cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/fe06b9dd42129609afc02a0268b01acf
title: Experimental evaluation of louver guidance efficiency for green sturgeon (Acipenser medirostris), Primary Datasets, 2016-2017

description: Throughout the world, louver-bypass systems are a common method for fish protection at water diversion sites. This study used controlled laboratory experiments to quantify louver efficiency for juvenile green sturgeon under a range of conditions. Green sturgeon juveniles used in the study were spawned from the University of California, Davis (hereafter UC Davis) captive broodstock program. Experimental trials were conducted within an indoor flume at the J. Amorocho Hydraulics Laboratory (JAHL) at UC Davis. The flume had a zero degree bed slop, and was equipped with a louver placed at a 15-degree angle to the streamwise flow. Louver slats had 25-mm clear spacing. The louver terminated at a bypass channel 0.3 m wide, and was operated to maintain water velocity in the bypass that was 1.2 times greater than the velocity in the flume. Full factorial experimental treatments were designed to address the influence of sturgeon size, water velocity, diel period, and water temperature on louver performance and behavior. Sturgeon were tested within three predefined size classes (range: 6 – 34 cm TL). At each size class, fish were tested under combinations of water velocity, water temperature, and photophase. During each experimental trial, 60 (+/-2) juvenile sturgeon were released from an acclimation chamber to enter the test area at the bottom of the flume. Fish were allowed to freely navigate throughout the test area until they were transported through the louver slats or bypass channel, at which point they were removed and the time was recorded. Trials ended when all fish were collected at a downstream endpoint, or after 90 minutes of exposure time, and remaining fish were removed from the flume. Trials were completed across two study years within seven months after hatch, following the same protocols with a few minor adjustments in year two. During daytime trials in both study years the louver face was monitored with video recordings to observe contact between fish and the louver slats across the 7m louver face. During nighttime trials in 2017, infrared (IR) lights and IR-sensitive cameras were added, allowing video recording of the 2.4 m of the louver face closest to the bypass channel. Videos were post-processed and contact events were recorded along with location, time, and rheotaxis upon contact.
DOI: 10.21227/wwt7-w739
title: Radar Measurements of Two Vehicles with Three Cooperative Imaging Sensors

description: Presented are the target lists of a total of 8718 measurement frames produced by three (3) radarsensors, matched together with the GNSS-RTK solution for position and motion of two (2) automobiles.The sensors are synced in respect to their measurement times, but operate independently. Theirmodulation is offset by a certain frequency, to ensure mono-static evaluation. This allows for acooperative evaluation of measurements.The sensor positions are static. The two automobiles are driving maneuvers in from of the threeradar sensors, including following, overtaking, driving alongside, and driving towards each other.The data set is available in three different CFAR threshold variants.Included is a description of the setup and radar modulation parameters, the data structure, as wellas a minimum working example of how to work with the data.
DOI: 10.6084/m9.figshare.14776821
title: Supplementary Files for Carbon Foam

description: Supplementary data from the study <strong>Remarkable Hydrogen and Carbon Dioxide Uptake on Scalable and Inexpensive Microporous Carbon Foams.</strong> <br> Includes adsorption data formatted in AIF file format, original files for some figures in the SI, as well as Stan codes for isotherm fitting.
DOI: 10.6084/m9.figshare.14776821.v1
title: Supplementary Files for Carbon Foam

description: Supplementary data from the study <strong>Remarkable Hydrogen and Carbon Dioxide Uptake on Scalable and Inexpensive Microporous Carbon Foams.</strong> <br> Includes adsorption data formatted in AIF file format, original files for some figures in the SI, as well as Stan codes for isotherm fitting.
DOI: 10.5066/f76972gb
title: Humpback chub (Gila cypha) and mean daily water temperature data, western Grand Canyon - 2000 to 2016

