cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4614565
title: Weather data file of ISTANBUL, KARS and ANTALYA

description: Weather data file of ISTANBUL, KARS and ANTALYA
DOI: 10.5281/zenodo.4614564
title: Weather data file of ISTANBUL, KARS and ANTALYA

description: Weather data file of ISTANBUL, KARS and ANTALYA
DOI: 10.6084/m9.figshare.14182640
title: wrfout_d01_0036-00009_20:00:00_sfc.ctl

description: Kite et al PNAS 2021 GCM output
DOI: 10.6084/m9.figshare.14182640.v1
title: wrfout_d01_0036-00009_20:00:00_sfc.ctl

description: Kite et al PNAS 2021 GCM output
DOI: 10.6084/m9.figshare.14182646
title: wrfout_d01_0036-00009_20:00:00_sub.dat

description: Kite et al PNAS 2021 GCM output
DOI: 10.6084/m9.figshare.14182634
title: wrfout_d01_0036-00009_20:00:00_sfc.dat

description: Kite et al PNAS 2021 GCM output
DOI: 10.6084/m9.figshare.14182634.v1
title: wrfout_d01_0036-00009_20:00:00_sfc.dat

description: Kite et al PNAS 2021 GCM output
DOI: 10.6084/m9.figshare.14182649
title: wrfout_d01_0036-00009_20:00:00_atm.ctl

description: Kite et al PNAS 2021 GCM output
DOI: 10.6084/m9.figshare.14182646.v1
title: wrfout_d01_0036-00009_20:00:00_sub.dat

description: Kite et al PNAS 2021 GCM output
DOI: 10.6084/m9.figshare.14182649.v1
title: wrfout_d01_0036-00009_20:00:00_atm.ctl

description: Kite et al PNAS 2021 GCM output
DOI: 10.6084/m9.figshare.14182631
title: wrfout_d01_0036-00009_20:00:00_sub.ctl

description: Kite et al PNAS 2021 GCM output
DOI: 10.6084/m9.figshare.14182631.v1
title: wrfout_d01_0036-00009_20:00:00_sub.ctl

description: Kite et al PNAS 2021 GCM output
DOI: 10.5281/zenodo.4613881
title: DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations

description: <strong>models.tar.gz</strong>: Models of protein mutants used for the predictions. <strong>predictions.tar.gz</strong>: ΔΔG predictions by using DDGun and DDGun3D methods.
DOI: 10.5281/zenodo.4613880
title: DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations

description: <strong>models.tar.gz</strong>: Models of protein mutants used for the predictions. <strong>predictions.tar.gz</strong>: ΔΔG predictions by using DDGun and DDGun3D methods.
DOI: 10.21227/f7hq-9x86
title: jan 20

description: January 20
DOI: 10.3886/e135121
title: Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic

description: Staying home and avoiding unnecessary contact is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States enacted policies to encourage distancing and some mandated staying home. Understanding how these policies interact with individuals’ voluntary responses to the COVID-19 ep- idemic is a critical initial step in understanding the role of these nonpharmaceutical interventions in transmission dynamics and assessing policy impacts. We use variation in policy responses along with smart device data that measures the amount of time Americans stayed home to disentangle the extent that observed shifts in staying home behavior are induced by policy. We find evidence that stay-at-home orders and voluntary response to locally reported COVID-19 cases and deaths led to behavioral change. For the median county, which implemented a stay-at-home order with about two cases, we find that the response to stay-at-home orders increased time at home as if the county had experienced 29 addi- tional local cases. However, the relative effect of stay-at-home or- ders was much greater in select counties. On the one hand, the mandate can be viewed as displacing a voluntary response to this rise in cases. On the other hand, policy accelerated the response, which likely helped reduce spread in the early phase of the pan- demic. It is important to be able to attribute the relative role of self- interested behavior or policy mandates to understand the limits and opportunities for relying on voluntary behavior as opposed to im- posing stay-at-home orders.
DOI: 10.3886/e135121v1
title: Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic

description: Staying home and avoiding unnecessary contact is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States enacted policies to encourage distancing and some mandated staying home. Understanding how these policies interact with individuals’ voluntary responses to the COVID-19 ep- idemic is a critical initial step in understanding the role of these nonpharmaceutical interventions in transmission dynamics and assessing policy impacts. We use variation in policy responses along with smart device data that measures the amount of time Americans stayed home to disentangle the extent that observed shifts in staying home behavior are induced by policy. We find evidence that stay-at-home orders and voluntary response to locally reported COVID-19 cases and deaths led to behavioral change. For the median county, which implemented a stay-at-home order with about two cases, we find that the response to stay-at-home orders increased time at home as if the county had experienced 29 addi- tional local cases. However, the relative effect of stay-at-home or- ders was much greater in select counties. On the one hand, the mandate can be viewed as displacing a voluntary response to this rise in cases. On the other hand, policy accelerated the response, which likely helped reduce spread in the early phase of the pan- demic. It is important to be able to attribute the relative role of self- interested behavior or policy mandates to understand the limits and opportunities for relying on voluntary behavior as opposed to im- posing stay-at-home orders.
DOI: 10.6084/m9.figshare.14233544
title: Table S1. Occurrences Rparkeri.csv

description: Table S1. Occurrences of Rickettsia parkeri in America.
DOI: 10.6084/m9.figshare.14233544.v1
title: Table S1. Occurrences Rparkeri.csv

description: Table S1. Occurrences of Rickettsia parkeri in America.
DOI: 10.5517/ccdc.csd.cc26ny2x
title: CCDC 2045909: Experimental Crystal Structure Determination

description: Related Article: Mathias Hermann, Tobias Böttcher, Marcel Schorpp, Sabine Richert, Daniel Wassy, Ingo Krossing, Birgit Esser|2021|Chem.-Eur.J.|27|4964|doi:10.1002/chem.202005131
DOI: 10.5517/ccdc.csd.cc26ny3y
title: CCDC 2045910: Experimental Crystal Structure Determination

description: Related Article: Mathias Hermann, Tobias Böttcher, Marcel Schorpp, Sabine Richert, Daniel Wassy, Ingo Krossing, Birgit Esser|2021|Chem.-Eur.J.|27|4964|doi:10.1002/chem.202005131
DOI: 10.5517/ccdc.csd.cc26nzmh
title: CCDC 2045957: Experimental Crystal Structure Determination

description: Related Article: Mathias Hermann, Tobias Böttcher, Marcel Schorpp, Sabine Richert, Daniel Wassy, Ingo Krossing, Birgit Esser|2021|Chem.-Eur.J.|27|4964|doi:10.1002/chem.202005131
DOI: 10.5517/ccdc.csd.cc26nznj
title: CCDC 2045958: Experimental Crystal Structure Determination

description: Related Article: Mathias Hermann, Tobias Böttcher, Marcel Schorpp, Sabine Richert, Daniel Wassy, Ingo Krossing, Birgit Esser|2021|Chem.-Eur.J.|27|4964|doi:10.1002/chem.202005131
DOI: 10.5517/ccdc.csd.cc26pl4n
title: CCDC 2046531: Experimental Crystal Structure Determination

description: Related Article: Ashley T. Brennan, Katrina A. Zenere, Cameron J. Kepert, Jack K. Clegg, Suzanne M. Neville|2021|Inorg.Chem.|60|3871|doi:10.1021/acs.inorgchem.0c03651
DOI: 10.5517/ccdc.csd.cc26pl5p
title: CCDC 2046532: Experimental Crystal Structure Determination

description: Related Article: Ashley T. Brennan, Katrina A. Zenere, Cameron J. Kepert, Jack K. Clegg, Suzanne M. Neville|2021|Inorg.Chem.|60|3871|doi:10.1021/acs.inorgchem.0c03651
DOI: 10.5517/ccdc.csd.cc26pl6q
title: CCDC 2046533: Experimental Crystal Structure Determination

description: Related Article: Ashley T. Brennan, Katrina A. Zenere, Cameron J. Kepert, Jack K. Clegg, Suzanne M. Neville|2021|Inorg.Chem.|60|3871|doi:10.1021/acs.inorgchem.0c03651
DOI: 10.5517/ccdc.csd.cc26pl7r
title: CCDC 2046534: Experimental Crystal Structure Determination

description: Related Article: Ashley T. Brennan, Katrina A. Zenere, Cameron J. Kepert, Jack K. Clegg, Suzanne M. Neville|2021|Inorg.Chem.|60|3871|doi:10.1021/acs.inorgchem.0c03651
DOI: 10.5517/ccdc.csd.cc26pl8s
title: CCDC 2046535: Experimental Crystal Structure Determination

description: Related Article: Ashley T. Brennan, Katrina A. Zenere, Cameron J. Kepert, Jack K. Clegg, Suzanne M. Neville|2021|Inorg.Chem.|60|3871|doi:10.1021/acs.inorgchem.0c03651
DOI: 10.5517/ccdc.csd.cc26pl9t
title: CCDC 2046536: Experimental Crystal Structure Determination

