cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p9r4v21z
title: Debris-flow and Flood Video Files, Chalk Cliffs, Colorado, USA, 2015

description: Chalk Cliffs, located 8 miles southwest of Buena Vista, Colorado, is one of the most active debris-flow areas in the state (U.S. Geological Survey). Three stations were set up at Chalk Cliffs which are located sequentially along a channel draining the 0.3 km2 study area. These stations are equipped with rain gauges, laser distance meters, and data loggers to record rainfall and stage data (Kean, et al., 2020). This data release includes videos of debris-flows and floods captured by high-definition cameras placed at two different locations, associated with the monitoring stations, along the study area at Chalk Cliffs during 2015. Both cameras are located near the Upper Station (Station 1). One is located at the bridge cross section at the channel (Bridge Camera) and another on the opposite side of the basin with a broad view of the channel (Wide-angle Camera). The attached figure "station_and_camera_locations.png" provides an overview figure with the location of the two cameras and three stations along the channel. The videos and their audio are used to record information on flow type and velocity of debris flows. They can also be used to interpret how the flow characteristics, such as sediment concentration, vary with time to compare to seismic observations. Video recording for all cameras is triggered using a rainfall threshold, derived from rainfall measurements using the rain gauges (Michel et al., 2019).The complete videos for all the cameras are downloaded manually during site visits. More detailed information about the cameras and settings used can be found in the metadata file under "Process steps" in "Data quality information." During 2015, several debris flows and floods were captured by the two cameras. The videos can currently be viewed in the individual 'Child item' pages accessible below. The 'Child item' pages contain either flood or debris flow video files for each camera described above. The following citations relate to reports that provide background information for this data release: Michel, A., Kean, J.W., Smith, J.B., Allstadt, K.E., Coe, J.A. (2019). Taking the pulse of debris flows: Extracting debris-flow dynamics from good vibrations in southern California and central Colorado. Debris-flow hazards mitigation: Mechanics, Monitoring, Modeling, and Assessment. http://dx.doi.org/10.25676/11124/173224 U.S. Geological Survey Landslide Hazards Program. (2020). Chalk Cliffs, Colorado. U.S Geological Survey, https://www.usgs.gov/natural-hazards/landslide-hazards/science/chalk-cliffs-colorado?qt-science_center_objects=0#qt-science_center_objects Kean, J.W., Smith, J.B., and Coe, J.A., 2020, Debris-flow monitoring data, Chalk Cliffs, Colorado, USA, 2014: U.S. Geological Survey data release, https://doi.org/10.5066/P9MUWDFN.
DOI: 10.5066/p94pjwsl
title: Debris-flow and Flood Video Files, Chalk Cliffs, Colorado, USA, 2016

description: Chalk Cliffs located 8 miles southwest of Buena Vista, Colorado, is one of the most active debris-flow areas in the state (U.S. Geological Survey). Three stations were set up at Chalk Cliffs which are located sequentially along a channel draining the 0.3 km2 study area. These stations are equipped with rain gauges, laser distance meters, and data loggers to record rainfall and stage data (Kean, et al., 2020). This data release includes videos of debris-flows and floods captured by high-definition cameras placed at three different locations, associated with the monitoring stations, along the study area at Chalk Cliffs during 2016. Near the Upper Station (Station 1) there are two cameras, one located at the bridge cross section at the channel (Bridge Camera) and another on the opposite side of the basin with a broad view of the channel (Wide-angle Camera). The third camera (Middle Camera) is located near Station 2. The attached figure "station_and_camera_locations.png" provides an overview figure with the location of the three cameras and three stations along the channel. The videos and their audio are used to record information on flow type and velocity of debris flows. They can also be used to interpret how the flow characteristics, such as sediment concentration, vary with time to compare to seismic observations. Video recording for all cameras is triggered using a rainfall threshold, derived from rainfall measurements from rain gauges (Michel et al., 2019). The complete videos for all the cameras are downloaded manually during site visits. More detailed information about the cameras and settings used can be found in the metadata file under "Process steps" in "Data quality information." During 2016, several debris flows and floods were captured by the three cameras. The videos can currently be viewed in the individual 'Child item' pages accessible below. The 'Child item' pages contain either flood or debris flow video files for each camera described above. The following citations relate to reports that provide background information for this data release: Michel, A., Kean, J.W., Smith, J.B., Allstadt, K.E., Coe, J.A. (2019). Taking the pulse of debris flows: Extracting debris-flow dynamics from good vibrations in southern California and central Colorado. Debris-flow hazards mitigation: Mechanics, Monitoring, Modeling, and Assessment. http://dx.doi.org/10.25676/11124/173224 U.S. Geological Survey Landslide Hazards Program. (2020). Chalk Cliffs, Colorado. U.S Geological Survey, https://www.usgs.gov/natural-hazards/landslide-hazards/science/chalk-cliffs-colorado?qt-science_center_objects=0#qt-science_center_objects Kean, J.W., Smith, J.B., and Coe, J.A., 2020, Debris-flow monitoring data, Chalk Cliffs, Colorado, USA, 2014: U.S. Geological Survey data release, https://doi.org/10.5066/P9MUWDFN
DOI: 10.5066/p91git04
title: Gridded estimates of postfire debris flow frequency and magnitude for southern California

