cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/v1rjpf/knfgnc
title: blc20150504_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fgt0dg
title: blc20150226_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/5qrtkm
title: blc20150827_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vdeqgm
title: blc20151120_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lphw41
title: blc20150802_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/drzcwk
title: blc20150711_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mjdime
title: blc20150402_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xxu9aj
title: blc20150323_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vsl7jl
title: blc20150306_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zzhiwo
title: blc20150209_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/j7og8f
title: blc20150112_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/upt1qh
title: blc20150427_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ri0rur
title: blc20150530_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qzkz4r
title: blc20151103_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/aemasf
title: blc20150703_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jghjuz
title: blc20150210_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zmtwzi
title: blc20150125_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/vozbkr
title: blc20150403_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/gb0ms7
title: blc20150316_03.txt.gz

description: :unav
DOI: 10.21227/h8t6-2d63
title: NPM Data for IEEE

description: signal data
DOI: 10.7910/dvn/v1rjpf/ve0otu
title: blc20150408_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zpiqf1
title: blc20150627_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/cwgnjs
title: blc20151106_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/om5bdh
title: blc20150114_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/eus9im
title: blc20150928_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lwc39o
title: blc20150913_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/re1aui
title: blc20151113_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/dcagna
title: blc20150309_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/oodxyd
title: blc20151013_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/pmyima
title: blc20150630_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/nedloz
title: blc20150921_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mxxvxi
title: blc20150322_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rkctld
title: blc20150605_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/b6syxb
title: blc20150927_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/6h5irx
title: blc20150804_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/uvyosu
title: blc20150925_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xsxtzv
title: blc20150613_03.txt.gz

description: :unav
DOI: 10.5281/zenodo.3902340
title: Dataset associated with article "Robots mediating interactions between animals for interspecies collective behaviors"

description: This dataset contains results and analysis described in the study "Robots mediating interactions between animals for interspecies collective behaviors", Bonnet, F., Mills, R., Szopek, M., Schönwetter-Fuchs, S., Halloy, J., Bogdan, S., Correia, L., Mondada, F. and Schmickl, T. (2019), <em>Science Robotics</em>, <em>4</em>(28), doi: 10.1126/scirobotics.aau7897 Contents: experimental data (logs from robotic systems, example videos) animal tracking analysis output See the readme and summary files contained within the archives for further details.
description: Data released as open access (see access rights for details). The manuscript is not part of this dataset, available from Science Robotics, or contact the authors.
DOI: 10.5281/zenodo.3902339
title: Dataset associated with article "Robots mediating interactions between animals for interspecies collective behaviors"

description: This dataset contains results and analysis described in the study "Robots mediating interactions between animals for interspecies collective behaviors", Bonnet, F., Mills, R., Szopek, M., Schönwetter-Fuchs, S., Halloy, J., Bogdan, S., Correia, L., Mondada, F. and Schmickl, T. (2019), <em>Science Robotics</em>, <em>4</em>(28), doi: 10.1126/scirobotics.aau7897 Contents: experimental data (logs from robotic systems, example videos) animal tracking analysis output See the readme and summary files contained within the archives for further details.
description: Data released as open access (see access rights for details). The manuscript is not part of this dataset, available from Science Robotics, or contact the authors.
DOI: 10.7910/dvn/v1rjpf/3bbgdf
title: blc20150718_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/20nweh
title: blc20150916_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fttgyp
title: blc20150507_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/pqdxlh
title: blc20150602_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/uc9jfe
title: blc20150215_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fo25sn
title: blc20150412_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/enlzto
title: blc20150227_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rxrpk0
title: blc20150702_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lyh5ah
title: blc20150720_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/il0rm4
title: blc20150524_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/3ebnjr
title: blc20150725_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/v7kytf
title: blc20150930_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tewjhp
title: blc20150712_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/i57zqg
title: blc20151223_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/0gvso2
title: blc20150901_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fhn9no
title: blc20150122_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jxxemb
title: blc20150705_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mxqgno
title: blc20150308_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/p8o1ka
title: blc20150521_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/urjqmt
title: blc20151117_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/n8z9zt
title: blc20151219_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/crtvxi
title: blc20150905_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/c2jhhq
title: blc20150421_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/afmemt
title: blc20151206_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/bqqwyk
title: blc20150120_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ntcfva
title: blc20150621_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/40tum9
title: blc20150401_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lpusvn
title: blc20151027_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/yhvth0
title: blc20150525_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/sbvxak
title: blc20150604_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/cgkhpe
title: blc20150714_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ncivij
title: blc20150823_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/4zq7kh
title: blc20151003_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/iyqvpo
title: blc20150829_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8iz4se
title: blc20150822_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/sefhrp
title: blc20151012_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xhz4le
title: blc20150211_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/9wczbs
title: blc20150707_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zyc9nm
title: blc20150909_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/hnp5am
title: blc20150810_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mptojp
title: blc20150110_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/e6lj52
title: blc20150329_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lhafvk
title: blc20150430_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/icvqsa
title: blc20151205_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qkeoqa
title: blc20150812_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/94guwt
title: blc20151010_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/n4yhrx
title: blc20151019_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qpipam
title: blc20150128_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/snqxdq
title: blc20150727_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ldvfwt
title: blc20151203_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rw8dk1
title: blc20150706_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/unwqmg
title: blc20151130_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xhuwee
title: blc20150626_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/v9b3jp
title: blc20150728_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/cispl5
title: blc20150404_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ocdbi8
title: blc20150622_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/47sz2y
title: blc20150422_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/rzm3ta
title: blc20150724_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/9yl21c
title: blc20151207_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/bokgwu
title: blc20151212_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/iyzdqc
title: blc20150126_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mtwkri
title: blc20150502_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qylkrf
title: blc20151004_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/u1sdpo
title: blc20150615_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/4rslqu
title: blc20150304_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/dvmwas
title: blc20151022_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/n05jiz
title: blc20151125_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/5jdxna
title: blc20150413_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/b5jolq
title: blc20150520_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/daficr
title: blc20150119_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8vsniv
title: blc20150708_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/7acsk6
title: blc20150710_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tvfcvd
title: blc20151029_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/y9lt25
title: blc20150225_03.txt.gz