description: These data were compiled to examine patterns of humpback chub (Gila cypha) in western Grand Canyon from just below Havasu Creek at river kilometer (rkm) 253 (Lees Ferry is rkm 0) downriver to Pearce Ferry at rkm 451. These data represent mean daily water temperatures in degrees Celsius from two USGS gaging locations (2005-2016), catches of individual humpback chub in hoop nets (2005-2016), total lengths of individual humpback chub captured in hoop nets during 2014, 2015 and 2016, and the effort (number of seine hauls) and catch of humpback chub in seines (2000-2016) in the Colorado River. These data were collected by research staff from the Grand Canyon, Monitoring & Research Center, Southwest Biological Science Center during field observations from 2000 to 2016. These data can be used to identify trends in humpback chub catch rates in the mainstem Colorado River downstream of Glen Canyon Dam in Grand Canyon. Long-term monitoring data from both hoop nets and seining was used to document likely local recruitment and population expansion of humpback chub in western Grand Canyon.
DOI: 10.6084/m9.figshare.20449530
title: mmdetection-song gao.rar

description: As an outstanding method for ocean monitoring, Synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain few feature information. It is difficult to detect them from the background clutter, and there is a problem of low detection rate and high false alarm. To improve the detection accuracy for small-scale ships, we propose an efficient ship detection model based on YOLOX, called YOLO-SD. First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in overall performance.
DOI: 10.6084/m9.figshare.20449530.v1
title: mmdetection-song gao.rar

description: As an outstanding method for ocean monitoring, Synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain few feature information. It is difficult to detect them from the background clutter, and there is a problem of low detection rate and high false alarm. To improve the detection accuracy for small-scale ships, we propose an efficient ship detection model based on YOLOX, called YOLO-SD. First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in overall performance.
DOI: 10.5517/ccdc.csd.cc1zyls9
title: CCDC 1845702: Experimental Crystal Structure Determination

description: Related Article: Szymon Sobczak, Abhishek Chitnis, Michał Andrzejewski, Mirosław Mączka, Smita Gohil, Nandini Garg, Andrzej Katrusiak|2018|CrystEngComm|20|5348|doi:10.1039/C8CE00884A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29x6s7
title: CCDC 2142279: Experimental Crystal Structure Determination

description: Related Article: Bo Qin, Zhepei Lu, Yanxing Jia|2022|Angew.Chem.,Int.Ed.|61|e202201712|doi:10.1002/anie.202201712
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29lbx5
title: CCDC 2132797: Experimental Crystal Structure Determination

description: Related Article: Xue-Yi Hu, Xiao-Ming Li, Hui Liu, Bin-Gui Wang, Ling-Hong Meng|2022|Bioorg.Chem.|128|106021|doi:10.1016/j.bioorg.2022.106021
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc288pcm
title: CCDC 2093720: Experimental Crystal Structure Determination

description: Related Article: Yu Wang, Kanglei Liu, Pangkuan Chen, Xiaodong Yin, Tai Peng, Javed Iqbal, Nan Wang|2022|J.Mater.Chem.C|10|10981|doi:10.1039/D2TC01999J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bvfl7
title: CCDC 2170359: Experimental Crystal Structure Determination

description: Related Article: Yu Wang, Kanglei Liu, Pangkuan Chen, Xiaodong Yin, Tai Peng, Javed Iqbal, Nan Wang|2022|J.Mater.Chem.C|10|10981|doi:10.1039/D2TC01999J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cgyg8
title: CCDC 2189110: Experimental Crystal Structure Determination

description: Related Article: Haiting Yin, Yunjun Wu, Xiaoxia Gu, Zhijun Feng, Meifang Wang, Dexiang Feng, Ming Wang, Ziyang Cheng, Shaoyin Wang|2022|RSC Advances|12|21066|doi:10.1039/D2RA03416F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.4388169
title: Data for article: Crack Identification in Necked Double Shear Lugs by means of the Electro-Mechanical Impedance Method

description: Data to preproduce results presented in the article:<br> Title: <strong>Crack Identification in Necked Double Shear Lugs by means of the Electro-Mechanical Impedance Method</strong><br> Authors: Markus Winklberger *, Christoph Kralovec, Christoph Humer, Peter Heftberger, Martin Schagerl<br> *Correspondence: markus.winklberger@jku.at<br> Journal: Sensors<br> Section: Physical Sensors<br> Special Issue: Piezoelectric Transducers Based Structural Health Monitoring Data saved in csv-files with ";" delimiter.
DOI: 10.5281/zenodo.4388168
title: Data for article: Crack Identification in Necked Double Shear Lugs by means of the Electro-Mechanical Impedance Method

