cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8206847
title: LPL11 - July 2023

description: Measurements of LPL11 (Frontera Rural Park) in July 2023 El Hierro Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206850
title: LPL12 - July 2023

description: Measurements of LPL12 (Ilhas Desertas) in July 2023 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206849
title: LPL12 - July 2023

description: Measurements of LPL12 (Ilhas Desertas) in July 2023 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206852
title: LPL14 - July 2023

description: Measurements of LPL14 (Ilha das Flores) in July 2023 Flores Light Pollution Laboratorie info
DOI: 10.5281/zenodo.8206851
title: LPL14 - July 2023

description: Measurements of LPL14 (Ilha das Flores) in July 2023 Flores Light Pollution Laboratorie info
DOI: 10.57760/sciencedb.cryosphere.08193
title: A dataset of snow cover phenology in China based on MODIS during 2000–2020
title: 2000-2020年MODIS中国积雪物候数据集

description: This dataset is based on the daily cloudless products with a snow cover area of 500m in China from 2000 to 2019. The MODIS China Snow Phenology Dataset for the years 2000 to 2020 was prepared, including three catalogs. The naming convention for the catalogs is the China XXXX dataset for the years 2000 to 2020, where XXXX represents the snow phenological parameters: number of snow days, initial day of snow cover, and final day of snow cover; The naming convention for sub files is NIEER_ MODIS_ TTT_ 500m_ YYYY-YYYY.tif, where TTT represents different snow phenological parameters, and YYYY-YYYY represents hydrological year, such as NIEER_ MODIS_ SCD_ 5000m_ 2001-2002. tif, with a spatial resolution of 500m and a temporal resolution of 1 year. This article uses the daily cloudless product with a snow cover area of 500m in China from 2000 to 2019 provided by our center. According to the corresponding definitions of snow cover days (SCD), start of snow cover (SCS), and snow cover days (SCM), the MODIS China Snow Phenology Dataset for the years 2000 to 2020 was prepared. The datasets were divided into three categories according to different parameters and accuracy was verified using ground station data. This dataset aims to provide basic data for in-depth research and accurate analysis of snow cover, animal protection, climate prediction, agricultural water resource utilization, flood, snow disaster warning, and other fields.
description: This dataset is based on the daily cloudless products with a snow cover area of 500m in China from 2000 to 2019. The MODIS China Snow Phenology Dataset for the years 2000 to 2020 was prepared, including three catalogs. The naming convention for the catalogs is the China XXXX dataset for the years 2000 to 2020, where XXXX represents the snow phenological parameters: number of snow days, initial day of snow cover, and final day of snow cover; The naming convention for sub files is NIEER_ MODIS_ TTT_ 500m_ YYYY-YYYY.tif, where TTT represents different snow phenological parameters, and YYYY-YYYY represents hydrological year, such as NIEER_ MODIS_ SCD_ 5000m_ 2001-2002. tif, with a spatial resolution of 500m and a temporal resolution of 1 year. This article uses the daily cloudless product with a snow cover area of 500m in China from 2000 to 2019 provided by our center. According to the corresponding definitions of snow cover days (SCD), start of snow cover (SCS), and snow cover days (SCM), the MODIS China Snow Phenology Dataset for the years 2000 to 2020 was prepared. The datasets were divided into three categories according to different parameters and accuracy was verified using ground station data. This dataset aims to provide basic data for in-depth research and accurate analysis of snow cover, animal protection, climate prediction, agricultural water resource utilization, flood, snow disaster warning, and other fields.
DOI: 10.57760/sciencedb.cryosphere.08144
title: A dataset of snow cover phenology in China based on AVHRR from 1980 to 2020
title: 1980-2020年AVHRR中国积雪物候数据集

