cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/871836339f4c7a0b522aa1dd5206eb4e
title: McMurdo Dry Valleys LTER: Daily summaries of seasonal stream gage measurements from Andersen Creek at H1 in Taylor Valley, Antarctica from 1993 to present

description: As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This package contains daily summaries derived from 15-minute measurements of water quality and quantity parameters measured with automatic recording devices on streams in this region. Specifically, this metadata record describes the daily hydrological summaries for theÂ McMurdo Dry Valley's Andersen Creek at H1, located in the Hoare Basin of Taylor Valley. Measurements commenced during the 1993-94 season and are ongoing.
DOI: 10.17632/sf4b6yk4bc.1
title: Data for: La Niña-driven flooding in the Indo-Pacific warm pool during the past millennium

description: Sediment proxies from Lake Lading, East Java, Indonesia are provided on depth and age scales. The proxy data include sand content, magnetic susceptibility, and nitrogen isotopic values.
DOI: 10.6073/pasta/31ec5d3e72c396a78daecd98cc4e5510
title: McMurdo Dry Valleys LTER: Daily summaries of seasonal stream gage measurements from the Onyx River at Lake Vanda in Wright Valley, Antarctica from 1969 to present

description: As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This package contains daily summaries derived from 15-minute measurements of water quality and quantity parameters measured with automatic recording devices on streams in this region. Specifically, this metadata record describes the daily hydrological summaries for theÂ McMurdo Dry Valley's Onyx River at Lake Vanda, located in the Wright Valley. Measurements commenced in 1969 and are ongoing.
DOI: 10.15468/oucfyf
title: Anfang einer Auseinandersetzung der deutschen Arten der Gattung Agrion F. (excerpt)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Hansemann, J. W. A. (1823): Anfang einer Auseinandersetzung der deutschen Arten der Gattung Agrion F. (excerpt). Zoologisches Magazin Wiedemann 2: 148-161, DOI: http://doi.org/10.5281/zenodo.3529462
DOI: 10.25345/c54t1d
title: MassIVE MSV000084538 - ATF4 Promotes Skeletal Muscle Atrophy by Forming a Heterodimer with C/EBPbeta
title: Skeletal muscle atrophy is a highly prevalent and debilitating condition that remains poorly understood at the molecular level. Previous work found that skeletal muscle atrophy involves activating transcription factor 4 (ATF4), a protein in the basic leucine zipper (bZIP) transcription factor family. However, the direct biochemical mechanism by which ATF4 promotes muscle atrophy was unknown. Because bZIP proteins such as ATF4 must dimerize to bind and activate genes, and because ATF4 is unable to form highly stable homodimers, we hypothesized that ATF4 may promote muscle atrophy by heterodimerizing with another bZIP family member. To test this hypothesis, we biochemically isolated skeletal muscle proteins that associate with the dimerization- and DNA-binding domain of ATF4 (the bZIP domain) in mouse skeletal muscle fibers in vivo. Interestingly, we found that ATF4 makes up one half of at least 5 distinct heterodimeric bZIP transcription factors in skeletal muscle fibers. This three-way interaction between ATF4, C/EBPbeta and the ATF4-C/EBP composite site activates the Gadd45a gene, which encodes a known mediator of muscle atrophy (Gadd45a). Together, these results identify a direct biochemical mechanism by which ATF4 induces skeletal muscle atrophy and provide new insight into the way that skeletal muscle atrophy occurs at the molecular level.
DOI: 10.5281/zenodo.3529343
title: Data: Trait-based food web model reveals the underlying mechanisms of biodiversity-ecosystem functioning relationships

description: Data and code related to 'Trait-based food web model reveals the underlying mechanisms of biodiversity-ecosystem functioning relationships' to reproduce figures and analyses.
DOI: 10.5281/zenodo.3529344
title: Data: Trait-based food web model reveals the underlying mechanisms of biodiversity-ecosystem functioning relationships

description: Data and code related to 'Trait-based food web model reveals the underlying mechanisms of biodiversity-ecosystem functioning relationships' to reproduce figures and analyses.
DOI: 10.5281/zenodo.3529105
title: Mock Neutron raytracing openPMD data