description: Related Article: Ashley T. Brennan, Katrina A. Zenere, Cameron J. Kepert, Jack K. Clegg, Suzanne M. Neville|2021|Inorg.Chem.|60|3871|doi:10.1021/acs.inorgchem.0c03651
DOI: 10.5517/ccdc.csd.cc26plbv
title: CCDC 2046537: Experimental Crystal Structure Determination

description: Related Article: Ashley T. Brennan, Katrina A. Zenere, Cameron J. Kepert, Jack K. Clegg, Suzanne M. Neville|2021|Inorg.Chem.|60|3871|doi:10.1021/acs.inorgchem.0c03651
DOI: 10.5517/ccdc.csd.cc26vz13
title: CCDC 2051705: Experimental Crystal Structure Determination

description: Related Article: Yuki Morimoto, Fengkun Chen, Yusuke Matsuo, Koki Kise, Takayuki Tanaka, Atsuhiro Osuka|2021|Chem.Asian J.|16|648|doi:10.1002/asia.202001459
DOI: 10.5517/ccdc.csd.cc26vz24
title: CCDC 2051706: Experimental Crystal Structure Determination

description: Related Article: Yuki Morimoto, Fengkun Chen, Yusuke Matsuo, Koki Kise, Takayuki Tanaka, Atsuhiro Osuka|2021|Chem.Asian J.|16|648|doi:10.1002/asia.202001459
DOI: 10.5517/ccdc.csd.cc26vz35
title: CCDC 2051707: Experimental Crystal Structure Determination

description: Related Article: Yuki Morimoto, Fengkun Chen, Yusuke Matsuo, Koki Kise, Takayuki Tanaka, Atsuhiro Osuka|2021|Chem.Asian J.|16|648|doi:10.1002/asia.202001459
DOI: 10.5281/zenodo.4386983
title: Tomographic data acquired by a half-acquisition scanning technique

description: Tomographic data acquired by a half-acquisition scan (360-degree scanning with offset center-of-rotation) was used to demonstrate data processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography"<br> Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect the files together and run "zip -F half_acquisition_scan_splitted.zip<br> --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
description: To extract data, you firstly collect the files together and run "zip -F half_acquisition_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4386982
title: Tomographic data acquired by a half-acquisition scanning technique

description: Tomographic data acquired by a half-acquisition scan (360-degree scanning with offset center-of-rotation) was used to demonstrate data processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography"<br> Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect the files together and run "zip -F half_acquisition_scan_splitted.zip<br> --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
description: To extract data, you firstly collect the files together and run "zip -F half_acquisition_scan_splitted.zip --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5519/qd.d8ec8ang
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.14233391.v1
title: Dataset_Straka-et-al. Metacognition_in_gifted_and_average_children.csv

description: Dataset for the study: Straka, O., Portešová, Š, Halámková, D., & Jabůrek, M. (202X). Metacognitive monitoring and metacognitive strategies of gifted and average children on dealing with deductive reasoning task<br>
DOI: 10.6084/m9.figshare.14233391
title: Dataset_Straka-et-al. Metacognition_in_gifted_and_average_children.csv

description: Dataset for the study: Straka, O., Portešová, Š, Halámková, D., & Jabůrek, M. (202X). Metacognitive monitoring and metacognitive strategies of gifted and average children on dealing with deductive reasoning task<br>
DOI: 10.5281/zenodo.4615129
title: Climate change impact on submesoscale ROMS data

description: Data needed for figures in the JGRO paper "The impact of climate change on ocean submesoscale activity" by Richards, Whitt, Brett, Bryan, Feloy, and Long. Output from regional ROMS run nested within global 1/10-degree CESM run under a timeslice method to represent early and late 21st-century climates. Atmospheric forcing is a repeating year for each climate. Timeslice method uses CESM large ensemble (Kay et al 2015) to determine changes in ocean TS and monthly atmospheric conditions between early and late 21st-century climates. This work is funded by the National Science Foundation (NSF) under grants OCE-1658550 and OCE-1658541.<br> This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement No. 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. We thank all the scientists, software engineers, and administrators who contributed to the development of CESM.<br> DB Whitt also acknowledges support from the NSF, via OPP-1501993, NOAA, via NA18OAR4310408, and NASA, via 80NSSC19K1116.
DOI: 10.5281/zenodo.3892106
title: Climate change impact on submesoscale ROMS data