description: This dataset is based on the daily cloudless snow cover area product of 5km AVHRR from 1980 to 2020, and a long-term series of snow phenology datasets in China were prepared. The dataset is divided into three directories based on different phenological parameters: number of snow days, initial day of snow cover, and final day of snow cover. Each directory contains 40 sub files, representing the snow phenological parameters for each hydrological year. The directory naming convention is the XXXX dataset of China from 1980 to 2020, where XXXX represents the snow phenological parameters: number of snow days, initial day of snow cover, and final day of snow cover; The naming convention for sub files is NIEER_ AVHRR_ TTT_ 5000m_ YYYY-YYYY.tif, where TTT represents different snow cover phenological parameters (SCD is the number of snow cover days, SCS is the beginning day of snow cover, and SCM is the end day of snow cover), and YYYY-YYYY represents the hydrological year. This dataset can serve for the analysis of spatiotemporal changes in snow cover in China, providing basic data for scientific research, engineering construction, and social services such as climate research, hydrological management, ecological environment, humanities and economics.
description: This dataset is based on the daily cloudless snow cover area product of 5km AVHRR from 1980 to 2020, and a long-term series of snow phenology datasets in China were prepared. The dataset is divided into three directories based on different phenological parameters: number of snow days, initial day of snow cover, and final day of snow cover. Each directory contains 40 sub files, representing the snow phenological parameters for each hydrological year. The directory naming convention is the XXXX dataset of China from 1980 to 2020, where XXXX represents the snow phenological parameters: number of snow days, initial day of snow cover, and final day of snow cover; The naming convention for sub files is NIEER_ AVHRR_ TTT_ 5000m_ YYYY-YYYY.tif, where TTT represents different snow cover phenological parameters (SCD is the number of snow cover days, SCS is the beginning day of snow cover, and SCM is the end day of snow cover), and YYYY-YYYY represents the hydrological year. This dataset can serve for the analysis of spatiotemporal changes in snow cover in China, providing basic data for scientific research, engineering construction, and social services such as climate research, hydrological management, ecological environment, humanities and economics.
DOI: 10.5281/zenodo.3540443
title: 2D STORM dataset U2OS Nup96-SNAP A647 (Au Fid)

description: 2D STORM dataset sample: U2OS Nup96-SNAP cells labelled with A647 imaging: STORM, COT buffer, pxlsize 106 nm drift correction: Au fiducials 100 nm Cells from: Thevathasan, J.V., Kahnwald, M., Cieśliński, K. <em>et al.</em> Nuclear pores as versatile reference standards for quantitative superresolution microscopy. <em>Nat Methods</em> <strong>16, </strong>1045–1053 (2019) doi:10.1038/s41592-019-0574-9
DOI: 10.57760/sciencedb.cryosphere.00005
title: Cataloging data set of high Asian ice lakes
title: 高亚洲冰湖编目数据集

description: This dataset is based on glacier catalogue data and 668 landscape Landsat TM/ETM+/OLI images, combined with ArcGIS and ENVI software, NDWI and Google Earth images, and extracted by manual visual interpretation in a buffer zone 10 km away from the boundary of the glacier Icy lake boundary.In the 1990 and 2018 glacial lake data sets, there are 27,205 and 30,121 glacial lakes in the High Asia region, with an area range of 0.0054-6.46 km2, with a total area of 1806.47±2.11 and 2080.12±2.28 km2, respectively. This glacial lake cataloging data set provides basic data for water resource evaluation in high Asia, evaluation of glacial lake outburst flood, and glacier hydrology research.Data set named High_Asia_glacial_lake_1990.Shp High_Asia_glacial_lake_2018.shp Attribute information: GLAKE_ID: The code of the ice lake GL_Image: Image data source GL_Type: Ice lake type GL_SubR: High Asia Glacial Lake Division GL_Time: Data source acquisition date GL_Elev: average elevation of ice lake (m) GL_Area: ice lake area (m2) GL_Peri: perimeter of ice lake (m) GL_A_Error: The area of the ice lake is wrong (m2) GL_Long: Longitude of the center point of the ice lake (°) GL_Lati: Latitude of the center point of the ice lake (°)
description: This dataset is based on glacier catalogue data and 668 landscape Landsat TM/ETM+/OLI images, combined with ArcGIS and ENVI software, NDWI and Google Earth images, and extracted by manual visual interpretation in a buffer zone 10 km away from the boundary of the glacier Icy lake boundary.In the 1990 and 2018 glacial lake data sets, there are 27,205 and 30,121 glacial lakes in the High Asia region, with an area range of 0.0054-6.46 km2, with a total area of 1806.47±2.11 and 2080.12±2.28 km2, respectively. This glacial lake cataloging data set provides basic data for water resource evaluation in high Asia, evaluation of glacial lake outburst flood, and glacier hydrology research.Data set named High_Asia_glacial_lake_1990.Shp High_Asia_glacial_lake_2018.shp Attribute information: GLAKE_ID: The code of the ice lake GL_Image: Image data source GL_Type: Ice lake type GL_SubR: High Asia Glacial Lake Division GL_Time: Data source acquisition date GL_Elev: average elevation of ice lake (m) GL_Area: ice lake area (m2) GL_Peri: perimeter of ice lake (m) GL_A_Error: The area of the ice lake is wrong (m2) GL_Long: Longitude of the center point of the ice lake (°) GL_Lati: Latitude of the center point of the ice lake (°)
DOI: 10.57760/sciencedb.cryosphere.08146
title: A Dataset of Snow phenology for the next 30 years in Chongli Area of Winter Olympics Division
title: 北京冬奥会崇礼赛区未来三十年积雪物候数据集

