cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4156152
title: DAFNE Basemap for the Omo-Turkana Basin case study

description: Spatial dataset derived from many different open data repositories and cropped on the Omo-Turkana Basin boundary, used to create a base-map describing the components of Water-Energy-Food nexus in the case study. <strong>omo-turkana.gpkg</strong>: vector dataset including the following layers, together with the related map style for QGIS Desktop used in the DAFNE Geoportal basemap: <em>basin</em>: Hydrological basin of the Omo-Turkana river (case study boundary) <em>subbasin</em>: Basins of the main tributaries <em>waterbodies</em>: Natural lakes and reservoirs boundaries <em>rivers</em>: River network <em>dams</em>: Existing dams <em>protected_areas</em>: Protected areas <em>aei_pct_cells</em>: Area equipped for irrigation, expressed as percentage of total area. <em>roads</em>: Main roads network <em>cities</em>: Main cities in the riparian countries <em>countries</em>: Administrative borders of riparian countries <em>markers</em>: DAFNE model components location, with existing and planned dams and power plants, irrigation schemes, environmental target areas. <strong>zambezi_raster.zip</strong>: raster dataset including the following layers: <em>srtm_90m: </em>Digital Elevation Model Global Surface Water: <em>change</em>: Occurrence Change Intensity map provides information on where surface water occurrence increased, decreased or remained the same between 1984-1999 and 2000-2015 <em>extent: </em>Maximum Water Extent shows all the locations ever detected as water over period 1984-2015 <em>occurence</em>: Occurrence shows where surface water occurred between 1984 and 2015 and provides information concerning overall water dynamics. <em>recurrence</em>: Recurrence provides information concerning the inter-annual behaviour of water surfaces and captures the frequency with which water returns from year to year. <em>seasonality</em>: Seasonality map provides information concerning the intra-annual behaviour of water surfaces for a single year (2015) and shows permanent and seasonal water and the number of months water was present. <em>transitions</em>: Transitions map provides information on the change in surface water seasonality between the first and last years (between 1984 and 2015) and captures changes between the three classes of not water, seasonal water and permanent water. Original data sources include: AQUASTAT, the FAO global information system on water resources and agricultural water management; Natural Earth, a public domain map dataset available at different scales; Protected Planet, the most up to date and complete source of data on protected areas and other effective area-based conservation measures, maintained by UNEP-WCMC and IUCN; OpenStreetMap, a collaborative project to create a free editable map of the world; NASA's Shuttle Radar Topography Mission (SRTM) Digital Elevation Database; Global Water Surface, a virtual time machine that maps the location and temporal distribution of water surfaces at the global scale.
DOI: 10.5281/zenodo.4156151
title: DAFNE Basemap for the Omo-Turkana Basin case study