description: :unav
DOI: 10.18112/openneuro.ds002937.v1.0.0
title: Ascending arousal network connectivity during recovery from traumatic coma
DOI: 10.5281/zenodo.3908221
title: DIY Modifications for Transparent Headphones

description: Accompanying data for AES ebrief "DIY Modifications for Very Open Headphones". HRTF measurement of KEMAR dummy head without a headphone, the AKG K702 and a modified version thereof. Measured in MCC at Aalto Acoustics Lab. High resolution only in the horizontal plane.
DOI: 10.5281/zenodo.3727977
title: DIY Modifications for Transparent Headphones

description: Accompanying data for AES ebrief "DIY Modifications for Very Open Headphones". HRTF measurement of KEMAR dummy head without a headphone, the AKG K702 and a modified version thereof. Measured in MCC at Aalto Acoustics Lab. High resolution only in the horizontal plane.
DOI: 10.21227/4kes-zp40
title: Urban Semantic 3D Dataset

description: This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
DOI: 10.21227/0qya-hw05
title: Urban Semantic 3D Dataset

description: This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
DOI: 10.21227/mske-h677
title: Urban Semantic 3D Dataset

description: This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
DOI: 10.21227/017w-cj51
title: Urban Semantic 3D Dataset

description: This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
DOI: 10.5281/zenodo.3906690
title: Transaction history of the first 10 million blocks of the Ethereum Blockchain

description: This dataset contains the transaction history for the first 10,000,000 blocks of the Ethereum Blockchain. The dataset is structured into 10 consecutive CSV files, which sequentially hold the transactions of 1,000,000 blocks. The information stored for each transaction includes the sending and receiving addresses of the transaction, the transferred value, the corresponding block number, a timestamp, and further information about the target address.
DOI: 10.5281/zenodo.3906689
title: Transaction history of the first 10 million blocks of the Ethereum Blockchain

description: This dataset contains the transaction history for the first 10,000,000 blocks of the Ethereum Blockchain. The dataset is structured into 10 consecutive CSV files, which sequentially hold the transactions of 1,000,000 blocks. The information stored for each transaction includes the sending and receiving addresses of the transaction, the transferred value, the corresponding block number, a timestamp, and further information about the target address.
DOI: 10.5519/qd.54iu69sj
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (51 records)
DOI: 10.5281/zenodo.3824493
title: Data set for "Dynamic perceptual feature selectivity in primary somatosensory cortex upon reversal learning"

description: This repository contains the data used to generate the figures and well as the main codes that were used for analyses.
DOI: 10.5281/zenodo.3824492
title: Data set for "Dynamic perceptual feature selectivity in primary somatosensory cortex upon reversal learning"

description: This repository contains the data used to generate the figures and well as the main codes that were used for analyses.
DOI: 10.25387/g3.12268739.v1
title: Supplemental Material for Gallagher et al., 2020