description: Snow phenology can reflect the dynamic changes of snow. In this study, snow phenology data such as snow days, first and last days of snow and continuous snow days in Chongli district from 2021 to 2050 are prepared to predict the snow situation in Chongli District in the next 30 years. The overall accuracy is 77% verified by Landsat 8 reference value.This data set can provide the reference of artificial snowfall for the development of Winter Olympic events, and guide the future ski resort operation, snow resource management, disaster monitoring and early warning, ski resort construction and extreme weather response. It is also conducive to further research on the future snow phenology and climate change in Chongli district.
description: Snow phenology can reflect the dynamic changes of snow. In this study, snow phenology data such as snow days, first and last days of snow and continuous snow days in Chongli district from 2021 to 2050 are prepared to predict the snow situation in Chongli District in the next 30 years. The overall accuracy is 77% verified by Landsat 8 reference value.This data set can provide the reference of artificial snowfall for the development of Winter Olympic events, and guide the future ski resort operation, snow resource management, disaster monitoring and early warning, ski resort construction and extreme weather response. It is also conducive to further research on the future snow phenology and climate change in Chongli district.
DOI: 10.57760/sciencedb.cryosphere.08171
title: Special data set of snow disaster in pastoral areas of Qinghai Tibet Plateau
title: 青藏高原牧区雪灾专题数据集

description: This data set uses the daily snow observation data of 109 ground meteorological stations in the Qinghai Tibet Plateau, and according to the classification criteria of snow disasters in the Meteorological Disaster Classification Index (DB63/T372-2018), counts the days of mild, moderate, severe and extra severe snow disasters in the Qinghai Tibet Plateau in the past 20 years. The snow disaster data set formed can provide data support for snow disaster risk assessment, risk early warning and snow disaster risk zoning in the Qinghai Tibet Plateau. The dataset contains four data files, named Mild Snowstorm. shp, Severe Snowstorm. shp, Severe Snowstorm. shp, and Extra Severe Snowstorm. shp. In the file attribute table, FID is the site number, and SUM is the number of days of snow disaster occurrence.
description: This data set uses the daily snow observation data of 109 ground meteorological stations in the Qinghai Tibet Plateau, and according to the classification criteria of snow disasters in the Meteorological Disaster Classification Index (DB63/T372-2018), counts the days of mild, moderate, severe and extra severe snow disasters in the Qinghai Tibet Plateau in the past 20 years. The snow disaster data set formed can provide data support for snow disaster risk assessment, risk early warning and snow disaster risk zoning in the Qinghai Tibet Plateau. The dataset contains four data files, named Mild Snowstorm. shp, Severe Snowstorm. shp, Severe Snowstorm. shp, and Extra Severe Snowstorm. shp. In the file attribute table, FID is the site number, and SUM is the number of days of snow disaster occurrence.
DOI: 10.57760/sciencedb.cryosphere.08186
title: snow runoff coefficient maps of Heihe basin during 2000-2015
title: 2000-2015年黑河流域上游融雪径流系数专题数据