description: This data release contains gridded estimates of postfire debris flow probability and magnitude for six different rainfall and wildfire scenarios in southern California. The scenarios represent the present and possible future precipitation and fire regimes for the region. The results are provided for 1 km2 cells across the study area. The data release accompanies the journal article Kean, J.W. and Staley, D.M. (2021). Forecasting the frequency and magnitude of postfire debris flow across southern California, Earth's Future, 2020EF001735.
DOI: 10.6084/m9.figshare.19341905.v1
title: HighSLA_Argo_interpolated

description: Interpolated argo data for high-SLA profiles
DOI: 10.5066/p9dbckt4
title: Inorganic Concentration Results for Assessment of Mixed Organic/Inorganic Chemical and Biological Exposures in North Dakota and South Dakota Tapwater, 2019

description: This data set reports results for inorganic constituents analyzed at the Redox Chemistry Laboratory in Boulder, Colorado, for the 2019 tapwater study conducted in North Dakota and South Dakota, USA. This project is part of the U.S. Geological Survey, Ecosystems Mission Area, Environmental Health Water and Wastewater Infrastructure Program.
DOI: 10.5281/zenodo.6345289
title: Magicicada cassinii decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6345288
title: Magicicada cassinii decontaminated FSCR

description: .
DOI: 10.5066/p9ou3u4p
title: Lidar data for gate release experiment at the USGS Debris-Flow Flume 24 and 25 May 2017

description: Lidar data was collected on 24 and 25 May 2017 at the USGS debris-flow flume to monitor two gate-release debris flow experiments. A static prism of sediment was emplaced behind a gate at the top of the flume. Water was added via sprinklers to the surface and also via pipes to the subsurface, in order to saturate the sediment mass. The sediment mass moved down the flume as a debris flow when the gate was opened. Lidar data were collected from a Riegl VZ-400 terrestrial laser scanner to capture the mass failure. The laser scanner was modified, so that rather than scanning in a 360 degree motion, as it is designed, it only scanned a narrow swath (approximately 1 mm) along the full profile of the constructed sediment mass. The lidar scan rate was set to re-scan at a rate of 60 Hz (~0.017s). The data in this data release represent entire lidar point cloud from the time before movement through full failure of the sediment mass. The points are organized in rows. The columns are: ['xM', 'yM', 'zM', 'rangeM', 'thetaDEG', 'phiDEG', 'riegl_ref', 'timestampSEC', 'Line'] . The 'xM', 'yM', 'zM' values are the x, y, and z real world UTM coordinates with units of meters. The 'rangeM' is the distance of each point from the scanner with units of meters. The vertical angle of the lidar scanner is shown as 'thetaDEG' with units of degrees. Similarly, the horizontal angle of the lidar scanner is shown as 'phiDEG' with units of degrees. The 'riegl_ref' represents reflectance value computed from the intensity. The 'timestampSEC' is the number of seconds since the beginning of the scan. The 'Line' column represents all the points that belong to a single lidar profile swath, that is, it is the integer line number corresponding to a single rotation of the scanner mirror.
DOI: 10.5281/zenodo.6103374
title: Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data

description: Supporting simulation code and data for the manuscript "Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data"
DOI: 10.5281/zenodo.6103373
title: Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data

description: Supporting simulation code and data for the manuscript "Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data"
DOI: 10.5066/p9qlp6xg
title: Soil moisture monitoring following the 2009 Station Fire, California, USA, 2016-2019