description: Here a mock dataset in h5 format is uploaded, which complies with the openPMD format with the neutron raytracing extension which is under construction. The extension can be found here: https://github.com/PaNOSC-ViNYL/openPMD-standard/blob/domex_neutron_raytracing/EXT_NRAYTRACING.md The dataset is created manually, and serves as an example of a file that complies with the current version of the openPMD extension for neutron raytracing.The dataset includes particle states of 5000 neutrons after a neutron guide, which are generated with reasonable correlations between velocity, weight and time. The positions at the guide exit are random within a 3 by 8 cm^2 area. The neutron state includes: position (x,y,z) positionOffset (x,y,z) velocity(x,y,z) polarization (x,y,z) weights time The python script used to generate this data is included. This dataset is part of the Deliverable D5.1 in Workpackage 5 (Virtual Neutron and X-ray Laboratory) of the Photon and Neutron Open Science Cloud (PaNOSC). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823852.
DOI: 10.5281/zenodo.3529106
title: Mock Neutron raytracing openPMD data

description: Here a mock dataset in h5 format is uploaded, which complies with the openPMD format with the neutron raytracing extension which is under construction. The extension can be found here: https://github.com/PaNOSC-ViNYL/openPMD-standard/blob/domex_neutron_raytracing/EXT_NRAYTRACING.md The dataset is created manually, and serves as an example of a file that complies with the current version of the openPMD extension for neutron raytracing.The dataset includes particle states of 5000 neutrons after a neutron guide, which are generated with reasonable correlations between velocity, weight and time. The positions at the guide exit are random within a 3 by 8 cm^2 area. The neutron state includes: position (x,y,z) positionOffset (x,y,z) velocity(x,y,z) polarization (x,y,z) weights time The python script used to generate this data is included. This dataset is part of the Deliverable D5.1 in Workpackage 5 (Virtual Neutron and X-ray Laboratory) of the Photon and Neutron Open Science Cloud (PaNOSC). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 823852.
DOI: 10.18169/papdeott008180
title: Hautauflage
DOI: 10.5281/zenodo.3529111
title: Replication data for: Evidence for Dispersing 1D Majorana Channels in an Iron-based Superconductor

description: Replication Data for: Evidence for Dispersing 1D Majorana Channels in an Iron-based Superconductor
DOI: 10.5281/zenodo.3529110
title: Replication data for: Evidence for Dispersing 1D Majorana Channels in an Iron-based Superconductor

description: Replication Data for: Evidence for Dispersing 1D Majorana Channels in an Iron-based Superconductor
DOI: 10.17632/69jxtyzc8r.1
title: Data for: Second-order beliefs and environmental policy support: Evidence from China’s coal-to-gas conversion
DOI: 10.5523/bris.13l8yadatwq602lusa9g9ss786
title: Genome-wide association study of facial emotion recognition in children and association with polygenic risk for mental health disorders

description: This study was a genome-wide association study of emotion recognition. Eight-year-old participants (n = 4,097) from the Avon Longitudinal Study of Parents and Children (ALSPAC) completed the Diagnostic Analysis of Non-Verbal Accuracy (DANVA) faces test. Genome-wide genotype data was available from the Illumina HumanHap550 Quad microarray. Genome-wide association studies were performed to assess associations with recognition of individual emotions and emotion in general.
DOI: 10.5281/zenodo.3529310
title: Novel representation of leaf phenology improves simulation of Amazonian evergreen forest photosynthesis in a land surface model

description: This dataset contains the LAI, Litterfall and GPP etc. of the four Amazon FLUX sites (BR-Sa1, BR-Sa3, BR-Ma2 and GF-Guy) simulated using the improved ORCHIDEE model and the corresponding FLUXNET eddy-covariance or ground-measured data. The more detial please the ReadMe.pdf in the zip. Data is organized with netCDF4(.nc). <br> If you want to know more detail please contact: chenxzh73@mail.sysu.edu.cn
DOI: 10.5281/zenodo.3529309
title: Novel representation of leaf phenology improves simulation of Amazonian evergreen forest photosynthesis in a land surface model

description: This dataset contains the LAI, Litterfall and GPP etc. of the four Amazon FLUX sites (BR-Sa1, BR-Sa3, BR-Ma2 and GF-Guy) simulated using the improved ORCHIDEE model and the corresponding FLUXNET eddy-covariance or ground-measured data. The more detial please the ReadMe.pdf in the zip. Data is organized with netCDF4(.nc). <br> If you want to know more detail please contact: chenxzh73@mail.sysu.edu.cn
DOI: 10.5281/zenodo.3529161
title: Machine learning pipeline to train toxicity prediction model of FunTox-Networks