description: This dataset contains daily data of snowmelt runoff depth in the upper reaches of the Heihe River Basin from January 2000 to December 2015, saved in tiff format. Fully open for sharing. The data is calibrated using the distributed cold region hydrological model GBEHM and remote sensing parameter calibration method, utilizing long time series high spatial resolution snow cover remote sensing data to provide the calibration objective function and driving data for the model; Using the snow attribute data obtained from ground surveys, more accurate parameter information was provided for the simulation system, and the annual snow melt runoff coefficient data from 2000 to 2015 was estimated. File name: cs_ Yyyy.tiff, field description: cs refers to the snowmelt runoff coefficient, yyyy is the year, for example, 2010; Mm is the month, for example, March is 03
description: This dataset contains daily data of snowmelt runoff depth in the upper reaches of the Heihe River Basin from January 2000 to December 2015, saved in tiff format. Fully open for sharing. The data is calibrated using the distributed cold region hydrological model GBEHM and remote sensing parameter calibration method, utilizing long time series high spatial resolution snow cover remote sensing data to provide the calibration objective function and driving data for the model; Using the snow attribute data obtained from ground surveys, more accurate parameter information was provided for the simulation system, and the annual snow melt runoff coefficient data from 2000 to 2015 was estimated. File name: cs_ Yyyy.tiff, field description: cs refers to the snowmelt runoff coefficient, yyyy is the year, for example, 2010; Mm is the month, for example, March is 03
DOI: 10.5281/zenodo.29601
title: Gm Eeg Data Zip 1 Of 2

description: This is the EEG dataset part 1 of 2 as well as the trial-by-trial behavioural data for the paper "Reduction of Pavlovian bias in schizophrenia" under submission at PLoS One.

EEG data contains the cleaned, ICA eye-blink corrected data in EEGLAB format. Also included is an ICA decomposition using the AMICA algorithm.

Triggers: Triggers are also included in the behavioural R data file "BehaviouralData&M6Fits.RData" as a data.table (will need the 'data.table' package in R) named "dftr". Model fits are in the R data file, also as a data.table, labelled dfM3.

Stimulus onset codes are: 1 & 101 = Go-to-Avoid; 2 & 202 = Go-to-Win; 3 & 303 = NoGo-to-Avoid; 4 & 104 = NoGo-to-Win. (with 100s as invalid stimuli, or requiring the opposite response based on the contingency)

Feedback onset codes are as follows: 21, 121, 41, 141 = win (thumbs up); 29, 129, 9, 109 = lose (thumbs down); 20, 120, 40, 140, 30, 130, 10, 110 = no change (horizontal thumb).

Response onset codes are: 301, 302, 303, 304 for a response to a 'valid' stimulus (not necessarily correct - refers to contingency); 401, 402, 403, 404 for a response to an invalid stimulus.

Hope you enjoy!
DOI: 10.5281/zenodo.6495402
title: Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces'

description: Data of publication 'Optical spin-state polarization in a binuclear europium complex towards molecule-based coherent light-spin interfaces' by Kuppusamy Senthil Kumar et al. The two versions of Fig. 4d datasets correspond to the preprint version (https://zenodo.org/record/4905692#.Ymj9odpBxaQ) and publication version (https://www.nature.com/articles/s41467-021-22383-x), since a new set of data was taken during the review process.
description: Preprint version of the article. Published version in Nature Communications can be found in full open access in https://www.nature.com/articles/s41467-021-22383-x
DOI: 10.5281/zenodo.45181
title: Open Data Intermediaries In Developing Countries Dataset

description: These three datasets support the findings of the paper "Open Data Intermediaries in Developing Countries" published in the <em>Journal of Community Informatics</em>. The paper explores the concept of open data intermediaries using the theoretical framework of Bourdieu’s social model, particularly his species of capital. Secondary data on intermediaries from Emerging Impacts of Open Data in Developing Countries research was analysed according to a working definition of an open data intermediary presented in this paper, and with a focus on how intermediaries are able to link agents in an open data supply chain, including to grassroots communities. The study found that open data supply chains may comprise multiple intermediaries and that multiple forms of capital may be required to connect the supply and use of open data. The effectiveness of intermediaries can be attributed to their proximity to data suppliers or users, and proximity can be expressed as a function of the type of capital that an intermediary possesses. However, because no single intermediary necessarily has all the capital available to link effectively to all sources of power in a field, multiple intermediaries with complementary configurations of capital are more likely to connect between power nexuses. This study concludes that consideration needs to be given to the presence of multiple intermediaries in an open data ecosystem, each of whom may possess different forms of capital to enable the use of open data.

