cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7737126
title: Glutamate and GABA are related to compulsive behaviour in individuals with obsessive compulsive disorder and healthy controls

description: This is the Data Source file for all the tables, figures, and data analysed in this article.
DOI: 10.5281/zenodo.7811467
title: Cortical glutamate and GABA are related to compulsive behaviour in individuals with obsessive compulsive disorder and healthy controls

description: This link contains the Data Source files, the code, and raw MRS data analysed in this article.
DOI: 10.5281/zenodo.7832197
title: Cortical glutamate and GABA are related to compulsive behaviour in individuals with obsessive compulsive disorder and healthy controls

description: This link contains the Data Source files, the code, and raw MRS data analysed in this article.
DOI: 10.6073/pasta/b3debdca5457a909a4b5087579596073
title: Periphyton Accumulation Rates from Shark River Slough, Taylor Slough and Florida Bay, Everglades National Park (FCE LTER), South Florida, USA, January 2001 - ongoing

description: Periphyton accumulation rates were measured bimonthly at FCE LTER sites in Florida Bay and Shark Slough. Glass slides were used as artificial substrates for periphyton growth. Twenty slides were incubated for 2 months in triplicate periphytometers (flow-through plastic boxes) at the surface (SRS sites) and bottom (FB) at each site. Twelve artificial blades (made from transparency film) were incubated for 2 months in triplicate in specially constructed wire frames at the bottom at sites TS/PH 9, TS/PH 10 and TS/PH 11. Data are presented in terms of grams dry and ash-free dry mass per area per time. This is part of continuous data collection to test the hypothesis that phosphorus and nitrogen limit productivity at the upstream and downstream ends, respectively, of the FCE transects. Conclusions await longer-term data collection.
DOI: 10.5281/zenodo.7832221
title: desmognathus_folkertsi_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7832220
title: desmognathus_folkertsi_spades_01.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22434547.v9
title: Different patterns of foreground and background processing contribute to texture segregation in humans: an electrophysiological study

description: The original data and examples of stimulus presentation for this study <br>
DOI: 10.5281/zenodo.1322364
title: STEM and EDX maps of biomass-MnO2 assemblages

description: Raw images and maps described in: Contaminant loading and competitive access of Pb, Zn and Mn(III) to vacancy sites in biogenic MnO<sub>2</sub> Julia Gonzalez Holguera<sup>1</sup>, Imelda Dossou Etui<sup>1</sup>, Jasquelin Peña<sup>1*</sup>
DOI: 10.5281/zenodo.1322363
title: STEM and EDX maps of biomass-MnO2 assemblages

description: Raw images and maps described in: Contaminant loading and competitive access of Pb, Zn and Mn(III) to vacancy sites in biogenic MnO<sub>2</sub> Julia Gonzalez Holguera<sup>1</sup>, Imelda Dossou Etui<sup>1</sup>, Jasquelin Peña<sup>1*</sup>
DOI: 10.34740/kaggle/dsv/5417910
title: #ChatGPT 1000 Daily 🐦 Tweets
DOI: 10.5281/zenodo.7832237
title: desmognathus_wrighti_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.7832236
title: desmognathus_wrighti_spades_01.fasta.gz

description: .
DOI: 10.15151/esrf-es-1160670353
title: Structural Biology at EMBL Grenoble, IBS-CIBB and IAB

description: This proposal clusters crystallography projects at the EMBL Grenoble, IBS and IAB working on different topics with a focus on nucleic acid-protein complexes and host-pathogen interactions. The proposers have long-standing experience in the use, as well as the development of beam lines for macromolecular crystallography and have participated in successive BAG applications over the years, mostly at the ESRF but also at other synchrotrons (i.e. Soleil, Petra II, Diamond, SLS)
DOI: 10.5281/zenodo.7830375
title: Example data set for NG-QTAIM eigenvector-following trajectories: rotary f-NAIBP motor

description: Example dataset for NG-QTAIM eigenvector following trajectories: the F-NAIBP molecular rotary motor.
description: Full details of dataset in README.md
DOI: 10.5281/zenodo.7830374
title: Example data set for NG-QTAIM eigenvector-following trajectories: rotary f-NAIBP motor