description: Data to preproduce results presented in the article:<br> Title: <strong>Crack Identification in Necked Double Shear Lugs by means of the Electro-Mechanical Impedance Method</strong><br> Authors: Markus Winklberger *, Christoph Kralovec, Christoph Humer, Peter Heftberger, Martin Schagerl<br> *Correspondence: markus.winklberger@jku.at<br> Journal: Sensors<br> Section: Physical Sensors<br> Special Issue: Piezoelectric Transducers Based Structural Health Monitoring Data saved in csv-files with ";" delimiter.
DOI: 10.17909/6p65-wg08
title: Data from Loyd et al. 2022 FUV Variability of GJ 436

description: All COS G130M data of GJ 436 as of 2022-08-08.
DOI: 10.5281/zenodo.6973297
title: GENEA Challenge 2022 3D coordinates of submitted motion

description: This Zenodo repository contains Cartesian 3D coordinates in npy format for all test-set motion submitted by teams participating in the GENEA Challenge 2022. The npy format is easily readable using the NumPy package in Python. Attribution: If you use this material, please cite our latest paper on the GENEA Challenge 2022. At the time of writing (2022-08-04) this is our ACM ICMI 2022 paper: Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM. You can find the latest information and a BibTeX file on the project website: https://youngwoo-yoon.github.io/GENEAchallenge2022/ Conditions FNA and UNA in the data contain motion from the Talking With Hands 16.2M dataset at https://github.com/facebookresearch/TalkingWithHands32M/. These are licensed under a CC BY NC 4.0 international license. The remaining material is available under a CC BY 4.0 international license, with the text provided in LICENSE.txt. To find more GENEA Challenge 2022 material on the web, please see: * https://youngwoo-yoon.github.io/GENEAchallenge2022/ * https://genea-workshop.github.io/2022/challenge/
DOI: 10.5281/zenodo.6973296
title: GENEA Challenge 2022 3D coordinates of submitted motion

description: This Zenodo repository contains Cartesian 3D coordinates in npy format for all test-set motion submitted by teams participating in the GENEA Challenge 2022. The npy format is easily readable using the NumPy package in Python. Attribution: If you use this material, please cite our latest paper on the GENEA Challenge 2022. At the time of writing (2022-08-04) this is our ACM ICMI 2022 paper: Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM. You can find the latest information and a BibTeX file on the project website: https://youngwoo-yoon.github.io/GENEAchallenge2022/ Conditions FNA and UNA in the data contain motion from the Talking With Hands 16.2M dataset at https://github.com/facebookresearch/TalkingWithHands32M/. These are licensed under a CC BY NC 4.0 international license. The remaining material is available under a CC BY 4.0 international license, with the text provided in LICENSE.txt. To find more GENEA Challenge 2022 material on the web, please see: * https://youngwoo-yoon.github.io/GENEAchallenge2022/ * https://genea-workshop.github.io/2022/challenge/
DOI: 10.6084/m9.figshare.20449630.v1
title: Late glacial and Holocene glaciation history of North and Northeast Greenland

description: Northeast Greenland is the place where the Greenland Ice Sheet (GrIS) experienced the largest areal changes since the Last Glacial Maximum. However, the age constraints of the last deglaciation are in some areas sparse. In this study, we use forty-seven new <sup>10</sup>Be cosmogenic exposure ages to constrain the deglaciation of the present-day ice-free areas in Northeast Greenland. Our results show that the outer coast region was deglaciated between 12.8 ± 0.6 and 11.5 ± 0.2 ka and the region close to the present ice margin was deglaciated 2 to 4 ka later between 9.2 ± 0.3 to 8.6 ± 0.3 ka. By combining our new results with previously published data from North and Northeast Greenland, we find that the ice sheet advanced to the shelf edge between 26 and 20 cal. ka BP. The outer coast was deglaciated between 12.8 and 9.7 ka and the present ice extent was reached between 10.8 to 5.8 ka. The ice margin continued to retreat farther inland during the Middle Holocene before it readvanced toward its Little Ice Age position. The deglaciation was probably forced by a combination of increased atmospheric and ocean temperatures, but local topography also played an important role. These results add to the growing knowledge about the glaciation history of the GrIS and add useful constraints for future ice sheet models.
DOI: 10.6084/m9.figshare.20449630
title: Late glacial and Holocene glaciation history of North and Northeast Greenland