description: Machine Learning pipeline used to provide toxicity prediction in FunTox-Networks 01_DATA # preprocessing and filtering of raw activity data from ChEMBL<br> - Chembl_v25 # latest activity assay data set from ChEMBL (retrieved Nov 2019)<br> - filt_stats.R # Filtering and preparation of raw data<br> - Filtered # output data sets from filt_stats.R<br> - toxicity_direction.csv # table of toxicity measurements and their proportionality to toxicity 02_MolDesc # Calculation of molecular descriptors for all compounds within the filtered ChEMBL data set<br> - datastore # files with all compounds and their calculated molecular descriptors based on SMILES<br> - scripts<br> - calc_molDesc.py # calculates for all compounds based on their smiles the molecular descriptors<br> - chemopy-1.1 # used python package for descriptor calculation as decsribed in: https://doi.org/10.1093/bioinformatics/btt105 03_Averages # Calculation of moving averages for levels and organisms as required for calculation of Z-scores<br> - datastore # output files with statistics calculated by make_Z.R<br> - scripts<br> -make_Z.R # script to calculate statistics to calculate Z-scores as used by the regression models<br> <br> 04_ZScores # Calculation of Z-scores and preparation of table to fit regression models<br> - datastore # Z-normalized activity data and molecular descriptors in the form as used for fitting regression models<br> - scripts<br> -calc_Ztable.py # based on activity data, molecular descriptors and Z-statistics, the learning data is calculated 05_Regression # Performing regression. Preparation of data by removing of outliers based on a linear regression model. Learning of random forest regression models. Validation of learning process by cross validation and tuning of hyperparameters. - datastore # storage of all random forest regression models and average level of Z output value per level and organism (zexp_*.tsv)<br> - scripts<br> - data_preperation.R # set up of regression data set, removal of outliers and optional removal of fields and descriptors<br> - Rforest_CV.R # analysis of machine learning by cross validation, importance of regression variables and tuning of hyperparameters (number of trees, split of variables)<br> - Rforest.R # based on analysis of Rforest_CV.R learning of final models rregrs_output<br> # early analysis of regression model performance with the package RRegrs as described in: https://doi.org/10.1186/s13321-015-0094-2
DOI: 10.5281/zenodo.3529162
title: Machine learning pipeline to train toxicity prediction model of FunTox-Networks

description: Machine Learning pipeline used to provide toxicity prediction in FunTox-Networks 01_DATA # preprocessing and filtering of raw activity data from ChEMBL<br> - Chembl_v25 # latest activity assay data set from ChEMBL (retrieved Nov 2019)<br> - filt_stats.R # Filtering and preparation of raw data<br> - Filtered # output data sets from filt_stats.R<br> - toxicity_direction.csv # table of toxicity measurements and their proportionality to toxicity 02_MolDesc # Calculation of molecular descriptors for all compounds within the filtered ChEMBL data set<br> - datastore # files with all compounds and their calculated molecular descriptors based on SMILES<br> - scripts<br> - calc_molDesc.py # calculates for all compounds based on their smiles the molecular descriptors<br> - chemopy-1.1 # used python package for descriptor calculation as decsribed in: https://doi.org/10.1093/bioinformatics/btt105 03_Averages # Calculation of moving averages for levels and organisms as required for calculation of Z-scores<br> - datastore # output files with statistics calculated by make_Z.R<br> - scripts<br> -make_Z.R # script to calculate statistics to calculate Z-scores as used by the regression models<br> <br> 04_ZScores # Calculation of Z-scores and preparation of table to fit regression models<br> - datastore # Z-normalized activity data and molecular descriptors in the form as used for fitting regression models<br> - scripts<br> -calc_Ztable.py # based on activity data, molecular descriptors and Z-statistics, the learning data is calculated 05_Regression # Performing regression. Preparation of data by removing of outliers based on a linear regression model. Learning of random forest regression models. Validation of learning process by cross validation and tuning of hyperparameters. - datastore # storage of all random forest regression models and average level of Z output value per level and organism (zexp_*.tsv)<br> - scripts<br> - data_preperation.R # set up of regression data set, removal of outliers and optional removal of fields and descriptors<br> - Rforest_CV.R # analysis of machine learning by cross validation, importance of regression variables and tuning of hyperparameters (number of trees, split of variables)<br> - Rforest.R # based on analysis of Rforest_CV.R learning of final models rregrs_output<br> # early analysis of regression model performance with the package RRegrs as described in: https://doi.org/10.1186/s13321-015-0094-2
DOI: 10.5281/zenodo.2645509
title: C-isotopic signatures and soil properties of Amazon basin oxisols

