cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25934/00003891.0
title: Available IPD datapackage for study 'A Long Term, Randomised, Double Blind, Placebo-controlled Study to Determine the Effect of Albiglutide, When Added to Standard Blood Glucose Lowering Therapies, on Major Cardiovascular Events in Patients With Type 2 Diabetes Mellitus'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00003891.1
title: IPD datapackage for study 'A Long Term, Randomised, Double Blind, Placebo-controlled Study to Determine the Effect of Albiglutide, When Added to Standard Blood Glucose Lowering Therapies, on Major Cardiovascular Events in Patients With Type 2 Diabetes Mellitus' and research request: 'Dummy request for 5435 - Additional data upload'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00006539
title: Dummy request for 5435 - Additional data upload

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.6084/m9.figshare.12815669
title: Global effects of land-use intensity on local pollinator biodiversity

description: <b>All data required to replicate the main text analyses in the paper "Global effects of land-use intensity on local pollinator biodiversity".</b>
DOI: 10.6084/m9.figshare.12815669.v1
title: Global effects of land-use intensity on local pollinator biodiversity

description: <b>All data required to replicate the analysis in the paper "Global effects of land-use intensity on local pollinator biodiversity".</b>
DOI: 10.5281/zenodo.4425256
title: Sign-specific stimulation "hot" and "cold" spots in Parkinson's disease validated with machine learning

description: <strong>Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation.</strong> <strong>We studied 275 PD patients who underwent STN-DBS between 2003 and 2018. We selected pre-DBS and best available post-DBS scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to DBS. Volumes of tissue activated (VTAs) were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n=51 STN-DBS patients from another institution).</strong> <strong> </strong> <strong>Tremor and rigidity hot spots were largely located outside and dorsolateral to STN whereas hot spots for bradykinesia and axial signs had larger overlap with STN. Using VTA overlap with sign-specific hot and cold spots, support vector machine (SVM) classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering VTA overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using VTA overlap with the sign-specific hot and cold spots.</strong> <br> <strong>In two independent datasets, distinct brain areas could predict sign-specific clinical changes in PD patients with STN-DBS. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement. </strong> <strong>Hot and cold spots for each sign are publicly available as binary labels in NIfTI format. </strong>
DOI: 10.5281/zenodo.4425255
title: Sign-specific stimulation "hot" and "cold" spots in Parkinson's disease validated with machine learning

description: <strong>Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become a standard therapy for Parkinson’s disease (PD). Despite extensive experience, however, the precise target of optimal stimulation and the relationship between site of stimulation and alleviation of individual signs remains unclear. We examined whether machine learning could predict the benefits in specific parkinsonian signs when informed by precise locations of stimulation.</strong> <strong>We studied 275 PD patients who underwent STN-DBS between 2003 and 2018. We selected pre-DBS and best available post-DBS scores from motor items of the Unified Parkinson's Disease Rating Scale (UPDRS-III) to discern sign-specific changes attributable to DBS. Volumes of tissue activated (VTAs) were computed and weighted by i) tremor, ii) rigidity, iii) bradykinesia, and iv) axial signs changes. Then, sign-specific sites of optimal (“hot spots”) and suboptimal efficacy (“cold spots”) were defined. These areas were subsequently validated using machine learning prediction of sign-specific outcomes with in-sample and out-of-sample data (n=51 STN-DBS patients from another institution).</strong> <strong> </strong> <strong>Tremor and rigidity hot spots were largely located outside and dorsolateral to STN whereas hot spots for bradykinesia and axial signs had larger overlap with STN. Using VTA overlap with sign-specific hot and cold spots, support vector machine (SVM) classified patients into quartiles of efficacy with ≥92% accuracy. The accuracy remained high (68-98%) when only considering VTA overlap with hot spots but was markedly lower (41-72%) when only using cold spots. The model also performed poorly (44-48%) when using only stimulation voltage, irrespective of stimulation location. Out-of-sample validation accuracy was ≥96% when using VTA overlap with the sign-specific hot and cold spots.</strong> <br> <strong>In two independent datasets, distinct brain areas could predict sign-specific clinical changes in PD patients with STN-DBS. With future prospective validation, these findings could individualize stimulation delivery to optimize quality of life improvement. </strong> <strong>Hot and cold spots for each sign are publicly available as binary labels in NIfTI format. </strong>
DOI: 10.5281/zenodo.3985949
title: Supporting Data for: Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation Technology

description: This is the full data set of all reviewed research items obtained from Google Scholar, Web of Science and Scopus for the Scoping Literature Review <em>Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation VR technology.</em>
DOI: 10.5281/zenodo.4428553
title: Variable-order fractional master equation and clustering of particles: non-uniform lysosome distribution