description: Northeast Greenland is the place where the Greenland Ice Sheet (GrIS) experienced the largest areal changes since the Last Glacial Maximum. However, the age constraints of the last deglaciation are in some areas sparse. In this study, we use forty-seven new <sup>10</sup>Be cosmogenic exposure ages to constrain the deglaciation of the present-day ice-free areas in Northeast Greenland. Our results show that the outer coast region was deglaciated between 12.8 ± 0.6 and 11.5 ± 0.2 ka and the region close to the present ice margin was deglaciated 2 to 4 ka later between 9.2 ± 0.3 to 8.6 ± 0.3 ka. By combining our new results with previously published data from North and Northeast Greenland, we find that the ice sheet advanced to the shelf edge between 26 and 20 cal. ka BP. The outer coast was deglaciated between 12.8 and 9.7 ka and the present ice extent was reached between 10.8 to 5.8 ka. The ice margin continued to retreat farther inland during the Middle Holocene before it readvanced toward its Little Ice Age position. The deglaciation was probably forced by a combination of increased atmospheric and ocean temperatures, but local topography also played an important role. These results add to the growing knowledge about the glaciation history of the GrIS and add useful constraints for future ice sheet models.
DOI: 10.6084/m9.figshare.14036549.v1
title: Estimated Deaths, Intensive Care Admissions and Hospitalizations Averted in Canada during the COVID-19 Pandemic

description: These datasets explore disparities in COVID-19 mortality observed in the US and Canada between January 2020 and early March 2021. Table 1 provides counts of deaths, hospitalizations, ICU admissions, and cases, by age, for Ontario, Canada (Canada's most populous province). <br>Table 2 estimates deaths averted by Canada's response to the COVID-19 pandemic, relative to that in the United States, by "Canada-standardizing" the US epidemic (i.e., by applying US age-specific mortality to Canadian populations, in order to estimate the deaths that would have occurred in a Canadian pandemic with the same rates of death as have been observed in the US). Observed Canadian deaths are compared to "expected" deaths with a US-like response in order to estimate both deaths averted and SMR (Table 2).<br>As Canadian age groups for purposes of death reporting are slightly different from those used in the US (e.g., 0-17 in the US vs. 0-19 in Canada), we reallocate Canadian deaths based on proportions of deaths occurring in 2-year age categories in Ontario (Table 1).<br>Ontario age-specific case-fatality is used to inflate the deaths averted, in order to estimate cases averted. Ontario age-specific hospitalization and ICU risk (again derived from Table 1) are used to estimate hospitalizations and ICU admissions averted (Table 2).
DOI: 10.5281/zenodo.6573878
title: MODIS MNDWI, 8-day time series for Europe at 1 km resolution

description: Modified Normalized Difference Water Index (MNDWI) from MODIS data for Europe at 1 km resolution. Source data:<br> - MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid (MOD09A1 v006): https://lpdaac.usgs.gov/products/mod09a1v006/ The corresponding MODIS/Aqua product (MYD09A1 v006) could not be used due to the fact that the Aqua satellite has a number of broken detectors resulting in unreliable data for band 6 (SWIR) measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. For the time periods October 2016 - March 2017 and August 2020 - April 2021, the original data has been reprojected to ETRS89-extended / LAEA Europe and aggregated to a 1 km grid. The temporal resolution is 8 days. Bad quality pixels (cloud, cloud shadow, dead detector, solar zenith angle too large, etc.) have been masked using the provided quality assurance (QA) layers and appear as "no data". File naming:<br> productCode.acquisitionDate[A (YYYYDDD)]_mosaic_spatialResolution_frequency_VI.tif<br> example: MOD09A1.A2016353_mosaic_1000m_8_days_MNDWI.tif The date is Year and Day of Year. Values are MNDWI * 10000. Example: Value -5099 = -0.5099 Projection + EPSG code:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035) Spatial extent:<br> north: 72N<br> south: 30S<br> west: -52W<br> east: 49E Spatial resolution:<br> 1 km Temporal resolution:<br> 8 days Pixel values:<br> MNDWI * 10000 (scaled to Integer; example: value -5099 = -0.5099) Software used:<br> GRASS GIS 8.0 Original dataset license:<br> All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: https://lpdaac.usgs.gov/products/mod09a1v006/ Processed by:<br> mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)
description: Acknowledgements: This study was partially funded by EU grant 874850 MOOD. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission.