cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/79384be16d78e0d559b32d04ad567818
title: Marsh Turbidity in Mockhorn Island Marsh, VA 2017-2018

description: Turbidity was measured in Mockhorn Island Marsh on the Atlantic coast of the Delmarva Peninsula. Three RBR Duo sensors were used. One was located at the edge of the marsh, one 7.8 meters away from the edge station in the channel and one in the marsh interior. The calibration factor from converting between Nephelometric Turbidity Units (NTU) and suspended sediment concentration in milligrams per liter is 1.3232. NTU*1.3232=mg/L Data was recorded every 15 minutes, but NaN was used when the sensor was not underwater (and so could not measure turbidity).
DOI: 10.6073/pasta/d3a96e0db54f4c66899e82e6641ab42b
title: PIE LTER suspended sediments at Law's Point, West Creek, a tidal marsh and creek off the Rowley River, Rowley, MA, during 2016-2017.

description: Turbidity and calibrated suspended sediment concentration measurements were collected along a shore-normal transect from the channel in West Creek, Rowley, MA extending 24 meters into the marsh interior at Law's Point at the PIE LTER. Data was collected every 15 minutes over two different growing seasons during 2016 and 2017, totaling 9 months.
DOI: 10.6073/pasta/a886dc23d15dff38d0e31499d5a69f27
title: Turbidity of a Salt Marsh within the Chesapeake Bay, VA, USA, 2016-2017

description: We measured turbidity along a transect from channel to marsh interior from December 2015 to January 2017 at a salt mash at the mouth of the York River in the Chesapeake Bay, VA, USA. We measured turbidity (NTU) with three optical back scatter sensors (YSI EXO) to in a shore normal transect, with one in the channel and two on the marsh surface at 1m from the marsh edge and 12m from the marsh edge, respectively. The sensors measured every 15 minutes and were equipped with automatic wipers to reduce biofouling. Following retrieval, the turbidity time series data was filtered to remove any erroneous points and times when the sensors were fouled or exposed (Coleman et al. 2020). Turbidity was then calibrated to suspended sediment concentration with local sediment resulting in the calibration equation SSC (mg/L) =1.667*Sensor Turbidity (NTU)
DOI: 10.6073/pasta/f1c1e6b2fd092c50fbe46dbe98b516cd
title: Turbidity of a Salt Marsh within the Altamaha River estuary, GA, USA, 2015-2017

description: We measured turbidity along a transect from channel to marsh interior for 1 year, beginning in June 2016 at a salt mash in the Altamaha River estuary. We measured turbidity (NTU) with three optical back scatter sensors to in a shore normal transect, with one in the channel (YSI 6600), and two on the marsh surface. The “marsh edge sensor” was 2.4m from the channel edge (Seapoint, RBR Solo) and the “marsh interior sensor” was 18m from the edge (Seapoint, RBR Duo; Figure 1c). The sensors measured every 15 minutes and were equipped with automatic wipers to reduce biofouling. Following retrieval, the turbidity time series data was filtered to remove any erroneous points and times when the sensors were fouled or exposed (Ganju et al. 2005). Turbidity was then calibrated to suspended sediment concentration with local sediment resulting in the calibration equation SSC (mg/L) =1.33*Sensor Turbidity (NTU) (R2=0.9345, n=26, p&lt;&lt;0.001).
DOI: 10.6073/pasta/99d113534ecaa04e820850e6169be04d
title: Soil Organic Matter Mechanisms of Stabilization (SOMMOS) - enhanced soil characterization data from 40 National Ecological Observatory Network (NEON) sites

description: Soil organic matter (SOM) is a critical linkage among many ecosystem services that sustain our society and life on Earth. It is the primary energy source for microbes and the principal storehouse of water necessary for plant growth. SOM also stores nutrients for plants and sorbs pollutants that otherwise could contaminate food and water supplies. Soils also help regulate climate by storing carbon that would otherwise be released to the atmosphere and contribute to climate change. The SOMMOS project investigated processes in the soil that protect SOM from being decomposed by microbes, processes that increase its sensitivity to environmental changes, and how changes in climate and land management influence the amount and stability of SOM. The project, which was a collaboration between scientists from the National Ecological Observatory Network (NEON), University of Colorado, University of Michigan, Oregon State University, Virginia Polytechnic Institute and State University, and the USDA-Forest Service, took advantage of soil samples collected across NEON, a major NSF investment in environmental monitoring that covers the entire United States. This continental-scale soil sample set was analyzed for a wide array of physical and chemical properties, well beyond those typically measured on such a large-scale sample set, including radiocarbon, extractable metals, organic matter chemistry by pyrolysis-GCMS, liquid extract fluorescence spectroscopy, and more. In addition to this dataset, archived samples are available from the project for sharing with interested researchers.
DOI: 10.5281/zenodo.5829534
title: DMSP F15/F16/F18 2011-2014 Binned Poynting Flux Data