description: Example dataset for NG-QTAIM eigenvector following trajectories: the F-NAIBP molecular rotary motor.
description: Full details of dataset in README.md
DOI: 10.5519/qd.7pam1xgq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.34740/kaggle/dsv/5418325
title: arXiv Dataset
DOI: 10.5281/zenodo.7830074
title: Chemoproteomic identification of a DPP4 homolog in Bacteroides thetaiotaomicron

description: Serine hydrolases play important roles in signaling and human metabolism, yet little is known about their functions in gut commensal bacteria. Using bioinformatics and chemoproteomics, we identify serine hydrolases in the gut commensal <em>Bacteroides thetaiotaomicron</em> that are specific to the Bacteroidetes phylum. Two are predicted homologs of the human protease dipeptidyl peptidase 4 (hDPP4), a key enzyme that regulates insulin signaling. Functional studies reveal that BT4193 is a true homolog of hDPP4 that can be inhibited by FDA-approved type 2 diabetes medications targeting hDPP4, while the other is a misannotated proline-specific triaminopeptidase. We demonstrate that BT4193 is important for envelope integrity and that loss of BT4193 reduces <em>B. thetaiotaomicron</em> fitness during <em>in vitro</em> growth within a diverse community. However, neither function is dependent on BT4193 proteolytic activity, suggesting a scaffolding or signaling function for this bacterial protease.
DOI: 10.5281/zenodo.7830073
title: Chemoproteomic identification of a DPP4 homolog in Bacteroides thetaiotaomicron

description: Serine hydrolases play important roles in signaling and human metabolism, yet little is known about their functions in gut commensal bacteria. Using bioinformatics and chemoproteomics, we identify serine hydrolases in the gut commensal <em>Bacteroides thetaiotaomicron</em> that are specific to the Bacteroidetes phylum. Two are predicted homologs of the human protease dipeptidyl peptidase 4 (hDPP4), a key enzyme that regulates insulin signaling. Functional studies reveal that BT4193 is a true homolog of hDPP4 that can be inhibited by FDA-approved type 2 diabetes medications targeting hDPP4, while the other is a misannotated proline-specific triaminopeptidase. We demonstrate that BT4193 is important for envelope integrity and that loss of BT4193 reduces <em>B. thetaiotaomicron</em> fitness during <em>in vitro</em> growth within a diverse community. However, neither function is dependent on BT4193 proteolytic activity, suggesting a scaffolding or signaling function for this bacterial protease.
DOI: 10.6084/m9.figshare.22639402
title: Reptile Database data dump 2015-03

description: See Reptile Database for details, citations, publications etc. <br> Citation: Uetz, P., Freed, P. & Hošek, J. (eds.) (2015) The Reptile Database, http://www.reptile-database.org, accessed April 2015
DOI: 10.6084/m9.figshare.22639402.v1
title: Reptile Database data dump 2015-03

description: See Reptile Database for details, citations, publications etc. <br> Citation: Uetz, P., Freed, P. & Hošek, J. (eds.) (2015) The Reptile Database, http://www.reptile-database.org, accessed April 2015
DOI: 10.6084/m9.figshare.22639417
title: Right Agent, Wrong Task: How High (vs Low) Hedonic Values in AI-performed Tasks Leads to Decreased Perceptions of Humanlikeness, Warmth, and Less Consumer Support.

description: Datasets used in the corresponding research.
DOI: 10.34740/kaggle/dsv/5418522
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.6084/m9.figshare.22639420
title: Reptile Database data dump 2016-04

description: See Reptile Database for details, citations, publications etc. <br> Citation: Uetz, P., Freed, P. & Hošek, J. (eds.) (2016) The Reptile Database, http://www.reptile-database.org, accessed May 2016
DOI: 10.6084/m9.figshare.22639420.v1
title: Reptile Database data dump 2016-04