description: Spatial dataset derived from many different open data repositories and cropped on the Omo-Turkana Basin boundary, used to create a base-map describing the components of Water-Energy-Food nexus in the case study. <strong>omo-turkana.gpkg</strong>: vector dataset including the following layers, together with the related map style for QGIS Desktop used in the DAFNE Geoportal basemap: <em>basin</em>: Hydrological basin of the Omo-Turkana river (case study boundary) <em>subbasin</em>: Basins of the main tributaries <em>waterbodies</em>: Natural lakes and reservoirs boundaries <em>rivers</em>: River network <em>dams</em>: Existing dams <em>protected_areas</em>: Protected areas <em>aei_pct_cells</em>: Area equipped for irrigation, expressed as percentage of total area. <em>roads</em>: Main roads network <em>cities</em>: Main cities in the riparian countries <em>countries</em>: Administrative borders of riparian countries <em>markers</em>: DAFNE model components location, with existing and planned dams and power plants, irrigation schemes, environmental target areas. <strong>zambezi_raster.zip</strong>: raster dataset including the following layers: <em>srtm_90m: </em>Digital Elevation Model Global Surface Water: <em>change</em>: Occurrence Change Intensity map provides information on where surface water occurrence increased, decreased or remained the same between 1984-1999 and 2000-2015 <em>extent: </em>Maximum Water Extent shows all the locations ever detected as water over period 1984-2015 <em>occurence</em>: Occurrence shows where surface water occurred between 1984 and 2015 and provides information concerning overall water dynamics. <em>recurrence</em>: Recurrence provides information concerning the inter-annual behaviour of water surfaces and captures the frequency with which water returns from year to year. <em>seasonality</em>: Seasonality map provides information concerning the intra-annual behaviour of water surfaces for a single year (2015) and shows permanent and seasonal water and the number of months water was present. <em>transitions</em>: Transitions map provides information on the change in surface water seasonality between the first and last years (between 1984 and 2015) and captures changes between the three classes of not water, seasonal water and permanent water. Original data sources include: AQUASTAT, the FAO global information system on water resources and agricultural water management; Natural Earth, a public domain map dataset available at different scales; Protected Planet, the most up to date and complete source of data on protected areas and other effective area-based conservation measures, maintained by UNEP-WCMC and IUCN; OpenStreetMap, a collaborative project to create a free editable map of the world; NASA's Shuttle Radar Topography Mission (SRTM) Digital Elevation Database; Global Water Surface, a virtual time machine that maps the location and temporal distribution of water surfaces at the global scale.
DOI: 10.5281/zenodo.4030853
title: Dirac cones and chiral selection of elastic waves in a soft strip

description: Data set obtained after Digital image correlation.
DOI: 10.5281/zenodo.4030852
title: Dirac cones and chiral selection of elastic waves in a soft strip

description: Data set obtained after Digital image correlation.
DOI: 10.6084/m9.figshare.13154465
title: Type 1 Diabetes MendelVar example

description: MendelVar annotation results for Onengut-Gumuscu et al. (2015) type 1 diabetes GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5
DOI: 10.6084/m9.figshare.13154465.v2
title: Type 1 Diabetes MendelVar example

description: MendelVar annotation results for Onengut-Gumuscu et al. (2015) type 1 diabetes GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5
DOI: 10.6084/m9.figshare.13154465.v1
title: Type 1 Diabetes MendelVar example

description: MendelVar annotation results for Onengut-Gumuscu et al. (2015) type 1 diabetes GWASDetailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5
DOI: 10.6084/m9.figshare.13160799
title: Dataset Fig 5a,b

description: Underlying Image Data for Fig. 5a, 5b
DOI: 10.5281/zenodo.4247190
title: Dataset from the ISMIR2020 article "Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation"

description: We release the data required to reproduce the cross-lingual music genre translation experiments from the article <strong><em>Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation</em></strong> presented at the ISMIR 2020 conference. More information about this data and how it should be used in the experiments can be found in the GitHub repository deezer/MultilingualMusicGenreEmbedding. Please cite our paper if you use the code or data in your work.
DOI: 10.6084/m9.figshare.13154480
title: Type 2 Diabetes MendelVar example

description: MendelVar annotation results for Xue et al. (2018) type 2 diabetes GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5<br>
DOI: 10.6084/m9.figshare.13154480.v2
title: Type 2 Diabetes MendelVar example

description: MendelVar annotation results for Xue et al. (2018) type 2 diabetes GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5<br>
DOI: 10.21227/yp10-y936
title: Model 1.5