description: This dataset presents C isotopic data from two sites (Apuí and Manacapuru) located in the state of Amazonas, Brazil. Soils were sampled at three time periods, under weak raining (March-2016), extreme dry (August-2016), and strong wet (March-2017) conditions. The dataset first presents general information about the site (on the tab "site"), followed by more detailed information (on the tab "profile") about both sampling locations. The coordinates, altitude, mean annual temperature, mean annual precipitation, soil order in USDA taxonomy and their respective land use categories and vegetation classifications are described. On the 'layer' tab, information about the soil depth, percent sand, silt and clay, pH CaCl2 and H2O, Organic and Total Carbon, total nitrogen and carbon/nitrogen ratio are described. The bulk C-isotopic signature is also listed on this tab as the Bulk Layer Δ14C and its standard deviation, Bulk Layer Fraction Modern and its standard deviation. The "Incubation" tab describes details of the soil incubations conducted at Apuí and Manacapuru. Information about the material and length of incubation, as well as the CO2 fluxes over the duration of incubation are reported. The respired C-isotopic signature during the incubation is also given on this tab as the incubation Δ14C and its standard deviation, incubation Fraction Modern and its standard deviation.
description: Supported by: BMZ, Ministério da Educação, DAAD, CAPES, GIZ, MPI
DOI: 10.5281/zenodo.2645510
title: C-isotopic signatures and soil properties of Amazon basin oxisols

description: This dataset presents C isotopic data from two sites (Apuí and Manacapuru) located in the state of Amazonas, Brazil. Soils were sampled at three time periods, under weak raining (March-2016), extreme dry (August-2016), and strong wet (March-2017) conditions. The dataset first presents general information about the site (on the tab "site"), followed by more detailed information (on the tab "profile") about both sampling locations. The coordinates, altitude, mean annual temperature, mean annual precipitation, soil order in USDA taxonomy and their respective land use categories and vegetation classifications are described. On the 'layer' tab, information about the soil depth, percent sand, silt and clay, pH CaCl2 and H2O, Organic and Total Carbon, total nitrogen and carbon/nitrogen ratio are described. The bulk C-isotopic signature is also listed on this tab as the Bulk Layer Δ14C and its standard deviation, Bulk Layer Fraction Modern and its standard deviation. The "Incubation" tab describes details of the soil incubations conducted at Apuí and Manacapuru. Information about the material and length of incubation, as well as the CO2 fluxes over the duration of incubation are reported. The respired C-isotopic signature during the incubation is also given on this tab as the incubation Δ14C and its standard deviation, incubation Fraction Modern and its standard deviation.
description: Supported by: BMZ, Ministério da Educação, DAAD, CAPES, GIZ, MPI
DOI: 10.5281/zenodo.3524306
title: Photonics Actors Database

description: The data are collected from the European countries in which the partners of the EPRISE project are based, and predominantly includes photonics Academia, Clusters and Companies who are active in the four EPRISE project target markets: agriculture, food, medical technologies and pharmaceuticals. The list is not intended to be exhaustive, while it is continuously updated by the project consortium.
DOI: 10.5281/zenodo.3524307
title: Photonics Actors Database

description: The data are collected from the European countries in which the partners of the EPRISE project are based, and predominantly includes photonics Academia, Clusters and Companies who are active in the four EPRISE project target markets: agriculture, food, medical technologies and pharmaceuticals. The list is not intended to be exhaustive, while it is continuously updated by the project consortium.
DOI: 10.5281/zenodo.3529566
title: NDVI, noise and land temperature of Geneva