description: This data release includes 2016-2019 soil moisture timeseries for two drainage basins (&quot;Arroyo Seco&quot; and &quot;Dunsmore Canyon&quot;) that burned during the 2009 Station Fire in Los Angeles County, California, USA. The Arroyo Seco (0.01 km2) and Dunsmore Canyon (0.5 km2) drainages include two soil pits, one located near the drainage divide and another near the basin outlet. Following the naming convention established by Smith et al. (2019), we refer to the soil pits near the Arroyo Seco drainage divide and basin outlet as &quot;AS1&quot; and &quot;AS3,&quot; respectively. Similarly, we refer to the soil pits near the Dunsmore Canyon drainage divide and basin outlet as ?DC1? and ?DC3,? respectively. The coordinates of AS1 and AS3 are, respectively, 34.236972?, -118.195756? and 34.236053?, -118.194915? (WGS84). The coordinates of DC1 and DC3 are, respectively, 34.265075?, -118.237336? and 34.257528?, -118.241728? (WGS84). Each array (for which the total soil profile is ~50 cm) includes three soil moisture sensors positioned at 10, 25, and 50 cm depths. We report all available soil moisture information in terms of volumetric water content (VWC) for the time period and flag lapses in coverage. The VWC data for AS1 and DC1 overlap with Smith et al. (2019) for January 2016 through November 2017. We provide those data here to maintain consistency with Thomas et al. (2021). Sensor information, including the make/model, measurement units, and measurement frequency are contained in the &quot;README.txt&quot; file. Additional study details are provided in the following publications: Thomas, M.A., Rengers, F.K., Kean, J.W., McGuire, L.A., Staley, D.M., Barnhart, K.R., and Ebel, B.A. (2021). Postwildfire soil-hydraulic recovery and the persistence of debris flow hazards. Journal of Geophysical Research: Earth Surface. https://doi.org/10.1029/2021JF006091 Smith, J.B., Kean, J.W., Mirus, B.B., Staley, D.M, Rengers, F.K., and McGuire, L.A. (2019). Hillslope hydrologic monitoring data following the 2009 Station Fire, Los Angeles County, California, November 2015 to June 2017: U.S. Geological Survey data release. https://doi.org/10.5066/P98G0FS2 Smith, J.B., and J.W. Kean. (2018). Long-term soil-water tension measurements in semiarid environments: a method for automated tensiometer refilling. Vadose Zone Journal. https://doi.org/10.2136/vzj2018.04.0070
DOI: 10.5281/zenodo.6345229
title: Dataser from paper "Proliferation of osteoblast precursor cells on the surface of TiO2 nanowires anodically grown on a -type biomedical titanium alloy"

description: Dataser from paper "Proliferation of osteoblast precursor cells on the surface of TiO2 nanowires anodically grown on a -type biomedical titanium alloy": - <strong>Contact Angle: </strong>Images and measurements. <strong>- Fluorescence Microscopy Images:</strong> Images. <strong>- MTT and pixel counting:</strong> Measurements.
DOI: 10.5281/zenodo.6345228
title: Dataser from paper "Proliferation of osteoblast precursor cells on the surface of TiO2 nanowires anodically grown on a -type biomedical titanium alloy"

description: Dataser from paper "Proliferation of osteoblast precursor cells on the surface of TiO2 nanowires anodically grown on a -type biomedical titanium alloy": - <strong>Contact Angle: </strong>Images and measurements. <strong>- Fluorescence Microscopy Images:</strong> Images. <strong>- MTT and pixel counting:</strong> Measurements.
DOI: 10.5281/zenodo.6345338
title: Costarican Sign Language (LESCO) emergency-based signs dataset

description: This dataset was part of Juan Zamora-Mora's doctoral dissertation on the recognition of Costarican Sign Language (LESCO) in emergency situations from Aspen University. The dataset is composed of 39 signs. There are three videos for each sign on each folder. Videos have been cropped and are on average 1 second long. This dataset contains a total of mp4 117 videos.
description: each folder represents a sign (the folder name is the label). There are 3 different signers performing the same sign at each folder.
DOI: 10.5281/zenodo.5756386
title: Costarican Sign Language (LESCO) emergency-based signs dataset

description: This dataset was part of Juan Zamora-Mora's doctoral dissertation on the recognition of Costarican Sign Language (LESCO) in emergency situations from Aspen University. The dataset is composed of 39 signs. There are three videos for each sign on each folder. Videos have been cropped and are on average 1 second long. This dataset contains a total of mp4 117 videos.
description: each folder represents a sign (the folder name is the label). There are 3 different signers performing the same sign at each folder.
DOI: 10.6084/m9.figshare.19179140
title: Pantoea_Aeromonas_OneCodex

description: <b>Pathogenic <i>Enterobacteriaceae</i> require multiple culture temperatures for detection in <i>Cannabis sativa L</i>.</b>
DOI: 10.6084/m9.figshare.19179140.v1
title: Pantoea_Aeromonas_OneCodex