description: Raw and tracking data to accompany publication titled: 'Variable-order fractional master equation and clustering of particles: non-uniform lysosome distribution' arXiv preprint available at https://arxiv.org/abs/2101.02698
DOI: 10.5281/zenodo.4428552
title: Variable-order fractional master equation and clustering of particles: non-uniform lysosome distribution

description: Raw and tracking data to accompany publication titled: 'Variable-order fractional master equation and clustering of particles: non-uniform lysosome distribution' arXiv preprint available at https://arxiv.org/abs/2101.02698
DOI: 10.6073/pasta/230a91ea34e766e0a410ad9d98379e4f
title: Sensor and nutrient data associated with the article Harrison et al. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression

description: This document describes a dataset used to produce Random Forests Regression models of stream nitrogen and phosphorus concentrations from high-frequency sensor data, as reported in: Harrison, J.W., Lucius, M.A., Farrell, J.L., Eichler, L.W., and Relyea, R.A. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment: https://doi.org/10.1016/j.scitotenv.2020.143005. The dataset consists of paired values of stream nitrogen and phosphorus concentrations and various high-frequency sensor parameters (water temperature, specific conductance, pH, fluorescent dissolved organic matter, turbidity, hydrostatic pressure, soil moisture) collected during baseflow and storm events from 2018 to 2019 as part of routine monitoring of eleven tributaries of Lake George, New York. This dataset does not include raw data; two levels of processing were performed: (1) erroneous values (extreme or otherwise outlying values with no apparent environmental cause) were removed from the sensor data as part of the routine QA/QC process of the Jefferson Project, and (2) one-hour rolling medians of the raw sensor data were calculated at a 1-minute timestep to maximize pairing of sensor data with nutrient concentrations. The resultant dataset was used to train and test the models presented in Harrison et al. 2020.
DOI: 10.6073/pasta/a72159e80355813581882489396a6b22
title: Sensor data associated with Lucius et al. 2020 – Using machine learning to correct for nonphotochemical quenching in high-frequency in vivo fluorometer data.

description: This document describes a dataset used to produce Using machine learning to correct for nonphotochemical quenching in high-frequency, in vivo fluorometer data, as reported in: Lucius, M.A., Johnston, K.E., Eichler, L.W., Farrell, J.L., Moriarty, V.W. and Relyea, R.A. (2020), Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data. Limnol Oceanogr Methods, 18: 477-494. https://doi.org/10.1002/lom3.10378 The dataset consists of high-frequency water quality and meterological sensor data collected from two autonomous vertical profiling platforms deployed on Lake George, NY during the ice-free months of 2017-2019. Water quality data include depth-referenced measurements of chlorophyll fluorescence, water temperature and dissolved oxygen. Meteorological data include surface-incident total radiation as well as two derived values: solar azimuth and 1-hr rolling average of total radiation. Finally, using interpolated data from regularly collected subsurface profiles of photosynthetically active radiation, estimates of subsurface total radiation were estimated and included in this dataset. This dataset does not include raw data. The data used were subjected to quality control procedures of the Jefferson Project, as well as additional outlier removal measures and the creation of derived data (as previously described and described in detail in Lucius et al. 2020).
DOI: 10.5281/zenodo.4428707
title: The simulation model to the prognosis of material loss in wood processing

description: The study was conducted in a production company operating in the wood processing industry. Geometric characteristics of input material were captured and used to derive statistical distributions, which were then included in the simulation model. The conducted experiments indicated that the quality of the simulation model was significantly affected by the quality and quantity of the sample, on the basis of which the stochastic model is estimated. It was shown that small sample for wood processing data was insufficient to capture process variability. On the other hand, excessive sample size (80 or more observations) for the material with high natural geometric variability, involves taking into account outliers, which may lower the overall prognostic quality of the simulation model. Based on the conducted simulation experiments, the recommended sample size which allows development of a reliable model for estimation of material loss in the analyzed manufacturing process, ranges from 40 to 60 measurements.
DOI: 10.5281/zenodo.4428706
title: The simulation model to the prognosis of material loss in wood processing

description: The study was conducted in a production company operating in the wood processing industry. Geometric characteristics of input material were captured and used to derive statistical distributions, which were then included in the simulation model. The conducted experiments indicated that the quality of the simulation model was significantly affected by the quality and quantity of the sample, on the basis of which the stochastic model is estimated. It was shown that small sample for wood processing data was insufficient to capture process variability. On the other hand, excessive sample size (80 or more observations) for the material with high natural geometric variability, involves taking into account outliers, which may lower the overall prognostic quality of the simulation model. Based on the conducted simulation experiments, the recommended sample size which allows development of a reliable model for estimation of material loss in the analyzed manufacturing process, ranges from 40 to 60 measurements.
DOI: 10.6073/pasta/837e59337bb2f96a30a63efe00894c1c
title: Systems Ecology Lab Bajada Site: Weekly phenology observations of shrub and grass species at a bajada site at Jornada Basin LTER from 2010-2019