description: Statistical data set (mean, standard deviation, median and sum) of - Land temperature (Landsat 8 satellite : band 11 thermal) - NDVI (Landsat 8 satellite : band 4 red and 5 NIR) - Road traffic noise (day and night) - Hectometric vector grid representing inhabited hectares of the canton of Geneva.
description: {"references": ["https://www.usgs.gov/faqs/what-are-best-landsat-spectral-bands-use-my-research?, consulted on 05.11.2019", "https://earthexplorer.usgs.gov/Information, consulted on 05.11.2019", "https://www.bafu.admin.ch/bafu/fr/home/themes/bruit/etat/cartes/geodonnees.html, consulted on 05.11.2019"]}
DOI: 10.5281/zenodo.3529567
title: NDVI, noise and land temperature of Geneva

description: Statistical data set (mean, standard deviation, median and sum) of - Land temperature (Landsat 8 satellite : band 11 thermal) - NDVI (Landsat 8 satellite : band 4 red and 5 NIR) - Road traffic noise (day and night) - Hectometric vector grid representing inhabited hectares of the canton of Geneva.
description: {"references": ["https://www.usgs.gov/faqs/what-are-best-landsat-spectral-bands-use-my-research?, consulted on 05.11.2019", "https://earthexplorer.usgs.gov/Information, consulted on 05.11.2019", "https://www.bafu.admin.ch/bafu/fr/home/themes/bruit/etat/cartes/geodonnees.html, consulted on 05.11.2019"]}
DOI: 10.7910/dvn/e43moq/gae3nw
title: readme.txt

description: :unav
DOI: 10.7910/dvn/e43moq/lyqkjy
title: tab-fig-1.R

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title: pan ftr.png

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title: Thumbs.db

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DOI: 10.7910/dvn/9qugur/zesgib
title: pan imp.png

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DOI: 10.7910/dvn/uihbc7/7uewku
title: DAT_ASCII_EURGBP_T_201506.txt

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DOI: 10.7910/dvn/uihbc7/8jxxre
title: DAT_ASCII_EURGBP_T_201509.tab

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DOI: 10.7910/dvn/uihbc7/bhkdca
title: DAT_ASCII_EURGBP_T_201507.txt

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DOI: 10.7910/dvn/uihbc7/btf3fb
title: DAT_ASCII_EURGBP_T_201508.tab

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DOI: 10.7910/dvn/uihbc7/ndyckt
title: DAT_ASCII_EURGBP_T_201506.tab

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DOI: 10.7910/dvn/uihbc7/vkxcff
title: DAT_ASCII_EURGBP_T_201508.txt

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DOI: 10.7910/dvn/uihbc7/zrp2rh
title: DAT_ASCII_EURGBP_T_201507.tab

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DOI: 10.7910/dvn/9qugur/0gh2qk
title: hnd ftr.png

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title: nic crn.png

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DOI: 10.7910/dvn/uihbc7/4pk8ng
title: DAT_ASCII_EURUSD_M1_201506.txt

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DOI: 10.7910/dvn/uihbc7/gfwhyz
title: DAT_ASCII_EURGBP_T_201511.tab

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DOI: 10.7910/dvn/uihbc7/hru2uk
title: DAT_ASCII_EURGBP_T_201511.txt

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DOI: 10.7910/dvn/uihbc7/htxq9j
title: DAT_ASCII_EURGBP_T_201509.txt

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DOI: 10.7910/dvn/uihbc7/i6v8r8
title: DAT_ASCII_EURGBP_T_201510.tab

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title: DAT_ASCII_EURUSD_M1_201508.tab

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title: DAT_ASCII_EURUSD_M1_201506.tab

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title: DAT_ASCII_EURUSD_M1_201507.tab

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DOI: 10.7910/dvn/uihbc7/z2unqo
title: DAT_ASCII_EURGBP_T_201510.txt

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title: hnd imp.png

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title: cri ftr.png

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title: gtm ftr.png

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DOI: 10.7910/dvn/9qugur/mhtvcl
title: 01.ReadmeCoffee.docx

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DOI: 10.7910/dvn/uihbc7/7cpkdu
title: DAT_ASCII_EURUSD_M1_201510.txt

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title: DAT_ASCII_EURUSD_M1_201509.txt

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title: DAT_ASCII_EURUSD_M1_201511.tab

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title: DAT_ASCII_EURUSD_M1_201509.tab

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title: DAT_ASCII_EURUSD_M1_201510.tab

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