description: <b>Pathogenic <i>Enterobacteriaceae</i> require multiple culture temperatures for detection in <i>Cannabis sativa L</i>.</b>
DOI: 10.5067/7kurdo0sqe1r
title: OCO-2 Gridded bias-corrected XCO2 and other select fields aggregated as Level 4 daily files
DOI: 10.5067/a613ybqvphcd
title: Multi-Instrument Fused bias-corrected XCO2 and other select fields aggregated as Level 4 daily files
DOI: 10.5281/zenodo.6315338
title: Fluorogenic DNA-PAINT for faster, low-background super-resolution imaging

description: Data associated with publication.
DOI: 10.5281/zenodo.6315337
title: Fluorogenic DNA-PAINT for faster, low-background super-resolution imaging

description: Data associated with publication.
DOI: 10.5517/ccdc.csd.cc290ykv
title: CCDC 2116077: Experimental Crystal Structure Determination

description: Related Article: Xilong Leng, Dongliang Shi, Xiaoping Yang, Zhen Zhang, Hao Li, Yuebo Cheng, Yanan Ma, Desmond Schipper|2022|J.Mater.Chem.C|10|3510|doi:10.1039/D1TC05929G
DOI: 10.5517/ccdc.csd.cc2914bv
title: CCDC 2116256: Experimental Crystal Structure Determination

description: Related Article: Xilong Leng, Dongliang Shi, Xiaoping Yang, Zhen Zhang, Hao Li, Yuebo Cheng, Yanan Ma, Desmond Schipper|2022|J.Mater.Chem.C|10|3510|doi:10.1039/D1TC05929G
DOI: 10.5517/ccdc.csd.cc296g88
title: CCDC 2121369: Experimental Crystal Structure Determination

description: Related Article: Ji-Hua Zhang, Teng-Fei Xiao, Zi-Qin Ji, Han-Nan Chen, Pen-Ji Yan, Yong-Chun Luo, Peng-Fei Xu, Guo-Qiang Xu|2022|Chem.Commun.|58|2882|doi:10.1039/D1CC07189K
DOI: 10.5517/ccdc.csd.cc297yp6
title: CCDC 2122808: Experimental Crystal Structure Determination

description: Related Article: Tadeusz Lis, Monika Moszner|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc297yr8
title: CCDC 2122810: Experimental Crystal Structure Determination

description: Related Article: Tadeusz Lis, Tomasz Popek|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc297zr9
title: CCDC 2122841: Experimental Crystal Structure Determination

description: Related Article: yupeng guo|2021|CSD Communication|||
description: Related Article: Yupeng Guo, Shuaishuai Ding, Na Zhang, Zhuofan Xu, Shaofei Wu, Jinlian Hu, Qin Xiang, Zhao-Yang Li, Xing Chen, Sota Sato, Jishan Wu, Zhe Sun|2022|J.Am.Chem.Soc.|144|2095|doi:10.1021/jacs.1c12854
DOI: 10.5517/ccdc.csd.cc29809x
title: CCDC 2122858: Experimental Crystal Structure Determination

description: Related Article: Andrei S. Batsanov, Ian R. Baxendale, Antoine Legato|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2980p9
title: CCDC 2122870: Experimental Crystal Structure Determination

description: Related Article: jianning liao|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2980qb
title: CCDC 2122871: Experimental Crystal Structure Determination

description: Related Article: Andrei S. Batsanov, Ian R. Baxendale, Henry Gould|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2980zl
title: CCDC 2122879: Experimental Crystal Structure Determination

description: Related Article: Andrei S. Batsanov, Ian R. Baxendale, Guido Gambacorta|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc29813r
title: CCDC 2122883: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, F. Deubel, R. Meier|2021|CSD Communication|||
DOI: 10.13026/g3me-rt62
title: Pulse Transit Time PPG Dataset

description: We provide an open access, high resolution and time synchronised dataset from
multiple sensors worn at different body locations including Photoplethysmogram
(PPG), Inertial, Pressure and ECG. The recordings are from 22 healthy subjects
performing 3 physical activities. This dataset contains 66 waveform records
from multi-site and multi-wavelength PPGs, sensors' attachment pressures,
sensors' temperatures, inertial data from accelerometer and gyroscope, along
with annotated ECG data for a total of 19 channels. Additionally, systolic and
diastolic blood pressures, as well as blood oxygenation saturation levels
(SpO2) numerics are provided.
DOI: 10.25345/c5m902616
title: MassIVE MSV000089042 - pSCoPE: Prioritized single-cell proteomics
title: These raw files, search results, and additional files are associated with the pSCoPE publication by the Slavov Lab. The pSCoPE_raw_file_guide.xlsx file is a guide to the samples associated with each raw file. The pSCoPE_pubFileGuide.xlsx is a guide to all files in this dataset.
DOI: 10.17632/9ks39y34m7.1
title: CEO education

description: This data is used to study the effect of CEO education on firms' entry timing in merger waves.
DOI: 10.17632/48w2376x9j.1
title: INSINAS2019-01

description: This dataset consists of "Rate and Efficiency of Organic Carbon Assimilation by Aquacultured Sandfish Juvenile Holothuria scabra." The dataset supports that paper accordingly.