description: Data needed for figures in the JGRO paper "The impact of climate change on ocean submesoscale activity" by Richards, Whitt, Brett, Bryan, Feloy, and Long. Output from regional ROMS run nested within global 1/10-degree CESM run under a timeslice method to represent early and late 21st-century climates. Atmospheric forcing is a repeating year for each climate. Timeslice method uses CESM large ensemble (Kay et al 2015) to determine changes in ocean TS and monthly atmospheric conditions between early and late 21st-century climates. This work is funded by the National Science Foundation (NSF) under grants OCE-1658550 and OCE-1658541.<br> This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement No. 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. We thank all the scientists, software engineers, and administrators who contributed to the development of CESM.<br> DB Whitt also acknowledges support from the NSF, via OPP-1501993, NOAA, via NA18OAR4310408, and NASA, via 80NSSC19K1116.
DOI: 10.5066/p9g8lkck
title: Geo-referenced orthophotographs of the Snow River, Alaska, acquired September 1, 2018

description: This data release consists of orthophotographs of the Snow River in Alaska acquired on September 1, 2018. The orthophotographs were produced from images obtained using a Hasselblad A6D-100C 100 megapixel digital mapping camera deployed within a pod mounted on the landing gear of a Robinson R44 helicopter. Images were acquired as the helicopter transited a series of flight lines designed to provide complete coverage, with ample overlap, of the study area along the Snow River. Also within the pod was an ATLANS GPS/Inertial Motion Unit (IMU) that recorded the position and orientation of the platform during the flight. This information was used to geo-reference the images by performing aerial triangulation and bundle adjustment within the TerraSolid software suite, which was also used to produce a LiDAR DEM that was incorporated into the orthophoto production process. The resulting orthorectified imagery had a spatial resolution (pixel size) of 0.05 m. The imagery is saved as a GeoTIFF files with an embedded header that contains the spatial referencing information for the image; the projected coordinate system is UTM Zone 6N, NAD83. The main purpose of these orthophotographs was to provide an ancillary dataset that could be used with a related lidar product and data release.
DOI: 10.5281/zenodo.4613224
title: Tomographic data acquired by a combination of a helical scanning and half-acquisition scanning technique: Part 02

description: Tomographic data acquired by a combination of a helical scan and a half-acquisition scan (offset center-of-rotation) was used to demonstrate data processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 5 files uploaded to 3 zenodo sections: *Part01, *Part02, *Part03) and run "zip -F helical_half_acquisition_scan_splitted.zip<br> --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4613223
title: Tomographic data acquired by a combination of a helical scanning and half-acquisition scanning technique: Part 02

description: Tomographic data acquired by a combination of a helical scan and a half-acquisition scan (offset center-of-rotation) was used to demonstrate data processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 5 files uploaded to 3 zenodo sections: *Part01, *Part02, *Part03) and run "zip -F helical_half_acquisition_scan_splitted.zip<br> --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4613644
title: Tomographic data acquired by a combination of a helical scanning and half-acquisition scanning technique: Part 03

description: Tomographic data acquired by a combination of a helical scan and a half-acquisition scan (offset center-of-rotation) was used to demonstrate data processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 5 files uploaded to 3 zenodo sections: *Part01, *Part02, *Part03) and run "zip -F helical_half_acquisition_scan_splitted.zip<br> --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4613047
title: Tomographic data acquired by a combination of a helical scanning and half-acquisition scanning technique: Part 01

description: Tomographic data acquired by a combination of a helical scan and a half-acquisition scan (offset center-of-rotation) was used to demonstrate data processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 5 files uploaded to 3 zenodo sections: *Part01, *Part02, *Part03) and run "zip -F helical_half_acquisition_scan_splitted.zip<br> --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.5281/zenodo.4613643
title: Tomographic data acquired by a combination of a helical scanning and half-acquisition scanning technique: Part 03

description: Tomographic data acquired by a combination of a helical scan and a half-acquisition scan (offset center-of-rotation) was used to demonstrate data processing methods in the paper: "Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography" Nghia T. Vo, Robert C. Atwood, M. Drakopoulos, and Thomas Connolley, https://doi.org/10.1364/OE.418448 To extract data, you firstly collect all the files together (There are 5 files uploaded to 3 zenodo sections: *Part01, *Part02, *Part03) and run "zip -F helical_half_acquisition_scan_splitted.zip<br> --out data.zip" to recreate dataset. Then you can unzip by running "unzip data.zip"
DOI: 10.15156/bio/sh2913498.08fu
title: SH2913498.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2913499.08fu
title: SH2913499.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2913500.08fu
title: SH2913500.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.