cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15787/vtt1/ivnw9l
title: Response of Autonomous Vehicles to Emergency Response Vehicles (03-051)

description: Project Description: The objective of this project is to explore how an autonomous vehicle must identify and safely respond to emergency vehicles using sound, vision and other onboard sensors. Emergency vehicles can belong to police, fire, hospital and other responders. The autonomous vehicle in the presence of an emergency vehicle must have the ability to accurately sense its surroundings in real-time and be able to safely yield to the emergency vehicles. This project identifies the presence of emergency vehicle mainly through onboard vision and safely maneuvers the vehicle and stops it. The project also explored using onboard sound sensors to identify the emergency vehicles. Several proof of concept demonstrations were performed, and the results were presented in several conferences and in poster sessions. The project also lead to the publication of a conference article and fully/partly supported the thesis of an M.S. and several Ph.D. students. Data Scope: Audio Dataset: The dataset used to train the machine learning algorithms for audio based EV detection include a large collection of audio files pertaining to emergency and non-emergency vehicles as well as other road sounds. The audio dataset is annotated into 527 classes representative of various sounds. The data is processed to extract audio files relevant to emergency and non-emergency classes by extracting features like zero crossing rate, spectral parameters, chromatograms etc., using the PyAudio Analysis library. Emergency Vehicle videos: Videos of Emergency vehicles collected from inside the Autonomous vehicle using BFLY-PGE-20E4C-CS 1/1.8" Blackfly® PoE GigE Color Camera. Classification Model Weights: Training weights generated by machine learning models developed and trained on the RAVEV dataset. The different combinations of parameters are as listed in ‘Nayak, A., Gopalswamy, S., and Rathinam, S., "Vision-Based Techniques for Identifying Emergency Vehicles," SAE Technical Paper 2019-01-0889, 2019, https://doi.org/10.4271/2019-01-0889’
DOI: 10.5281/zenodo.3403599
title: Landscape and biodiversity indicators for Samaria

description: Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions. Regarding Samaria, Landscape and biodiversity indicators were generated for 1985, 1995, 2000, 2005, 2010, 2015. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object. The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
DOI: 10.5281/zenodo.3403600
title: Landscape and biodiversity indicators for Samaria

description: Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions. Regarding Samaria, Landscape and biodiversity indicators were generated for 1985, 1995, 2000, 2005, 2010, 2015. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object. The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
DOI: 10.5281/zenodo.3406370
title: Software Variability Tools: Industrial Survey and Systematic Mapping Study

description: Software Variability Tools: Industrial Survey and Systematic Mapping Study
description: Funded by CNPq, Capes, FAPESP (Grant: 2014/02244-7, 2015/00405-6, 2016/05919-0, 2017/06195-9), and Spanish Research network MCIU-AEI TIN2017-90664-REDT
DOI: 10.5281/zenodo.3406371
title: Software Variability Tools: Industrial Survey and Systematic Mapping Study

description: Software Variability Tools: Industrial Survey and Systematic Mapping Study
description: Funded by CNPq, Capes, FAPESP (Grant: 2014/02244-7, 2015/00405-6, 2016/05919-0, 2017/06195-9), and Spanish Research network MCIU-AEI TIN2017-90664-REDT
DOI: 10.5281/zenodo.3403660
title: Landscape and biodiversity indicators for La Palma - Canary Island

description: Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions. Regarding La Palma – Canary Island, Landscape and biodiversity indicators were generated for 2007. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object. The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
DOI: 10.5281/zenodo.3403661
title: Landscape and biodiversity indicators for La Palma - Canary Island

description: Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions. Regarding La Palma – Canary Island, Landscape and biodiversity indicators were generated for 2007. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object. The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
DOI: 10.5061/dryad.d12k64m/1
title: ArchivedData

description: Bioluminescence data
DOI: 10.5281/zenodo.3406613
title: Dataset for "Universal Loop assembly (uLoop): open, efficient, and species-agnostic DNA fabrication"

description: This data is associated to the project "<strong>Universal Loop assembly (uLoop): open, efficient, and species-agnostic DNA fabrication". </strong>For more details see: https://osf.io/kw4fh/ Data is organized as follows: DNA: DNA genbank files for uLoop receivers, L0 parts and L1, L2, L3 and L4 assemblies<br> Efficiency-productivity: scoring representing uLoop assembly efficiency<br> Performance-bac: flow cytometry and plate fluorometry data describing the stability of uLoop genetic constructs in E coli
description: This work was funded by the Gordon and Betty Moore Foundation [GBMF5007.01 to CLD and VB, GBMF4981.01 to FF and PvD]. BP was supported by the Programa de Apoyo a Centros con Financiamiento Basal [grant number AFB 170004]. AK was supported by the National Institutes of Health [grant number 1R01DK11077001A1]. FF, BP, TM and IN were supported by Instituto Milenio iBio and PvD by the Instituto Milenio de Oceanografía [grant number IC120019] of the Iniciativa Científica Milenio MINECON. IN was also supported by a PUC VRI PhD studentship and AC was supported by CONICYT scholarship for doctoral degrees in Chile [grant number 21150778].
DOI: 10.5281/zenodo.3406614
title: Dataset for "Universal Loop assembly (uLoop): open, efficient, and species-agnostic DNA fabrication"

description: This data is associated to the project "<strong>Universal Loop assembly (uLoop): open, efficient, and species-agnostic DNA fabrication". </strong>For more details see: https://osf.io/kw4fh/ Data is organized as follows: DNA: DNA genbank files for uLoop receivers, L0 parts and L1, L2, L3 and L4 assemblies<br> Efficiency-productivity: scoring representing uLoop assembly efficiency<br> Performance-bac: flow cytometry and plate fluorometry data describing the stability of uLoop genetic constructs in E coli
description: This work was funded by the Gordon and Betty Moore Foundation [GBMF5007.01 to CLD and VB, GBMF4981.01 to FF and PvD]. BP was supported by the Programa de Apoyo a Centros con Financiamiento Basal [grant number AFB 170004]. AK was supported by the National Institutes of Health [grant number 1R01DK11077001A1]. FF, BP, TM and IN were supported by Instituto Milenio iBio and PvD by the Instituto Milenio de Oceanografía [grant number IC120019] of the Iniciativa Científica Milenio MINECON. IN was also supported by a PUC VRI PhD studentship and AC was supported by CONICYT scholarship for doctoral degrees in Chile [grant number 21150778].
DOI: 10.17632/hn5f5kmjjc.1
title: Data for: Mineralogical and geochemical characteristics of the Permian coal from the Qinshui Basin, northern China, with emphasis on lithium enrichment

description: Sequential leaching data for lithium in selected No.3 coals, and parting, roof and floor samples from the Sucun and Gaohe Mines, Qinshui Basin
DOI: 10.5281/zenodo.3403605
title: Landscape and biodiversity indicators for Lake Prespa

description: Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions. Regarding Lake Prespa, Landscape and biodiversity indicators were generated for 2012. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object. The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
DOI: 10.5281/zenodo.3403606
title: Landscape and biodiversity indicators for Lake Prespa

description: Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions. Regarding Lake Prespa, Landscape and biodiversity indicators were generated for 2012. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object. The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
DOI: 10.6073/pasta/59f8119de06e68a0e79178bfad18e18a
title: Monthly mean sea level data (1912-2018) relative to NAVD88 for Portland, Maine, NOAA/NOS

description: Monthly sea level data for NOAA/NOS station 8418150, Portland, Maine. Tidal bench marks directions from north bound Interstate 295 in Portland, take the Waterfront Exit (Alt. U.S. 1) to Commercial Street, then continue NE along Commercial Street for 2.4 km (1.5 mi) to the Maine State Pier, the last pier- warehouse along the waterfront. The bench marks are located within 1.6 km (1 mi) radius of tide station. The tide gage is located in the south corner on the off shore end of the Maine State Pier. NOAA/NOS Center for Operational Oceanographic Products and Services (CO-OPS).
DOI: 10.17894/ucph.263de3a8-9d79-46c9-9b5e-8adca7e6a716
title: Vertebrate diversity revealed by metabarcoding of bulk arthropod samples from tropical forests
DOI: 10.17632/4nyfyjbr4z.1
title: Data for: Responses of marine phytoplankton communities to environmental changes: new insights from a niche classification scheme