The growth rate of H. scabra, as shown by the wet and dry weight of the juveniles. The codes S, K, and T are used for juveniles from Sekotong, Kayangan, and Labuhan Treng pond, respectively. The codes X, Y, Z, and C are used for rice bran, cow manure, seagrass extract, and the control, respectively. We highlight the exponential growth rate of juveniles with the rice bran diet.

Fecal pellet (FP) egestion rate (column Ia) and the average of FP egestion rate based on treatment in dry weight of fecal pellet (Column Ib) and organic carbon (Column Ic). Column II shows the cumulative egestion of fecal pellets for three juvenile origins (Kayangan, Sekotong, and field pond, respectively). The codes S, K, and T are used for juveniles from Sekotong, Kayangan, and Labuhan Treng pond, respectively. The codes X, Y, Z, and C are used for rice bran, cow manure, seagrass extract, and the control, respectively.

Organic carbon assimilation rate and efficiency by H. scabra juveniles. The codes S, K, and T are used for juveniles from Sekotong, Kayangan, and Labuhan Treng pond, respectively. The codes X, Y, Z, and C are used for rice bran, cow manure, seagrass extract, and the control, respectively.

Assimilation rate and efficiency
The assimilation rate is calculated using the following formula:
Assimilation rate (mg C/d) = [((s*a)+(d*a)-(f*a))]/t
where a = dry weight of the ingested mixed sediment ≈ dry weight of excreted fecal pellets (in grams); d = organic diet material (in mg C g-1); s = organic material from sediment substrate (in mg C g-1); f = organic material of fecal pellets (in mg C g-1); and t = time (in days).
Assimilation efficiency is calculated using the following formula:
Assimilation efficiency (in %) = [((s*a)+(d*a)-(f*a))/((s*a)+(d*a))] * 100%
Where a = dry weight of ingested mixed sediment ≈ dry weight of excreted fecal pellets (in grams); d = organic diet material consumed per day (in mg C g-1.d-1); s = organic material from sediment substrate ingested per day (in mgC g-1.d-1); f = organic material of fecal pellets excreted per day (in mg C g-1. d-1).
DOI: 10.17632/pf7k48k7pr.1
title: ColitisNatHist

description: IL10ko colitis natural history. Raw data
DOI: 10.17632/r7b9shhx6p.1
title: The LC-QTOF-MS/MS analysis of acid degradation products of Rifaximin, An Antibiotic

description: The present research aims to propose a simple and accurate technique for the
analysis of Rifaximin in the presence of its stress degradation products and analysis of
degradation products by LC-MS/MS analysis. Rifaximin was submitted to forced
degradation under acid hydrolysis condition as prescribed by the ICH.The active
Rifaximin was complementary provided by Hilton Pharma Pvt Ltd, and the extract was
prepared by firstly treated with HCl and heated about 4 to 8 hours. The filtrate was
collected and separated using dichloromethane followed by evaporation in rotary
evaporator to obtain a solid crude extract which was then stored under refrigeration at -
80  0  C. Liquid chromatography quadrupole time of ﬂight mass spectrometry (LC-
QTOF-MS/MS) was utilized to identify products in the drug sample. The data
processing results revealed the presence of 9 products in the degraded sample of
Rifaximin. This data article contains the m/z [M+H + ] values, molecular formula,
retention times and the comprehensive list of m/z values detected during the LC-
QTOF- MS/MS analysis.
DOI: 10.5517/ccdc.csd.cc29814s
title: CCDC 2122884: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, F. Deubel, R. Meier|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc29815t
title: CCDC 2122885: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, F. Deubel, R. Meier|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2981f2
title: CCDC 2122893: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, R. Meier|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2981g3
title: CCDC 2122894: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, R. Meier|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2981h4
title: CCDC 2122895: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, R. Meier|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2981j5
title: CCDC 2122896: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, R. Meier|2021|CSD Communication|||