.
DOI: 10.5066/p9q8fe9q
title: O'ahu waterbird movement from 2016 to 2018

description: This data release includes data and metadata containing estimated and predicted locations of radio telemetered Hawaiian waterbirds. Radio telemetry data was collected using an automated radio telemetry network from March 2016 to August 2018 on O'ahu, Hawaii, for three species: ?Alae ?ula (Hawaiian Gallinule; Gallinula galeata sandvicensis), ?Alae ke?oke?o (Hawaiian Coot; Fulica alai), and Ae?o (Hawaiian Stilt; Himantopus mexicanus knudseni). Data were used in the analysis for the manuscript entitled " Hawaiian waterbird movement across a developed landscape."
DOI: 10.5281/zenodo.7612843
title: BLD Leaf Physiology

description: Data sets associated with the publication: Physiological impacts of beech leaf disease across a gradient of symptom severity among understory American beech.
DOI: 10.5281/zenodo.7612842
title: BLD Leaf Physiology

description: Data sets associated with the publication: Physiological impacts of beech leaf disease across a gradient of symptom severity among understory American beech.
DOI: 10.5281/zenodo.7612571
title: Dataset related to article "A cytokine/PTX3 prognostic index as a predictor of mortality in sepsis"

description: This record contains raw data related to article “A cytokine/PTX3 prognostic index as a predictor of mortality in sepsis" Abstract <strong>Background: </strong> Early prognostic stratification of patients with sepsis is a difficult clinical challenge. Aim of this study was to evaluate novel molecules in association with clinical parameters as predictors of 90-days mortality in patients admitted with sepsis at Humanitas Research Hospital. <strong>Methods: </strong> Plasma samples were collected from 178 patients, diagnosed based on Sepsis-3 criteria, at admission to the Emergency Department and after 5 days of hospitalization. Levels of pentraxin 3 (PTX3), soluble IL-1 type 2 receptor (sIL-1R2), and of a panel of pro- and anti-inflammatory cytokines were measured by ELISA. Cox proportional-hazard models were used to evaluate predictors of 90-days mortality. <strong>Results: </strong> Circulating levels of PTX3, sIL-1R2, IL-1β, IL-6, IL-8, IL-10, IL-18, IL-1ra, TNF-α increased significantly in sepsis patients on admission, with the highest levels measured in shock patients, and correlated with SOFA score (PTX3: r=0.44, p&lt;0.0001; sIL-1R2: r=0.35, p&lt;0.0001), as well as with 90-days mortality. After 5 days of hospitalization, PTX3 and cytokines, but not sIL-1R2 levels, decreased significantly, in parallel with a general improvement of clinical parameters. The combination of age, blood urea nitrogen, PTX3, IL-6 and IL-18, defined a prognostic index predicting 90-days mortality in Sepsis-3 patients and showing better apparent discrimination capacity than the SOFA score (AUC=0.863, 95% CI: 0.780-0.945 <em>vs.</em> AUC=0.727, 95% CI: 0.613-0.840; p=0.021 respectively). <strong>Conclusion: </strong> These data suggest that a prognostic index based on selected cytokines, PTX3 and clinical parameters, and hence easily adoptable in clinical practice, performs in predicting 90-days mortality better than SOFA. An independent validation is required.
DOI: 10.5281/zenodo.7612570
title: Dataset related to article "A cytokine/PTX3 prognostic index as a predictor of mortality in sepsis"