description: Model 1.5 Dropout Additive Regression Trees (DART) + Clustered-meter average monthly ratios Monthly (kWh) consumption clustering: 1. Twelve clustering models (one per month of signup): Spectral Clustering2. Clustering using (daily kWh) consumption extracted features. Cluster count set to 4 (empirically better than the 5 or 6 suggested by the elbow distortion method). Forecasting for a meter: Using the mean monthly consumption + in-cluster month-to-month ratios. Clustering Features (170) from weekend/weekday and full series:- Statistical: median, variance, quantiles, ...- Time-series: autocorrelations, trends, seasonalities, ... Month-to-month ratios:- Robust STL LOESS trend was used to calculate the month-to-month percentage ratios Preprocessing:- Time-series: Converted 0 to Nans and dropped them. Regression Forecasting:- Using DART Gradient Boosted Trees (GBT) on the calculated 2018 month ratios + Nested Cross-validation. Dual Annealing used for hyper-parameter optimization.
DOI: 10.5517/ccdc.csd.cc255d1t
title: CCDC 2001175: Experimental Crystal Structure Determination

description: Related Article: Lidia A. Smyshliaeva, Mikhail V. Varaksin, Ekaterina I. Fomina, Muthipeedika N. Joy, Vasiliy A. Bakulev, Valery N. Charushin, Oleg N. Chupakhin|2020|Organometallics|39|3679|doi:10.1021/acs.organomet.0c00478
DOI: 10.5517/ccdc.csd.cc25r368
title: CCDC 2018199: Experimental Crystal Structure Determination

description: Related Article: Xin Liu  HanLin Zhao  JinKe Jiang  SiYu Ma  Yong Li and YuJie Li|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25rbcn
title: CCDC 2018421: Experimental Crystal Structure Determination

description: Related Article: Harmeet Bhoday, Steven P. Kelley, Rainer Glaser|2020|CSD Communication|||
DOI: 10.5281/zenodo.2542577
title: Expectations affect the perception of material properties

description: Complete stimulus set: Expectations affect the perception of material properties
DOI: 10.5281/zenodo.2542576
title: Expectations affect the perception of material properties

description: Complete stimulus set: Expectations affect the perception of material properties
DOI: 10.21227/cmy5-tg16
title: Dyn mean 10

description: Dyn mean 10
DOI: 10.21227/naha-4k65
title: Dyn median 10

description: Dyn median 10
DOI: 10.5281/zenodo.4247192
title: Original_data_and_Result_data

description: The data set of the article "Parallel extraction of marine target applying OIDA architecture". Including Original data and Result data
DOI: 10.5281/zenodo.4247191
title: Original_data_and_Result_data

description: The data set of the article "Parallel extraction of marine target applying OIDA architecture". Including Original data and Result data
DOI: 10.25345/c5b782
title: MassIVE MSV000086412 - Molecular pathogenesis of rhegmatogenous retinal detachment
title: Here, we performed molecular pathology analysis of the vitreous proteomes collected from 127 patients with RRD using SWATH-mass spectrometry. For comparison, samples of neurodegenerative vitreoretinal interface eyes (MH, Pucker) and proliferative diabetic retinopathy eyes with tractional-retinal detachment (PDR-TRD) were used as a control.
DOI: 10.5281/zenodo.4246393
title: Radio-Frequency Control and Video Signal Recordings of Drones

description: This database contains IQ-baseband recordings of 10 consumer/prosumer drones’ radio-control and video signals in an interleaved (I1 Q1 I2 Q2 I3 Q3 ...) 16-bit signed integer, little-endian, format. The drones were recorded at 2.44 GHz and 5.8 GHz center frequencies (with the exception of Yuneec Typhoon H at 5.7 GHz) if the drone supported both. In the dataset, these center frequencies were denoted with 2G and 5G in the filename, respectively. The sampling frequency was 120 MHz at 2.44 GHz and 200 MHz at 5.8 GHz. The recording length at 2.44 GHz was 120e6 samples, which corresponds to a recording period of 1 s and 100e6 samples at 5.8 GHz, which corresponds to a recording period of 0.5 s. The measurements were conducted in the anechoic chamber of Tampere University, where the drones were on a turntable. The controller was next to the drone, and the measurement antenna was at the opposite side of the chamber. The published data are chosen in a way that a complete hopping sequence of the control signal is captured in each single recording, if possible. There were a few drones, for which two separate recordings were provided; these recordings are not consecutive, but it should be possible to combine them to extract the hopping sequence. Some drones also did not have a discernible pattern, or it was too long for our recording period. The recordings of the Parrot Mambo are separated into two files, since it was not possible to record a clean signal with simultaneous active video and control links. The following drone models are included in the first version of the database: DJI Inspire 2 (2.44 and 5.8 GHz) DJI Matrice 100 (2.44 GHz) DJI Matrice 210 (2.44 and 5.8 GHz) DJI Mavic mini (2.44 GHz) DJI Mavic pro (2.44 GHz) DJI Phantom 4 (2.44 GHz) DJI Phantom 4 Pro Plus (2.44 and 5.8 GHz) Parrot Disco (2.44 GHz) Parrot Mambo (2.44 GHz) Yuneec Typhoon H (2.44 and 5.7 GHz) For further information contact Jaakko Marin.
DOI: 10.21227/bsae-pe03
title: Model 1.5.1