description: A dataset that contains mean values of total chlorophyll a (iTChla), relative abundances of nine phytoplankton groups (iDino: dinoflagellates, iDiat: diatoms, iHapt_8: haptophytes_8, iHapt_6: haptophytes_6, iChlo: chlorophytes, iCryp: cryptophytes, iProc: Prochlorococcus, iSyne: Synechococcus, and iPras: prasinophytes), and three environmental factors (iTemp: temperature, Em: irradiance, and iNOx: nitrate) in the upper mixed layer of the South China Sea.
DOI: 10.17632/t2vy6rzn6f.1
title: Data for: Individual differences in parietal and frontal cortex structure predict dissociable capacities for perception and cognitive control

description: VBM and behavioural data for each of the individual tasks and factor scores relating to this manuscript
DOI: 10.5061/dryad.0qv86b5/1
title: Dryad Item 10.5061/DRYAD.0QV86B5/1

description: (:tba)
DOI: 10.5061/dryad.0qv86b5/2
title: Dryad Item 10.5061/DRYAD.0QV86B5/2

description: (:tba)
DOI: 10.5281/zenodo.3403652
title: Landscape and biodiversity indicators for Curonian Lagoon

description: Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions. Regarding Curonian Lagoon, Landscape and biodiversity indicators were generated for 2013-2014. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object. The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
DOI: 10.5281/zenodo.3403653
title: Landscape and biodiversity indicators for Curonian Lagoon

description: Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions. Regarding Curonian Lagoon, Landscape and biodiversity indicators were generated for 2013-2014. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object. The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
DOI: 10.17632/bwmswgpvvd.1
title: Data for: Insomnia and cognitive performance: A systematic review and meta-analysis
DOI: 10.7910/dvn/66ntqj/nmni80
title: BVIS-Dry-Season-LULC.pdf

description: :unav
DOI: 10.7910/dvn/jmozfu/krj5qr
title: Bwanje Valley Dry Season LULC Data.pdf

description: :unav
DOI: 10.7910/dvn/ud9crd/a9nx8x
title: BVIS-RS-LULC.pdf

description: :unav
DOI: 10.15482/usda.adc/1504422
title: Data from: Exercise plasma metabolomics and xenometabolomics in obese, sedentary, insulin-resistant women: impact of a fitness and weight loss intervention
DOI: 10.17632/8mdy856frh.3
title: Results of the 3D postioning user study

description: The higher complexity levels of the positioning task require much more time and produce many more errors irrespective of the input method applied. ANOVA found that the positioning task was performed significantly faster with the 3D glove (M=139.1 s, SD=12) than with the 2D glove (M=198.4 s, SD=21.5), F1, 29=11.4, p=0.002. As can be seen in Fig. 9-left, the decrease in the task completion time was much higher for Levels 2 and 3 than for Level 1. The discovered significant differences between 2D and 3D input were 88.3 s (p=0.001) for the most complex trial (Level 3) and 71.8 s (p&lt;0.001) for the slightly less complex trial (Level 2). (this is version 3 to test RDM implementations)
DOI: 10.5281/zenodo.3403604
title: Landscape and biodiversity indicators for Montado

description: Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions. Regarding Montado, Landscape and biodiversity indicators were generated for 2007, 2012. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object. The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
DOI: 10.5281/zenodo.3406407
title: NDVI for Northern Limestone (2005/06/19)

description: NDVI computation (using Landsat-5 TM bands: (([NIR]-[R])/([NIR]+[R])), scale factor 0.001. Clip to area of interest. Cloud masking.
DOI: 10.5281/zenodo.3406408
title: NDVI for Northern Limestone (2005/06/19)

description: NDVI computation (using Landsat-5 TM bands: (([NIR]-[R])/([NIR]+[R])), scale factor 0.001. Clip to area of interest. Cloud masking.
DOI: 10.7910/dvn/9bzu2c/9bdwmv
title: BVIS_ManagementQuestionnarie_RainySeason.pdf

description: :unav
DOI: 10.7910/dvn/pzejwa/0mw7mh
title: BVIS_ManagementQuestionnarie_DrySeason.pdf

description: :unav
DOI: 10.7910/dvn/qkfevm/so7myq
title: BVIS_ManagementQuestionnarie_RainySeasonv2.pdf

description: :unav
DOI: 10.5066/p9v3xdy6
title: Data on the Deliverables, Sustainability, and Collaboration of Community for Data Integration Projects from 2010-2016

