cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25914/j8ax-5g32
title: ERA5 monthly averaged by hour of day data on potential temperature levels

description: ERA5 is the fifth generation model reanalysis of the global climate from the European Centre for Medium-range Weather Forecasts (ECMWF). Data at NCI is replicated through the Meteorological Archival and Retrieval System (MARS) and hosted for use by the Australian research community. This dataset includes monthly averaged by hour of day (MNTH) data on potential temperature levels from 1979 to present time (approx. 3 months in arrears). Please note that the initial release of this dataset may not include all available products. Please see the NCI ERA5 Community site for information on the NCI data replication status. Full details of the model and outputs can be found in the ECMWF ERA5 Data Documentation.
DOI: 10.25914/691z-3536
title: ERA5 hourly data on potential vorticity levels

description: ERA5 is the fifth generation model reanalysis of the global climate from the European Centre for Medium-range Weather Forecasts (ECMWF). Data at NCI is replicated through the Meteorological Archival and Retrieval System (MARS) and hosted for use by the Australian research community. This dataset includes hourly data (OPER) data on potential vorticity levels from 1979 to present time (approx. 3 months in arrears). Please note that the initial release of this dataset may not include all available products. Please see the NCI ERA5 Community site for information on the NCI data replication status. Full details of the model and outputs can be found in the ECMWF ERA5 Data Documentation.
DOI: 10.25914/vema-yr08
title: ERA5 hourly data on potential temperature levels

description: ERA5 is the fifth generation model reanalysis of the global climate from the European Centre for Medium-range Weather Forecasts (ECMWF). Data at NCI is replicated through the Meteorological Archival and Retrieval System (MARS) and hosted for use by the Australian research community. This dataset includes hourly data (OPER) data on potential temperature levels from 1979 to present time (approx. 3 months in arrears). Please note that the initial release of this dataset may not include all available products. Please see the NCI ERA5 Community site for information on the NCI data replication status. Full details of the model and outputs can be found in the ECMWF ERA5 Data Documentation.
DOI: 10.5281/zenodo.6391677
title: Data for manuscript: Ecological lags govern the pace and outcome of plant community responses to 21st century climate change

description: These data were used in the analyses reported in Block et al. "Ecological lags govern the pace and outcome of plant community responses to 21st century climate change".
description: This research was funded by Swiss National Science Foundation grant 31003A_173210.
DOI: 10.5281/zenodo.6391676
title: Data for manuscript: Ecological lags govern the pace and outcome of plant community responses to 21st century climate change

description: These data were used in the analyses reported in Block et al. "Ecological lags govern the pace and outcome of plant community responses to 21st century climate change".
description: This research was funded by Swiss National Science Foundation grant 31003A_173210.
DOI: 10.5517/ccdc.csd.cc29z1n0
title: CCDC 2144042: Experimental Crystal Structure Determination

description: Related Article: Jas S. Ward, Rosa M. Gomila, Antonio Frontera, Kari Rissanen|2022|RSC Advances|12|8674|doi:10.1039/D2RA01390H
DOI: 10.5517/ccdc.csd.cc29z1p1
title: CCDC 2144043: Experimental Crystal Structure Determination

description: Related Article: Jas S. Ward, Rosa M. Gomila, Antonio Frontera, Kari Rissanen|2022|RSC Advances|12|8674|doi:10.1039/D2RA01390H
DOI: 10.5517/ccdc.csd.cc29z1q2
title: CCDC 2144044: Experimental Crystal Structure Determination

description: Related Article: Jas S. Ward, Rosa M. Gomila, Antonio Frontera, Kari Rissanen|2022|RSC Advances|12|8674|doi:10.1039/D2RA01390H
DOI: 10.5517/ccdc.csd.cc2b5byt
title: CCDC 2150096: Experimental Crystal Structure Determination

description: Related Article: Jas S. Ward, Rosa M. Gomila, Antonio Frontera, Kari Rissanen|2022|RSC Advances|12|8674|doi:10.1039/D2RA01390H
DOI: 10.5517/ccdc.csd.cc2b5bzv
title: CCDC 2150097: Experimental Crystal Structure Determination

description: Related Article: Jas S. Ward, Rosa M. Gomila, Antonio Frontera, Kari Rissanen|2022|RSC Advances|12|8674|doi:10.1039/D2RA01390H
DOI: 10.34740/kaggle/dsv/3373474
title: Polish-Ukrainian translations from Tatoeba
DOI: 10.15472/iuyxwr
title: Peces, crustáceos decápodos y moluscos de San Andrés y Providencia recopilados a partir de literatura - Expedición Sea Flower Plus 2021