description: This record contains raw data related to article “A cytokine/PTX3 prognostic index as a predictor of mortality in sepsis" Abstract <strong>Background: </strong> Early prognostic stratification of patients with sepsis is a difficult clinical challenge. Aim of this study was to evaluate novel molecules in association with clinical parameters as predictors of 90-days mortality in patients admitted with sepsis at Humanitas Research Hospital. <strong>Methods: </strong> Plasma samples were collected from 178 patients, diagnosed based on Sepsis-3 criteria, at admission to the Emergency Department and after 5 days of hospitalization. Levels of pentraxin 3 (PTX3), soluble IL-1 type 2 receptor (sIL-1R2), and of a panel of pro- and anti-inflammatory cytokines were measured by ELISA. Cox proportional-hazard models were used to evaluate predictors of 90-days mortality. <strong>Results: </strong> Circulating levels of PTX3, sIL-1R2, IL-1β, IL-6, IL-8, IL-10, IL-18, IL-1ra, TNF-α increased significantly in sepsis patients on admission, with the highest levels measured in shock patients, and correlated with SOFA score (PTX3: r=0.44, p&lt;0.0001; sIL-1R2: r=0.35, p&lt;0.0001), as well as with 90-days mortality. After 5 days of hospitalization, PTX3 and cytokines, but not sIL-1R2 levels, decreased significantly, in parallel with a general improvement of clinical parameters. The combination of age, blood urea nitrogen, PTX3, IL-6 and IL-18, defined a prognostic index predicting 90-days mortality in Sepsis-3 patients and showing better apparent discrimination capacity than the SOFA score (AUC=0.863, 95% CI: 0.780-0.945 <em>vs.</em> AUC=0.727, 95% CI: 0.613-0.840; p=0.021 respectively). <strong>Conclusion: </strong> These data suggest that a prognostic index based on selected cytokines, PTX3 and clinical parameters, and hence easily adoptable in clinical practice, performs in predicting 90-days mortality better than SOFA. An independent validation is required.
DOI: 10.6073/pasta/759b07d4276f65b39e4bd3adee650b47
title: Precipitation data for C1 chart recorder, 1952 - ongoing.

description: Precipitation data were collected on a daily time-scale from the C1 climate station (3018 m) since 1952. Over time various circumstances have led to days with missing values. Some of these values were estimated from nearby climate stations.
DOI: 10.3886/e184605
title: Bayesian Structural Equation Modelling for Research in Retailing

description: This paper discusses the Bayesian structural equation modelling (SEM) with the purpose of popularising the Bayesian method among researchers in the retail domain. We attempt to explain every Bayesian concept in a lucid manner. In order to explain, we collected data from 160 respondents and analyzed using AMOS software, which has a graphical user interface and is so easy to use for researchers. We performed the Bayesian confirmatory factor analysis, Bayesian SEM, and Bayesian mediation analysis. The paper discusses the various concepts of Bayesian SEM, such as the MCMC algorithm, posterior predictive probability (PPP), deviance information criterion (DIC), etc. This is the first paper to discuss the Bayesian structural equation modelling in the retailing domain.
DOI: 10.3886/e184605v2
title: Bayesian Structural Equation Modelling for Research in Retailing

description: This paper discusses the Bayesian structural equation modelling (SEM) with the purpose of popularising the Bayesian method among researchers in the retail domain. We attempt to explain every Bayesian concept in a lucid manner. In order to explain, we collected data from 160 respondents and analyzed using AMOS software, which has a graphical user interface and is so easy to use for researchers. We performed the Bayesian confirmatory factor analysis, Bayesian SEM, and Bayesian mediation analysis. The paper discusses the various concepts of Bayesian SEM, such as the MCMC algorithm, posterior predictive probability (PPP), deviance information criterion (DIC), etc. This is the first paper to discuss the Bayesian structural equation modelling in the retailing domain.
DOI: 10.25573/serc.20054420.v7
title: 2022 Water Quality Data, Upper Chesapeake Bay, MarineGEO, Smithsonian Environmental Research Center

description: 2022 water quality data collected on a YSI EXO2 multiparameter sonde at the MarineGEO Upper Chesapeake Bay observatory (Smithsonian Environmental Research Center, Edgewater, MD, USA). The figshare resource also contains quality control metadata to assist interpretation and analysis. Review the "readme.txt" file for more information. Location: 38.886928, -76.540172
DOI: 10.6084/m9.figshare.22022549
title: Genetic Substrates of Brain Vulnerability and Resilience in APOE2 Mice Transitioning from Midlife to Old Age