description: Model 1.5.1 Dropout Additive Regression Trees (DART) + Clustered-meter average monthly ratios Monthly (kWh) consumption clustering: 1. Twelve clustering models (one per month of signup): Spectral Clustering2. Clustering using (daily kWh) consumption extracted features. Cluster count set to 4 (empirically better than the 5 or 6 suggested by the elbow distortion method). Forecasting for a meter: Using the mean monthly consumption + in-cluster month-to-month ratios. Clustering Features (170) from weekend/weekday and full series:- Statistical: median, variance, quantiles, ...- Time-series: autocorrelations, trends, seasonalities, ... Month-to-month ratios:- Robust STL LOESS trend was used to calculate the month-to-month percentage ratios. All monthly averages are replaced with the new computed ratios. Preprocessing:- Time-series: Converted 0 to Nans and dropped them. Regression Forecasting:- Using DART Gradient Boosted Trees (GBT) on the calculated 2018 month ratios + Nested Cross-validation. Dual Annealing used for hyper-parameter optimization.
DOI: 10.5523/bris.1v7ah03ajjb6o2w1tu9tvmxxu9
title: Data for Advanced Biosystems 2020

description: This project contains the structured data from the Perriman Group in CMM
DOI: 10.5517/ccdc.csd.cc231hzn
title: CCDC 1937871: Experimental Crystal Structure Determination

description: Related Article: Fureya Elif   Ozturkkan|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc23lxl7
title: CCDC 1954599: Experimental Crystal Structure Determination

description: Related Article: M. Murugavel, B. Adinarayana, Mainak Das, S. Peruncheralathan, Narasinga Rao Palepu, A. Srinivasan|2020|Chem.Commun.|56|12809|doi:10.1039/D0CC05309K
DOI: 10.5517/ccdc.csd.cc23r9y4
title: CCDC 1958826: Experimental Crystal Structure Determination

description: Related Article: Lei Zhang, Yin Wang, Yunhui Yang, Ping Zhang, Congyang Wang|2020|Org.Chem.Front.|7|3234|doi:10.1039/D0QO00953A
DOI: 10.5517/ccdc.csd.cc21stwk
title: CCDC 1900699: Experimental Crystal Structure Determination

description: Related Article: Shi-Hui Zhang, Shu-Yu Zhang, Xian-jun Weng, Bei Liu, Zhen-Qi Huang, Jing Yang, Kefen Yue, Yao-Yu Wang|2020|CSD Communication|||
description: Related Article: Shi-Hui Zhang, Shu-Yu Zhang, Jing-Rong Li, Zhen-Qi Huang, Jing Yang, Ke-Fen Yue and Yao-Yu Wang|2020|Dalton Trans.|49|11201|doi:10.1039/D0DT01996H
DOI: 10.5517/ccdc.csd.cc242nqm
title: CCDC 1968770: Experimental Crystal Structure Determination