<strong>Data:</strong>


	Data for 27 Asian cases extracted from the 17 Emerging Impacts of Open Data in Developing Countries case study reports.
	Data for 4 African cases extracted from the 17 Emerging Impacts of Open Data in Developing Countries case study reports.
	Tabulated data of findings for types of capital, organisational type and primary source of revenue for each of the 32 open data intermediaries included in the study.
DOI: 10.5281/zenodo.926082
title: Cooperative Cortical Network For Categorical Processing Of Chinese Lexical Tone

description: <strong>This dataset contains the ECoG, CT and MRI data for the six subjects associated with the manuscript, "Cooperative cortical network for categorical processing of Chinese lexical tone", as well as stimulus sound files used in the study.</strong>

data_MRI.rar  - MRI scan before the ECoG electrode implantation

data_CT.rar  -  CT images with ECoG electrode implanted

data_ECoG.rar - ECoG recording of six patients reported in the manuscript

stimuli_BehaviorContinumm.rar  - Chinese tone continuum stimuli (Token 1-13 as in Fig 1 of the manuscript)

stimuli_ECoG_MMN.rar - Chinese tone stimuli used for oddball paradigm in ECoG experiment (Token 2, 5 and 8, see Fig 1 of the manuscript)

Upon email request (hongbo@tsinghua.edu.cn), the authors may provide analysis code. 

 

<strong>Original manuscript:</strong>

Si, Zhou and Hong. <em>PNAS</em>, 2017

<strong>Cooperative cortical network for categorical processing of Chinese lexical tone</strong>

<strong>Abstract:</strong> In tonal languages such as Chinese, lexical tone with varying pitch contours serves as a key feature to provide contrast in word meaning. Similar to phoneme processing, behavioral studies have suggested that Chinese tone is categorically perceived. However, its underlying neural mechanism remains poorly understood. By conducting cortical surface recordings in surgical patients, we revealed a cooperative cortical network along with its dynamics responsible for this categorical perception. Based on an oddball paradigm, we found amplified neural dissimilarity between cross-category tone pairs, rather than between within-category tone pairs, over cortical sites covering both the ventral and dorsal streams of speech processing. The bilateral superior temporal gyrus (STG) and the middle temporal gyrus (MTG) exhibited increased response latencies and enlarged neural dissimilarity, suggesting a ventral hierarchy that gradually differentiates the acoustic features of lexical tones. In addition, the bilateral motor cortices were also found to be involved in categorical processing, interacting with both the STG and the MTG and exhibiting a response latency in between. Moreover, the motor cortex received enhanced Granger causal influence from the semantic hub, the anterior temporal lobe, in the right hemisphere. These unique data suggest that there exists a distributed cooperative cortical network supporting the categorical processing of lexical tone in tonal language speakers, not only encompassing a bilateral temporal hierarchy that is shared by categorical processing of phonemes but also involving intensive speech-motor interactions over the right hemisphere, which might be the unique machinery responsible for the reliable discrimination of tone identities.
DOI: 10.5281/zenodo.1011458
title: Mt2068 Egl-42(N995)Ii | 2011-08-02T10:39:00+01:00

description: This experiment is part of the <em>C.elegans behavioural database</em>. For more information and the complete collection of experiments visit http://movement.openworm.org



<b>preview link</b> : https://www.youtube.com/watch?v=3BJENbBx-5g
<b>strain</b> : MT2068
<b>timestamp</b> : 2011-08-02T10:39:00+01:00
<b>gene</b> : egl-42
<b>chromosome</b> : II
<b>allele</b> : n995
<b>strain_description</b> : egl-42(n995)II
<b>sex</b> : hermaphrodite
<b>stage</b> : adult
<b>ventral_side</b> : clockwise
<b>media</b> : NGM agar low peptone
<b>arena</b> :   
  <b>style</b> : petri
  <b>size</b> : 35
  <b>orientation</b> : away
  

<b>food</b> : OP50
<b>habituation</b> : 30m wait
<b>who</b> : Laura Grundy
<b>protocol</b> : Method in E. Yemini et al. doi:10.1038/nmeth.2560. Worm transferred to arena 30 minutes before recording starts.
<b>lab</b> :   
  <b>name</b> : William R Schafer
  <b>location</b> : MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 0QH, UK
  

<b>software</b> :   
  <b>name</b> : tierpsy (https://github.com/ver228/tierpsy-tracker)
  <b>version</b> : cbfc23eb4f1ac2f29be75ade7a937eed58a5b219
  <b>featureID</b> : @OMG
  

<b>base_name</b> : AQ2000 on food L_2011_08_02__10_39___3___1
<b>total time (s)</b> : 21591.0
<b>frames per second</b> : 5.00751
<b>video micrometers per pixel</b> : 4.53662
<b>number of segmented skeletons</b> : 107763