description: See Reptile Database for details, citations, publications etc. <br> Citation: Uetz, P., Freed, P. & Hošek, J. (eds.) (2016) The Reptile Database, http://www.reptile-database.org, accessed May 2016
DOI: 10.6084/m9.figshare.22434547.v10
title: Different patterns of foreground and background processing contribute to texture segregation in humans: an electrophysiological study

description: The original data and examples of stimulus presentation for this study <br>
DOI: 10.6084/m9.figshare.22639426
title: Reptile Database data dump 2018-07

description: See Reptile Database for details, citations, publications etc. <br> Citation: Uetz, P., Freed, P. & Hošek, J. (eds.) (2018) The Reptile Database, http://www.reptile-database.org, accessed August 2018 <br> For most recent version contact Peter Uetz
DOI: 10.6084/m9.figshare.22639423
title: Reptile Database data dump 2017-05

description: See Reptile Database for details, citations, publications etc. <br> Citation: Uetz, P., Freed, P. & Hošek, J. (eds.) (2017) The Reptile Database, http://www.reptile-database.org, accessed May 2017 <br> Please contact Peter Uetz for most recent releases and data dumps
DOI: 10.6084/m9.figshare.22639432
title: Reptile Database data dump 2019-04

description: See Reptile Database for details, citations, publications etc. <br> Citation: Uetz, P., Freed, P. & Hošek, J. (eds.) (2019) The Reptile Database, http://www.reptile-database.org, accessed May 2019 <br> For most recent releases please contact Peter Uetz
DOI: 10.6084/m9.figshare.22639438
title: Reptile Database data dump 2020-04

description: Please cite Uetz, P., Freed, P, Aguilar, R., Reyes, F. & Hošek, J. (eds.) (2020) The Reptile Database, http://www.reptile-database.org, accessed May 2020 (or later, but not later than the next version, which was released in August 2020). <br> For details see http://www.reptile-database.org <br> For most recent releases, please contact Peter Uetz
DOI: 10.5281/zenodo.7497509
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2017

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2018 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2017. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2016</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2018</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2017 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7492098
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2017

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2018 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2017. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2016</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2018</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2017 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.21227/fe9z-qw92
title:  DeepSense 6G Machine Learning Challenge

description: The DeepSense 6G Machine Learning Challenge is a student competition organized by the Student and Outreach Subcommittee (SOSC) of the IEEE Information Theory Society, in collaboration with the Wireless Intelligence Lab at Arizona State University (ASU). The competition is focused on developing innovative machine learning solutions for various applications using the DeepSense 6G dataset, which comprises coexisting multi-modal sensing and communication data, such as mmWave wireless communication, camera, GPS data, LiDAR, and Radar, collected in realistic wireless environments. The competition consists of three tasks of increasing difficulty that require participants to use different machine and deep learning modalities and techniques. The scenario considered is as follows: a remote radio head (RRH) is tasked with assisting a base station (BS) in communicating toward a user equipment (UE). The RRH obtains the channel state information (CSI) of the channel between the RRH and UE and wishes to communicate this estimate to the BS through a noiseless but rate-limited channel. The BS reconstructs CSI under a given MSE distortion, while also complementing this estimate through a datastream consisting of radar, lidar, GPS, and image data. This CSI estimate is then used in downstream tasks that are not considered further.
DOI: 10.5281/zenodo.7496083
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2018

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2018. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2017</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2019</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2018 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7490483
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 500m SIN Grid in 2018

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2018. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2017</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2019</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2018 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7755485
title: MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 500m SIN Grid in 2019

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FAPAR product at 500m spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao <em>et al</em>., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous. This dataset is the MUSES FAPAR product in 2019. <em>Please <strong>click here</strong> to download the MUSES FAPAR </em><em>product <strong>in 2018</strong></em>, and <em><strong>click here</strong> to download the MUSES FAPAR </em><em>product <strong>in 2020</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2019 Spatial Resolution: 500m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. <em>Remote Sensing of Environment</em>, 171,105-117, 2015. Xiao Zhiqiang, <em>et al</em>., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 56, 5509-5524, 2018. Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. <em>Remote Sensing</em>, 14, 3304, 2022. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).