description: Normalized RNA-Seq counts and metadata accompanying "Genetic Substrates of Brain Vulnerability and Resilience in APOE2 Mice Transitioning from Midlife to Old Age", by Ali Mahzarnia, Hae Sol Moon, Jacques Stout, Robert J Anderson, Madison Strain, Jessica T. Tremblay, Zay Yar Han, Andrei Niculescu, Anna Mac Farlane, Jasmine King, Alisson Ashley-Koch, Darin Clark, Michael Lutz, Alexandra Badea.
DOI: 10.6084/m9.figshare.22022549.v1
title: Genetic Substrates of Brain Vulnerability and Resilience in APOE2 Mice Transitioning from Midlife to Old Age

description: Normalized RNA-Seq counts and metadata accompanying "Genetic Substrates of Brain Vulnerability and Resilience in APOE2 Mice Transitioning from Midlife to Old Age", by Ali Mahzarnia, Hae Sol Moon, Jacques Stout, Robert J Anderson, Madison Strain, Jessica T. Tremblay, Zay Yar Han, Andrei Niculescu, Anna Mac Farlane, Jasmine King, Alisson Ashley-Koch, Darin Clark, Michael Lutz, Alexandra Badea.
DOI: 10.34740/kaggle/ds/2874109
title: Global Real GDP from 1960 to 2020
DOI: 10.34740/kaggle/dsv/4954386
title: Cricket (Semantic Segmentation)
DOI: 10.6084/m9.figshare.22022552
title: Raw_and_DLL

description: .Raw File and .dll
DOI: 10.6084/m9.figshare.22022552.v1
title: Raw_and_DLL

description: .Raw File and .dll
DOI: 10.25921/gxv1-ex22
title: EK60-ek5 Water Column Sonar Data Collected During DY1906
DOI: 10.6084/m9.figshare.21997712
title: Dataset for Influence of environmental factors on seasonal variation in plankton dynamics in nearshore waters of Eastern Guangdong, China

description: Established the impact of various environmental parameters on the richness and distribution of phytoplankton and zooplankton in Eastern Guangdong, China.
DOI: 10.6084/m9.figshare.21997712.v3
title: Dataset for Influence of environmental factors on seasonal variation in plankton dynamics in nearshore waters of Eastern Guangdong, China

description: Established the impact of various environmental parameters on the richness and distribution of phytoplankton and zooplankton in Eastern Guangdong, China.
DOI: 10.6084/m9.figshare.21997712.v2
title: Dataset for Influence of environmental factors on seasonal variation in plankton dynamics in nearshore waters of Eastern Guangdong, China

description: Established the impact of various environmental parameters on the richness and distribution of phytoplankton and zooplankton in Eastern Guangdong, China.
DOI: 10.5281/zenodo.7607895
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of buildings of RGB 1024x1024 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of buildings of RGB 1024x1024 high-res. images</strong></em> Models have been created using Segmentation Gym* using the following dataset**: https://github.com/FrontierDevelopmentLab/multi3net These Residual-UNet model data are based on 1m spatial footprint images and associated labels of buildings in Houston. Imagery made available through DigitalGlobe*** Image size used by model: 1024 x 1024 x 3 pixels classes:<br> other<br> building <br> 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 **Rudner, T. G. J.; Rußwurm, M.; Fil, J.; Pelich, R.; Bischke, B.; Kopačková, V.; Biliński, P. Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. In AAAI 2019. https://arxiv.org/pdf/1812.01756.pdf ***DigitalGlobe. 2018. DigitalGlobe Open Data Program. https://www.digitalglobe.com/opendata. Online; accessed 2018-09-01.
DOI: 10.5281/zenodo.7607894
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of buildings of RGB 1024x1024 high-res. images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of buildings of RGB 1024x1024 high-res. images</strong></em> Models have been created using Segmentation Gym* using the following dataset**: https://github.com/FrontierDevelopmentLab/multi3net These Residual-UNet model data are based on 1m spatial footprint images and associated labels of buildings in Houston. Imagery made available through DigitalGlobe*** Image size used by model: 1024 x 1024 x 3 pixels classes:<br> other<br> building <br> 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 **Rudner, T. G. J.; Rußwurm, M.; Fil, J.; Pelich, R.; Bischke, B.; Kopačková, V.; Biliński, P. Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. In AAAI 2019. https://arxiv.org/pdf/1812.01756.pdf ***DigitalGlobe. 2018. DigitalGlobe Open Data Program. https://www.digitalglobe.com/opendata. Online; accessed 2018-09-01.
DOI: 10.21223/bklgmg/s2xj56
title: 03 Pupa.txt