description: This dataset contains field-observed plot level phenology data for three perennial shrubs and one perennial grass at a bajada site at the Jornada Basin LTER site from 2010 to present. Shrubs include creosote (Larrea tridentata), honey mesquite (Prosopis glandulosa), and tarbush (Fluorensia). The grass is bush muhly grass (Muhlenbergia porteri). The protocols and phenophase categories were developed by the US National Phenology Network (US-NPN) to detect the different life-cycles of the plant, from leaf development (breaking leaf buds, young unfolded leaves, percentage of leaves in canopy), to flower development (flower buds, open flowers, full flowering), and presence of fruit (ripe fruits, fruits from past growing season). Field data sheets were modified from the US-NPN to record the presence or absence of each phenophase recorded at each observation period for each tagged plant. This is an ongoing dataset.
DOI: 10.5281/zenodo.4091051
title: Syk signaling network comparison reveals the potential molecular determinants of its tumor promoter and suppressor functions

description: Extra data for the comparison of Syk signaling networks including colored KEGG pathways and Cytoscape sessions.
DOI: 10.5281/zenodo.4091050
title: Syk signaling network comparison reveals the potential molecular determinants of its tumor promoter and suppressor functions

description: Extra data for the comparison of Syk signaling networks including colored KEGG pathways and Cytoscape sessions.
DOI: 10.5281/zenodo.4428736
title: Aisel-palletdegr

description: Dataset for palette detection in images. Contains matching .xml and .jpg files including image and annotated bounding box. Palettes are simulated in unity with multiple degrees of degradation.
DOI: 10.5281/zenodo.4428735
title: Aisel-palletdegr

description: Dataset for palette detection in images. Contains matching .xml and .jpg files including image and annotated bounding box. Palettes are simulated in unity with multiple degrees of degradation.
DOI: 10.5255/ukda-sn-854580
title: Digit ratios and masculinity-femininity of women 2020

description: Homosexual women are, on average, more masculine in their appearance and behavior than heterosexual women. We hypothesized that their masculinity was influenced by exposure to elevated levels of prenatal androgen during early development. We recruited 199 women (including 67 homosexual women) and measured their masculinity via self-report and observer ratings. Our measure of prenatal androgen exposure was the ratio of the index to ring finger (2D:4D), which is hypothesized to be lower in women exposed to elevated levels of androgens during prenatal development. The included data covers these 199 women and includes their demographic information (age, race, sexual orientation), their self-reported gender nonconformity for childhood and adulthood as measured on the Childhood Gender Nonconformity Scale and Continuous Gender Identity Scale respectively, their observer-rated masculinity, and measurements of their digit lengths by trainees.
DOI: 10.5281/zenodo.4275329
title: Dataset of the hydrometrics and stable isotopes of water in Schwingbach Environmental Observatory, Germany

description: Dataset includes the following variables for Aug-Dec 2018 and Apr-Oct 2019 : Precipitation Air temperature soil moisture at 5, 30 and 70 cm depths stable isotopes of stream water stable isotopes of precipitation
DOI: 10.5281/zenodo.4275328
title: Dataset of the hydrometrics and stable isotopes of water in Schwingbach Environmental Observatory, Germany

description: Dataset includes the following variables for Aug-Dec 2018 and Apr-Oct 2019 : Precipitation Air temperature soil moisture at 5, 30 and 70 cm depths stable isotopes of stream water stable isotopes of precipitation
DOI: 10.5281/zenodo.4428308
title: REDB-BR: Rainfall Erosivity Database for Brazil

description: This is REDB-BR, the Rainfall Erosivity Database for Brazil from the MSWEP rainfall dataset. It provides the R factor from the Universal Soil Loss Equation (USLE) in a 0.1º resolution grid, developed with 37 years of rainfall data from the MSWEP dataset. The R factor was calculated trough 73 erosivity index regression equations, which mostly uses a relation between monthly precipitation and annual precipitation, the Modified Fournier Index (MFI), and represents a good approximation to locals with no sub-hourly data for long periods. The main product of REDB-BR is the R factor map, available also as a .tif raster. The database also includes the equations shapefile, Thiessen Polygons shapefile and the equations table.
DOI: 10.5281/zenodo.4428307
title: REDB-BR: Rainfall Erosivity Database for Brazil