description: Related Article: Jun-Ping Pei, Xue-Jiao Lv, Chen-Jun Peng, Yan-Kai Liu|2020|Chem.Commun.|56|12765|doi:10.1039/D0CC05099G
DOI: 10.5517/ccdc.csd.cc24lqp5
title: CCDC 1984207: Experimental Crystal Structure Determination

description: Related Article: Dong Zhang, Shunxi Dong, Qianwen He, Yao Luo, Yun Liu, Xiaohua Liu, Xiaoming Feng|2020|Chem.Commun.|56|12757|doi:10.1039/D0CC05621A
DOI: 10.5517/ccdc.csd.cc24npp6
title: CCDC 1986098: Experimental Crystal Structure Determination

description: Related Article: Sunhee Lee, Jisu Shin, Doo-Hyun Ko, Won-Sik Han|2020|Chem.Commun.|56|12741|doi:10.1039/D0CC04684A
DOI: 10.5517/ccdc.csd.cc24npq7
title: CCDC 1986099: Experimental Crystal Structure Determination

description: Related Article: Sunhee Lee, Jisu Shin, Doo-Hyun Ko, Won-Sik Han|2020|Chem.Commun.|56|12741|doi:10.1039/D0CC04684A
DOI: 10.5517/ccdc.csd.cc25c3pc
title: CCDC 2006682: Experimental Crystal Structure Determination

description: Related Article: Ruan C.B. Ribeiro, Daniel B. de Marins, Iris Di Leo, Luana da Silva Gomes, Matheus G. de Moraes, Bruno L. Abbadi, Anne D. Villela, Wellington F. da Silva, Luiz Cláudio R.P. da Silva, Pablo Machado, Cristiano Valim Bizarro, Luiz Augusto Basso, Marcela Cristina de Moraes, Vitor F. Ferreira, Fernando de C. da Silva, Vanessa Nascimento|2021|Eur.J.Med.Chem.|209|112859|doi:10.1016/j.ejmech.2020.112859
description: Related Article: Ruan C. B. Ribeiro, Daniel B. de Marins, Iris Di Leo, Luana S. Gomes, Bruno L. Abbadi, Anne D. Villela, Wellington F. da Silva, Luiz Cláudio R. P. da Silva, Luiz Augusto Basso, Pablo Machado, Cristiano Valim Bizarro, Marcela Cristina de Moraes, Vitor F. Ferreira, Fernando C. da Silva, Vanessa Nascimento|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25fwvc
title: CCDC 2009353: Experimental Crystal Structure Determination

description: Related Article: Dong Zhang, Shunxi Dong, Qianwen He, Yao Luo, Yun Liu, Xiaohua Liu, Xiaoming Feng|2020|Chem.Commun.|56|12757|doi:10.1039/D0CC05621A
DOI: 10.5519/qd.sjx94gu7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.12379922
title: Habitat associations of bats in an agricultural landscape: linear features versus open habitats.

description: Bat activity data along linear features and in the middle of agricultural fields.
DOI: 10.21227/w3ch-f740
title: Dyn median 30

description: Dyn median 30
DOI: 10.21227/k3xa-x715
title: Dyn median 30 weather

description: Dyn median 30 weather
DOI: 10.6084/m9.figshare.13194128
title: ML tree and gene list-Mcajuputi

description: ML tree of 17 Myrtaceae species and with Punica granatum as the outgroup. List of genes for phylogenetic analysis. Mcajuputi cp.
DOI: 10.6084/m9.figshare.13194128.v1
title: ML tree and gene list-Mcajuputi

description: ML tree of 17 Myrtaceae species and with Punica granatum as the outgroup. List of genes for phylogenetic analysis. Mcajuputi cp.
DOI: 10.6084/m9.figshare.12899375
title: Data set: Graded recruitment of pupil-linked neuromodulation by parametric stimulation of the vagus nerve