description: A partir de búsqueda de información secundaria se recopiló información las especies de peces, crustáceos decápodos y moluscos de aguas dulces y salobres del Archipiélago de San Andrés, Providencia y Santa Catalina. Se revisó tanto bibliografía de estudios previos publicados como bases de datos de colecciones de la ictiofauna y carcinofauna registrada para la zona. Se registró un total de 28 especies de peces distribuidas en 10 ordenes y 17 familias; 18 especies de crustáceos decápodos distribuidos en 1 orden y 9 familias y moluscos pertenecientes a 1 orden y 1 familia.
DOI: 10.5281/zenodo.6392140
title: Draft Genome Sequences of Pseudomonas sp strain MWU-15.20650

description: Annotated genome of Pseudomonas sp. MWU 15-20650.
DOI: 10.5281/zenodo.6392139
title: Draft Genome Sequences of Pseudomonas sp strain MWU-15.20650

description: Annotated genome of Pseudomonas sp. MWU 15-20650.
DOI: 10.5281/zenodo.6392145
title: Draft Genome Sequences of Pseudomonas sp. Strain MWU12-2233, Isolated from a Wild Cranberry Bog in Provincetown, Massachusetts

description: Annotated genome of Pseudomonas sp. MWU12-2233.
DOI: 10.5281/zenodo.6392144
title: Draft Genome Sequences of Pseudomonas sp. Strain MWU12-2233, Isolated from a Wild Cranberry Bog in Provincetown, Massachusetts

description: Annotated genome of Pseudomonas sp. MWU12-2233.
DOI: 10.6084/m9.figshare.19166253
title: A comparison of temperature and relative humidity measurements derived from COSMIC-2 radio occultations with radiosonde observations made over the Asian summer monsoon region

description: The newly launched FORMOSAT-7/COSMIC-2 (Formosa Satellite Mission-7 and Constellation Observing System for Meteorology, Ionosphere, and Climate-2), has an equatorial constellation of six satellites carrying advanced radio occultation receivers, providing high-resolution measurements of temperature and humidity between 45° N to 45° S. COSMIC-2 provides global data of about 5000–6000 profiles per day and has better profiling over the tropics as compared to FORMOSAT-3/COSMIC-1. The vertical resolution of COSMIC-2 (50 m up to tropopause) is better than COSMIC-1 (100 m). In this study, two years of COSMIC-2 measurements are compared with radiosonde observations over the Asian summer monsoon region, which is a highly monsoon active region and a gateway for stratospheric pollutants. The present study analysed the accuracy of temperature and water vapour measurements in the lower and middle atmosphere. A very good agreement between COSMIC-2 and radiosonde measured temperature by an absolute mean difference of less than 0.5 K with a standard deviation of less than 2.5 K is observed. Relative humidity shows a mean difference of 10% with a standard deviation of 15–20%. These results indicate that the COSMIC-2 sounding is robust, has high accuracy, and has excellent global coverage, especially over the tropical and sub-tropical regions.
DOI: 10.6084/m9.figshare.19166253.v1
title: A comparison of temperature and relative humidity measurements derived from COSMIC-2 radio occultations with radiosonde observations made over the Asian summer monsoon region

description: The newly launched FORMOSAT-7/COSMIC-2 (Formosa Satellite Mission-7 and Constellation Observing System for Meteorology, Ionosphere, and Climate-2), has an equatorial constellation of six satellites carrying advanced radio occultation receivers, providing high-resolution measurements of temperature and humidity between 45° N to 45° S. COSMIC-2 provides global data of about 5000–6000 profiles per day and has better profiling over the tropics as compared to FORMOSAT-3/COSMIC-1. The vertical resolution of COSMIC-2 (50 m up to tropopause) is better than COSMIC-1 (100 m). In this study, two years of COSMIC-2 measurements are compared with radiosonde observations over the Asian summer monsoon region, which is a highly monsoon active region and a gateway for stratospheric pollutants. The present study analysed the accuracy of temperature and water vapour measurements in the lower and middle atmosphere. A very good agreement between COSMIC-2 and radiosonde measured temperature by an absolute mean difference of less than 0.5 K with a standard deviation of less than 2.5 K is observed. Relative humidity shows a mean difference of 10% with a standard deviation of 15–20%. These results indicate that the COSMIC-2 sounding is robust, has high accuracy, and has excellent global coverage, especially over the tropical and sub-tropical regions.
DOI: 10.5281/zenodo.6392152
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Stylianos Chatzimanolis, https://orcid.org/0000-0001-9008-0611. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.6084/m9.figshare.19424015.v11
title: Neocortical Localization and Thalamocortical Modulation of Neuronal Hyperexcitability contribute to Fragile X Syndrome: Source Data for Figures and Statistics