description: :unav
DOI: 10.21223/bklgmg/xxkrmz
title: 06 Oviposition.txt

description: :unav
DOI: 10.21223/bklgmg/osuauc
title: 01 Egg.txt

description: :unav
DOI: 10.21223/bklgmg/mk2w2v
title: 02 Larva.txt

description: :unav
DOI: 10.21223/bklgmg/lxrlsg
title: 07 DataDictionary_Tuta.tab

description: :unav
DOI: 10.21223/bklgmg/pzoeed
title: 04 Female.txt

description: :unav
DOI: 10.21223/bklgmg/twq2w1
title: 05 Male.txt

description: :unav
DOI: 10.26208/a699-3a21
title: Model output for Model Insights into Bed Control on Retreat of Thwaites Glacier, West Antarctica

description: Thwaites Glacier plays an important role in future sea-level rise contribution from the West Antarctic Ice Sheet. Recent observations show that Thwaites Glacier is losing mass, and its grounding zone is retreating. Previous modeling has produced a wide range of results concerning whether, when, and how rapidly further retreat will occur under continued warming. These differences arise at least in part from ill-constrained processes, including friction from the bed, and future atmosphere and ocean forcing affecting ice-shelf and grounding-zone buttressing. Here, we apply the Ice Sheet and Sea-level System Model (ISSM) with a range of specifications of basal sliding behavior in response to varying ocean forcing. We find that basin-wide bed character strongly affects Thwaites Glacier's response to sub-shelf melt by modulating how changes in driving stress are balanced by the bed as the glacier responds to external forcing. Resulting differences in dynamic thinning patterns alter modeled grounding-line retreat across Thwaites’ catchment, affecting both modeled rates and magnitudes of sea-level rise contribution from this critical sector of the ice sheet. Bed character introduces large uncertainties in projections of Thwaites Glacier under equal external forcing, pointing to this as a crucial constraint needed in predictive models of West Antarctica.
DOI: 10.5281/zenodo.7612288
title: mopalia_swanii_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7612287
title: mopalia_swanii_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22023032.v1
title: Tact + Play IF Study

description: Aggregrated data for each participant in seperate Excel sheets.
DOI: 10.6084/m9.figshare.22023032
title: Tact + Play IF Study

description: Aggregrated data for each participant in seperate Excel sheets.
DOI: 10.21223/5vze2h/243ogo
title: DataDictionary_Dolichogeneida.tab

description: :unav
DOI: 10.21223/5vze2h/wok64d
title: 05_Oviposition.txt

description: :unav
DOI: 10.21223/5vze2h/aokzoi
title: 02_Pupa.txt

description: :unav
DOI: 10.21223/5vze2h/yualpo
title: 01_Egg-Larvae.txt

description: :unav
DOI: 10.21223/5vze2h/faql3g
title: 04_Male.txt

description: :unav
DOI: 10.21223/5vze2h/znqqsd
title: 03_Female.txt

description: :unav
DOI: 10.21223/5vze2h
title: Dataset for: Get phenology project in ILCYM 4.0 program (Dolichogenidae Gelechiidivoris)

description: "A study of life tables to determine the effect of temperature on the phenological variables of Dolichogenidae gelechiidivoris, carried out by the Entomology area of the International Potato Center in 2016. 5 constant temperatures were considered to evaluate daily the 5 states of this insect. We evaluated: development time, development rate, mortality, senescence, oviposition time, oviposition rate, and total oviposition.
DOI: 10.34740/kaggle/dsv/4956241
title: NSE-Scripts-Data
DOI: 10.21223/bklgmg
title: Dataset for: Get phenology project in ILCYM 4.0 program (Tuta absoluta)

description: "A study of life tables to determine the effect of temperature on the phenological variables of Tuta absoluta, carried out by the Entomology area of the International Potato Center in 2016. 5 constant temperatures were considered to evaluate daily the 5 states of this insect. We evaluated: development time, development rate, mortality, senescence, oviposition time, oviposition rate, and total oviposition"