description: Fig S1, RNA-seq library PCA by timepoint; Fig. S2, REVIGO plots summarizing homoeologous gene coexpression network module GO term enrichment; Fig. S3, patterns of module eigengene expression for each homoeologous gene coexpression network module; Fig. S4, gene membership correspondence between the wild and domesticated gene coexpression networks; Table S1, RNA-seq library read mapping and counts summary; Table S2, GO enrichment results for differentially expressed genes; Table S3, basic statistics for the joint and homoeologous gene coexpression networks; Table S4, GO enrichment results for the homoeologous gene coexpression network modules; Table S5, basic statistics for the wild and domesticated gene coexpression networks; Table S6, GO enrichment results for the differentially coexpressed genes; Table S7, genes showing both differential expression and differential coexpression; Table S8, genes showing both differential expression and differential coexpression related to cell wall biosynthesis and the cytoskeleton.
DOI: 10.17632/fcb47rngbd.2
title: KERATOCONUS WITH PENTACAM INDICES

description: KERATOCONUS WITH PENTACAM INDICES
DOI: 10.17632/yhwhn8zbm5.1
title: Output of the fitting process in C4ISTAR domain

description: This dataset contains the C4ISTAR technologies that have more than 15 years old extracted with text mining techniques. For each technology we attempt to forecast the potential direction using the S-shaped model. The patent information is used for fitting the S-curves (or growth curves), in particular the Pearl (or Logistic) and Gompertz models. For each technology we indicate: - the error metrics of the growth models used to forecast the future trend of the C4ISTAR technology. For each technology is reported the RMSE and MAE for Logistic (Pearl) model and Gompertz in scientific notation. The errors of the models are reported in "Error" sheet. - the estimated parameters for the best models considering the minimum value of RMSE between the Logistic and Gompertz models. For each parameter we show the standard error and the p-value of the parameters calculated in training model process. The parameters of the models are reported in "Parameter" sheet. The model parameters for either Logistic and Gompertz curves are: - d the upper limit (or asymptote) of the cumulative patent numbers; - b the parameter that depend on the growth in the inflection points; - a the year of inflection.
DOI: 10.17632/yhwhn8zbm5.2
title: Output of the fitting process in C4ISTAR domain

description: This dataset contains the C4ISTAR technologies that have more than 15 years old extracted with text mining techniques. For each technology we attempt to forecast the potential direction using the S-shaped model. The patent information is used for fitting the S-curves (or growth curves), in particular the Pearl (or Logistic) and Gompertz models. For each technology we indicate: - the error metrics of the growth models used to forecast the future trend of the C4ISTAR technology. For each technology is reported the RMSE and MAE for Logistic (Pearl) model and Gompertz in scientific notation. The errors of the models are reported in "Error" sheet. - the estimated parameters for the best models considering the minimum value of RMSE between the Logistic and Gompertz models. For each parameter we show the standard error and the p-value of the parameters calculated in training model process. The parameters of the models are reported in "Parameter" sheet. The model parameters for either Logistic and Gompertz curves are: - d the upper limit (or asymptote) of the cumulative patent numbers; - b the parameter that depend on the growth in the inflection points; - a the year of inflection. In the "Parameter" sheet we also indicate for each technology the year of birth, the year of inflection calculated as the parameter a plus the year of birth, the actual value of the overall number of patents at year 2017 and the maximum potential value of the cumulative number of patents, equal to the parameter d rounded.
DOI: 10.17632/sztn2dfkk3.1
title: Drought and hurricane damage detection on four major Caribbean islands

description: These data files link with a recently submitted Remote Sensing of Environment manuscripts. The datasets present filtered, disturbance index data based on the Tasseled Cap indices derived from MODIS BRDF data at 8-day temporal and 500m spatial resolution. The data files are presented as compressed binary files with headers. There is one data file and one binary for each of the four major Caribbean islands.
DOI: 10.17632/8cn4g6ppxx.1
title: DeRango_2020_Galapagos_sea_lion_endocrine_hormones_states

description: Data for focal Galapagos sea lion pups used to understand how state-related traits can impact intraspecific endocrine hormone differentiation and behaviors related to personality.
DOI: 10.17632/ndyd7p6th3.1
title: Species life-history traits and population stability
DOI: 10.17632/n7c8bdw9x4.1
title: External marker concentrations overtime in cattle feces after pulse dosed with ytterbium

description: Two methods for fitting nonlinear compartmental, age-dependent digestion models: A comparison with empirical data. The objective of this research was to compare similar parameters generated by fitting the G2 and G2G1 models to fecal marker excretion datasets using published data from cattle given pulse doses of Yb using either SAS (Version 9.4; SAS Inst., Inc.) and R (R Core Team, 2017) software, and to compare the digesta kinetics estimates calculated with these different sets of parameters. We hypothesize that the model parameters and digesta kinetics estimates will not be different between the 2 software sources. Furthermore, we will characterize the bias and SD of differences between methods to determine whether these methods may be considered interchangeable.