description: Source data for manuscript as requested by publisher.
DOI: 10.5281/zenodo.6371680
title: CNN-Filter-DB

description: <strong>A diverse database of over 1.4B 3x3 convolution filters extracted from CNN models trained for various tasks in diverse image domains.</strong> We collected a total of 647 publicly available CNN models that have been pre-trained for various 2D visual tasks. In order to provide a heterogeneous and diverse representation of convolution filters "in the wild", we retrieved pre-trained models for 11 different tasks e.g. such as classification, segmentation} and image generation. We also recorded various meta-data such as depth and frequency of included operations for each model, and manually categorized the variety of used training sets into 16 visually distinctive groups like natural scenes, medical ct, seismic, or astronomy. In total, the models were trained on 71 different data sets. The dominant subset is formed by image classification models trained on ImageNet1k (355 models). <strong>More details:</strong> https://github.com/paulgavrikov/cnn-filter-db
description: This work was supported in part by the German Ministry for Science, Research and Arts Baden-Wuerttemberg (MWK) under Grant 32-7545.20/45/1 Quality Assurance of Machine Learning Applications (Q-AMeLiA). https://q-amelia.in.hs-furtwangen.de/
DOI: 10.5281/zenodo.6371679
title: CNN-Filter-DB

description: <strong>A diverse database of over 1.4B 3x3 convolution filters extracted from CNN models trained for various tasks in diverse image domains.</strong> We collected a total of 647 publicly available CNN models that have been pre-trained for various 2D visual tasks. In order to provide a heterogeneous and diverse representation of convolution filters "in the wild", we retrieved pre-trained models for 11 different tasks e.g. such as classification, segmentation} and image generation. We also recorded various meta-data such as depth and frequency of included operations for each model, and manually categorized the variety of used training sets into 16 visually distinctive groups like natural scenes, medical ct, seismic, or astronomy. In total, the models were trained on 71 different data sets. The dominant subset is formed by image classification models trained on ImageNet1k (355 models). <strong>More details:</strong> https://github.com/paulgavrikov/cnn-filter-db
description: This work was supported in part by the German Ministry for Science, Research and Arts Baden-Wuerttemberg (MWK) under Grant 32-7545.20/45/1 Quality Assurance of Machine Learning Applications (Q-AMeLiA). https://q-amelia.in.hs-furtwangen.de/
DOI: 10.25452/figshare.plus.17869013
title: Back color images supporting "Functional movement screen dataset collected with two Azure Kinect depth sensors"

description: This is color images of Azure Kinect sensor in back position. <br> This dataset supports the following publication: Xing, QJ., Shen, YY., Cao, R. et al. Functional movement screen dataset collected with two Azure Kinect depth sensors. Sci Data 9, 104 (2022). https://doi.org/10.1038/s41597-022-01188-7 <br>See related materials in collection at: https://doi.org/10.25452/figshare.plus.c.5774969 <br>Collection Description: This presents a dataset for vision-based autonomous functional movement screen (FMS) collected from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up, and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, and 3D human skeleton joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, three-dimensional trajectories, quaternions, and 2D pixel trajectories of 32 joints were recorded. Our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 158 GB. As a supplement, we also provide color image data from the other two cameras (back and side low positions). This dataset provides the opportunity for automatic action quality evaluation of FMS.
DOI: 10.25452/figshare.plus.17871707
title: Side low color images supporting "Functional movement screen dataset collected with two Azure Kinect depth sensors"

description: This is color images of Azure Kinect sensor in side low position. <br> This dataset supports the following publication: Xing, QJ., Shen, YY., Cao, R. et al. Functional movement screen dataset collected with two Azure Kinect depth sensors. Sci Data 9, 104 (2022). https://doi.org/10.1038/s41597-022-01188-7 <br>See related materials in collection at: https://doi.org/10.25452/figshare.plus.c.5774969 <br>Collection Description: This presents a dataset for vision-based autonomous functional movement screen (FMS) collected from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up, and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, and 3D human skeleton joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, three-dimensional trajectories, quaternions, and 2D pixel trajectories of 32 joints were recorded. Our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 158 GB. As a supplement, we also provide color image data from the other two cameras (back and side low positions). This dataset provides the opportunity for automatic action quality evaluation of FMS.
DOI: 10.25452/figshare.plus.18095906
title: Front color images supporting "Functional movement screen dataset collected with two Azure Kinect depth sensors"