description: These four tables document the data used to draw the findings in the manuscript "Measuring sustainability of seed-funded Earth science informatics projects," by Hsu et al. Table 1, "ListProjects-data" lists the projects funded by the Community for Data Integration from 2010-2016. Table 2, "Outputs-data" lists the deliverables that were proposed and delivered to the Community for Data Integration from 2010-2016, and notes if they were still accessible or sustained at the time of this study (December 2018). Table 3, "Influences-and-Sustainabiltiy-data" lists the projects and the sustainability influences examined in this study, as well as if the project achieved individual-level or organizational-level sustainability. Table 4, "Collaborations-data" lists each project and the number of project personnel associated with different organizations.
DOI: 10.5281/zenodo.3403603
title: Landscape and biodiversity indicators for Montado

description: Landscape and biodiversity indicators have been identified as crucial for detecting changes in the Land Cover/Habitat map target classes and evaluating threats and intense impacts on certain areas of a site. This analysis is useful to prevent future ecosystem degradation, update the preservation strategies or take immediate mitigation actions. Regarding Montado, Landscape and biodiversity indicators were generated for 2007, 2012. The Land Cover/Habitat map and Object-ID raster files were used as input to estimate the indicators. The outputs include a raster file of each indicator and a file “indValues.csv” containing the values of indicators per object. The calculated indicators are: (i) PLAND; (ii) PD; (iii) SHAPE_MN; (iv) CA; (v) MPS; (vi) MESH; (vii) AWMPFD. Indicator files are accompanied by INSPIRE metadata XML. Detailed information can be found in the “Readme.pdf” included in the zip containing the dataset.
DOI: 10.5281/zenodo.3406415
title: NDVI for Northern Limestone (2005/09/07)

description: NDVI computation (using Landsat-5 TM bands: (([NIR]-[R])/([NIR]+[R])), scale factor 0.001. Clip to area of interest. Cloud masking.
DOI: 10.5281/zenodo.3406416
title: NDVI for Northern Limestone (2005/09/07)

description: NDVI computation (using Landsat-5 TM bands: (([NIR]-[R])/([NIR]+[R])), scale factor 0.001. Clip to area of interest. Cloud masking.
DOI: 10.17632/h23kcd7vyk.1
title: Data for: Survey of male college students' perceptions and knowledge of speech-language pathology

description: Survey used to asses male undergraduate's knowledge and perceptions of speech-language pathology
DOI: 10.5519/qd.sbc40a0r
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5435 records)
DOI: 10.5281/zenodo.3406344
title: Nepal School Seismology Network

description: The Nepal School Seismology Network is a low-cost seismological network installed for both educational and observational purposes. After a pilot station installed in 2018, the network operates 22 sensors since April-May 2019 (see map). The sensors are RaspberryShake 1D instruments. The data is freely available to the public through IRIS (http://ds.iris.edu) and RaspberryShake (https://raspberryshake.org/). The online waveforms are visible at: https://raspberryshake.net/stationview/ (then zoom to Nepal). The station are regrouped under the "_NSSN" virtual network code. Full details on the stations can be found here: http://ds.iris.edu/mda/_NSSN/ Further details on the Seismology at School in Nepal program can be found here: www.seismoschoolnp.org Both the program and the network operate on a non-profit basis. We are grateful to all participating schools, teachers and students for hosting the instruments.
DOI: 10.5281/zenodo.3406345
title: Nepal School Seismology Network

description: The Nepal School Seismology Network is a low-cost seismological network installed for both educational and observational purposes. After a pilot station installed in 2018, the network operates 22 sensors since April-May 2019 (see map). The sensors are RaspberryShake 1D instruments. The data is freely available to the public through IRIS (http://ds.iris.edu) and RaspberryShake (https://raspberryshake.org/). The online waveforms are visible at: https://raspberryshake.net/stationview/ (then zoom to Nepal). The station are regrouped under the "_NSSN" virtual network code. Full details on the stations can be found here: http://ds.iris.edu/mda/_NSSN/ Further details on the Seismology at School in Nepal program can be found here: www.seismoschoolnp.org Both the program and the network operate on a non-profit basis. We are grateful to all participating schools, teachers and students for hosting the instruments.