description: This is REDB-BR, the Rainfall Erosivity Database for Brazil from the MSWEP rainfall dataset. It provides the R factor from the Universal Soil Loss Equation (USLE) in a 0.1º resolution grid, developed with 37 years of rainfall data from the MSWEP dataset. The R factor was calculated trough 73 erosivity index regression equations, which mostly uses a relation between monthly precipitation and annual precipitation, the Modified Fournier Index (MFI), and represents a good approximation to locals with no sub-hourly data for long periods. The main product of REDB-BR is the R factor map, available also as a .tif raster. The database also includes the equations shapefile, Thiessen Polygons shapefile and the equations table.
DOI: 10.6084/m9.figshare.12887969
title: Supplementary Information for 'The main Nile Valley at the end of the Pleistocene (28-15 ka BP)'

description: Supplementary information for:<br>Leplongeon, A. (2021). The main Nile Valley at the end of the Pleistocene (28-15 ka): dispersal corridor or environmental refugium? <i>Frontiers in Earth Science</i> 8. doi:10.3389/feart.2020.607183.<br><br><br>The supplementary information is composed of four files:- SupplInfo1a.pdf – presentation of supplementary information and how to use it, bibliographical references cited in the supplementary information- SupplInfo1b-SITES.csv – raw dataset of all archaeological sites considered in the manuscript- SupplInfo1c-DATES.csv – raw dataset of all dates associated with the archaeological sites considered in the manuscript- SupplInfo1d-DATES.pdf – same as SupplInfo1c-DATES but in pdf to enhance data visualization and make printing possible
DOI: 10.6084/m9.figshare.12887969.v1
title: Supplementary Information for 'The main Nile Valley at the end of the Pleistocene (28-15 ka BP)'

description: Supplementary information for:<br>Leplongeon, A. (2021). The main Nile Valley at the end of the Pleistocene (28-15 ka): dispersal corridor or environmental refugium? <i>Frontiers in Earth Science</i> 8. doi:10.3389/feart.2020.607183.<br><br><br>The supplementary information is composed of four files:- SupplInfo1a.pdf – presentation of supplementary information and how to use it, bibliographical references cited in the supplementary information- SupplInfo1b-SITES.csv – raw dataset of all archaeological sites considered in the manuscript- SupplInfo1c-DATES.csv – raw dataset of all dates associated with the archaeological sites considered in the manuscript- SupplInfo1d-DATES.pdf – same as SupplInfo1c-DATES but in pdf to enhance data visualization and make printing possible
DOI: 10.21429/cjsa-ac35
title: Data and Metadata for 'A regional analysis of long-term gray and harbor seal stranding events' and the seasonal migrants

description: NOAA’s Greater Atlantic Region Marine Mammal Stranding Network Database was examined for its utility as a potential long-term time series for the evaluation of phenological patterns and shifts. Although records from stranding events represent sick or injured animals, these data have been found to be reasonably comparable to survey data and provide useful information on species’ distribution, abundance, and foraging ecology (Maldini et al., 2005; Staudinger et al., 2014; Johnston et al., 2012, 2015). In this study, we focused on stranding data of two marine mammal species, harbor seals (Phoca vitulina) and gray seals (Halichoerus grypus). It was anticipated these data would also be good indicators of the areas and habitats that seal populations use on a seasonal and annual basis in the region. Viable stranding data from Maine to North Carolina included 1,571 gray seals and 4,399 harbor seals from 2001 to 2015. This paper presents a summary of the spatial and temporal patterns of these data, and suggests their suitability as supplemental data to other Gulf of Maine marine species phenological studies, such as the North Atlantic Right Whale Consortium database modelling efforts.
DOI: 10.13020/t6gc-z164
title:  Supplementary Materials for the article: A participatory method for prioritizing invasive species: Ranking threats to Minnesota’s terrestrial ecosystems
DOI: 10.5517/ccdc.csd.cc10ffyt
title: CCDC 936446: Experimental Crystal Structure Determination

description: Related Article: Rajesh Raju, Raghavachary Raghunathan, Natarajan Arumugam, Abdulrahman I. Almansour, Raju Suresh Kumar|2020|Tetrahedron Lett.|61|152661|doi:10.1016/j.tetlet.2020.152661
DOI: 10.5517/ccdc.csd.cc127dqf
title: CCDC 990224: Experimental Crystal Structure Determination

description: Related Article: Li-Mei Jin, Pan Xu, Jingjing Xie, X. Peter Zhang|2020|J.Am.Chem.Soc.|142|20828|doi:10.1021/jacs.0c10415
DOI: 10.5517/ccdc.csd.cc127dsh
title: CCDC 990226: Experimental Crystal Structure Determination

description: Related Article: Li-Mei Jin, Pan Xu, Jingjing Xie, X. Peter Zhang|2020|J.Am.Chem.Soc.|142|20828|doi:10.1021/jacs.0c10415