description: <b>exploration.tar.gz</b><br>VNS-locked pupil, eyelid and walking data from the "parameter exploration experiment"<br><b>baseline.tar.gz</b><br>VNS-locked pupil and eyelid data from the "baseline pupil dependence experiment"<br><b>imaging.tar.gz</b><br>VNS-locked fluorescence, pupil, eyelid and walking data from the "imaging experiment"<br><b>DLC_version4_resnet_101</b><br>pre-trained deeplabcut network for pupil size extraction from raw videos.
DOI: 10.6084/m9.figshare.12899375.v2
title: Data set: Graded recruitment of pupil-linked neuromodulation by parametric stimulation of the vagus nerve

description: <b>exploration.tar.gz</b><br>VNS-locked pupil, eyelid and walking data from the "parameter exploration experiment"<br><b>baseline.tar.gz</b><br>VNS-locked pupil and eyelid data from the "baseline pupil dependence experiment"<br><b>imaging.tar.gz</b><br>VNS-locked fluorescence, pupil, eyelid and walking data from the "imaging experiment"<br><b>DLC_version4_resnet_101</b><br>pre-trained deeplabcut network for pupil size extraction from raw videos.
DOI: 10.6084/m9.figshare.12699698
title: Data to support Scoping reviews in medical education: A scoping review

description: This data set supports the manuscript Scoping reviews in medical education: A scoping review by Lauren Maggio PhD, Kelsey Larsen PhD, Aliki Thomas PhD, Joseph Costello MSIS, Anthony R Artino PhD. A preprint of this manuscript is available on bioRxiv: https://www.biorxiv.org/content/10.1101/2020.07.23.218743v1<br>If you have any questions, please contact: Lauren.maggio@usuhs.edu<br><br>
DOI: 10.6084/m9.figshare.13194326
title: Table S7 from "A rooted phylogeny resolves early bacterial evolution" (Coleman et al.)

description: Protein family annotations (COG and KO) and root presence posterior probabilities (PPs) for reconstruction of the last bacterial common ancestor (LBCA).
DOI: 10.6084/m9.figshare.13194326.v1
title: Table S7 from "A rooted phylogeny resolves early bacterial evolution" (Coleman et al.)

description: Protein family annotations (COG and KO) and root presence posterior probabilities (PPs) for reconstruction of the last bacterial common ancestor (LBCA).
DOI: 10.5281/zenodo.4247318
title: Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm - deep net Matlab

description: The file is the trained convolutional neural network (CNN) developed in "De Logu, Francesco, et al. "Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm." <em>Frontiers in Oncology</em> 10 (2020)". The CNN is based on a pretrained Inception-ResNet-v2 to automatically recognizes cutaneous melanoma from histopathological digitalized slides. The file is in a Matlab format (.mat).
DOI: 10.5281/zenodo.4247317
title: Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm - deep net Matlab

description: The file is the trained convolutional neural network (CNN) developed in "De Logu, Francesco, et al. "Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm." <em>Frontiers in Oncology</em> 10 (2020)". The CNN is based on a pretrained Inception-ResNet-v2 to automatically recognizes cutaneous melanoma from histopathological digitalized slides. The file is in a Matlab format (.mat).
DOI: 10.21227/rc3f-h006
title: Dyn median 20

description: Dyn median 20
DOI: 10.14284/426
title: SUMARiS survival dataset

description: The SUMARiS survival dataset contains trip (TR), haul (HL) and survival data (ST) of 31 commercial fishing trips for four species of skate.
description: In the North East Atlantic, skates (Rajidae) are primarily caught as a byproduct in otter- and beam-trawl fisheries and also targeted, with lower effort, with gill- and trammel nets. In the context of a potential "high survival" exemption on the European Landing Obligation (LO), discard survival was quantified for four species caught in North Sea and English Channel: thornback (Raja clavata), blonde (Raja brachyura), spotted (Raja montagui) and undulate (Raja undulata) skates. The SUMARiS survival dataset contains all relevant (meta)data to quantify and explore at-vessel and delayed survival for these four species.