description: This is color images of Azure Kinect sensor in front position. <br><br> This dataset supports the following publication: Xing, QJ., Shen, YY., Cao, R. et al. Functional movement screen dataset collected with two Azure Kinect depth sensors. Sci Data 9, 104 (2022). https://doi.org/10.1038/s41597-022-01188-7 <br>See related materials in collection at: https://doi.org/10.25452/figshare.plus.c.5774969 <br><br>Collection Description: This presents a dataset for vision-based autonomous functional movement screen (FMS) collected from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up, and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, and 3D human skeleton joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, three-dimensional trajectories, quaternions, and 2D pixel trajectories of 32 joints were recorded. Our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 158 GB. As a supplement, we also provide color image data from the other two cameras (back and side low positions). This dataset provides the opportunity for automatic action quality evaluation of FMS.
DOI: 10.6084/m9.figshare.19441403
title: Source data_220328.xlsx

description: Source data
DOI: 10.25452/figshare.plus.18108341
title: Side color images supporting "Functional movement screen dataset collected with two Azure Kinect depth sensors"

description: This is color images of Azure Kinect sensor in side position. <br><br> This dataset supports the following publication: Xing, QJ., Shen, YY., Cao, R. et al. Functional movement screen dataset collected with two Azure Kinect depth sensors. Sci Data 9, 104 (2022). https://doi.org/10.1038/s41597-022-01188-7 <br>See related materials in collection at: https://doi.org/10.25452/figshare.plus.c.5774969 <br>Collection Description: This presents a dataset for vision-based autonomous functional movement screen (FMS) collected from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up, and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, and 3D human skeleton joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, three-dimensional trajectories, quaternions, and 2D pixel trajectories of 32 joints were recorded. Our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 158 GB. As a supplement, we also provide color image data from the other two cameras (back and side low positions). This dataset provides the opportunity for automatic action quality evaluation of FMS.
DOI: 10.25452/figshare.plus.18126782
title: Front depth images supporting "Functional movement screen dataset collected with two Azure Kinect depth sensors"

description: This is depth images of Azure Kinect sensor in front position. <br> This dataset supports the following publication: Xing, QJ., Shen, YY., Cao, R. et al. Functional movement screen dataset collected with two Azure Kinect depth sensors. Sci Data 9, 104 (2022). https://doi.org/10.1038/s41597-022-01188-7 <br>See related materials in collection at: https://doi.org/10.25452/figshare.plus.c.5774969 <br>Collection Description: This presents a dataset for vision-based autonomous functional movement screen (FMS) collected from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up, and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, and 3D human skeleton joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, three-dimensional trajectories, quaternions, and 2D pixel trajectories of 32 joints were recorded. Our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 158 GB. As a supplement, we also provide color image data from the other two cameras (back and side low positions). This dataset provides the opportunity for automatic action quality evaluation of FMS.
DOI: 10.25345/c5m03z184
title: MassIVE MSV000089138 - Transcription Factor Acj6 Specifies Distinct Dendrite Targets via Unique Cell-Surface Protein Combinations
title: Xie Q, Li J1, Li H, Udeshi ND, Svinkina T, Kohani S, Guajardo R, Mani DR, Xu C, Li T, Han S, Wei W, Shuster SA, Luginbuhl DJ, Ting AY, Carr SA, Luo L. Transcription factors specify the fate and connectivity of developing neurons. To understand how distal neurites execute commands from these nucleus-residing factors to specify their connectivity, we investigated how a lineage-specific transcription factor, Acj6, controls the precise dendrite targeting of Drosophila olfactory projection neurons (PNs). Quantitative cell-surface proteomic profiling of wild-type and acj6 mutant PNs in intact developing brains and a proteome-informed genetic screen identified PN surface proteins that execute Acj6-regulated wiring decisions. These include canonical cell adhesion molecules and molecules previously not associated with wiring, such as the mechanosensitive ion channel Piezo. Ion channel activity is dispensable for Piezo's function in PN dendrite targeting. Combinatorial expression of Acj6 executors more strongly rescued specific acj6 mutant phenotypes than expression of individual executors. Together, our findings reveal that a key transcription factor controls wiring specificity of different neuron types by regulating expression of unique combinations of cell-surface executors.