description: This dataset, created using the esabin Python library, comprises 9 spacecraft-years of electrodynamics measurements from Defense Meteorology Satellite Program F15, F16 and F18 spacecraft. <strong>Users of this data should prefer files which do not contain 'idm_only' in their filenames, as these do not use both components of DMSP ion drift vector measurements.</strong> They are included here to support reproducibility of a upcoming publication. The HDF5 files herein organize this data in equal area bins in magnetic coordinates. Each Group in the files represents one bin. Each Dataset contains the data for one crossing of that bin by a DMSP spacecraft. The name of each Dataset is the approximate time of the crossing as Julian date. Hourly NASA OMNIWeb solar wind and geomagnetic activity parameters are included as attributes for each Dataset. The electrodynamic parameters herein are magnetic perturbation (dB), electric field (E) and ion drift velocity (V). These quantities are scaled from satellite altitude (~850 km) where they were observed to ionospheric altitude (~110 km) using Modified Magnetic Apex coordinates. In the filenames, 'e' represents magnetic eastward and 'n' represents magnetic northward. Also included are geomagnetic-main-field-aligned Poynting flux also scaled to 110km altitude (files with 'poynting' in the name).
DOI: 10.14291/tccon.ggg2020.solar.r0
title: Solar Line List for the TCCON 2020 Data Release 

description: The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. This is the Solar Line List for the TCCON GGG 2020 Data Release.
DOI: 10.14291/tccon.ggg2020.atm.r0
title: Atmospheric Voigt Line List for the TCCON 2020 Data Release 

description: The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. This is the atmospheric line list for lines with Voigt lineshapes for the TCCON GGG2020 data release.
DOI: 10.14291/tccon.ggg2020.atmnv.r0
title: Atmospheric Non-Voigt Line List for the TCCON 2020 Data Release

description: The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. This is the atmospheric line list for lines with non-Voigt lineshapes for the TCCON GGG2020 data release.
DOI: 10.17632/tzdrr7bsff.1
title: Data for: Expression of Early Cretaceous Global anoxic events in northeastern Brazilian basins

description: Data used in the manuscript.
DOI: 10.17632/f23zhjcwcv.1
title: Invariance of the trait emotional intelligence construct across clinical populations and sociodemographic variables

description: Pooled dataset for measurement invariance across clinical datasets
DOI: 10.17632/cx7ww42dc8.1
title: Data for: Evolution characteristics of the upper Paleozoic source kitchen and its controlling effects on hydrocarbon accumulation in the Paleozoic petroleum system in Huanghua Depression, Bohai Bay Basin, China

description: the data that I uploaded was the key material used for studying hydrocarbon expulsion and accumulation
DOI: 10.17632/r6t9tmzzmz.1
title: KAMC_COVID-19

description: We retrospectively collected data of admitted confirmed COVID-19 patients (positive RT-PCR test) to King Abdulaziz Medical City in Riyadh. Starting from the first case, which was on April 2, 2020, till June 18, 2020. We retrieved 5,739 patients records from the EHR system. However,  1513 COVID-19 patients passed the criteria set to be included for mortality endpoint, and 1508 patients passed the inclusion criteria for the ventilatory support requirement endpoint. For ventilatory support requirement, any patient that is lacking one of the following are excluded: 1) confirmed RT-PCR for COVID-19, 2) age >18, 3) CXR, 4) CBC test results, 5) availability of medical history, and 6) Full code status at the time of COVID-19 diagnosis. For mortality inclusion criteria is same as ventilatory support endpoint except that COVID-19 patients who are assigned no-code before the SARS-CoV-2 test are also included. The data include the 36 variables and we below describe how it is obtained and:
1) Gender
2) Age at diagnosis
3) Ventilation support status
4) Vital status
5) Haematocrit
6) Hemoglobin
7) MCHC
8) MCH
9) MCV
10) MPV
11) RBC
12) Platelet_count
13) RDW
14) WBC: 
15) CXR zone 1
16) CXR zone 2
17) CXR zone 3
18) CXR zone 4
19) CXR zone 5
20) CXR zone 6
21) CXR zone 7
22) CXR zone 8
23) CXR zone 9
24) CXR zone 10
25) CXR zone 11
26) CXR zone 12
27) cancer_ICD-10_C00-C97
28) chronic_ischaemic_heart_disease_coronary_artery_disease_ICD-10_I25_I250-I259
29) hypertension_ICD-10_I10
30) asthma_ICD-10_J45_J450-J459
31) chronic_obstructive_pulmonary_disease_ICD-10_J44_J440-J449
32) type_2_diabetes_mellitus_ICD-10_E11
33) liver_cirrhosis_ICD-10_K74_K740-K746
34) chronic_viral_hepatitis_B_ICD-10_B181
35) chronic_viral_hepatitis_C_ICD-10_B182
36) chronic_kidney_disease_all_stages_ICD-10N18_N181-N189

Variable 1 is obtained from EHR. Variable 2 obtained from the EHR system as date of birth and converted to age using Covid-19 test date. Variable 3 is filled manually by a physician from the Infectious Disease Department at KAMC, the value MV assigned for mechanical ventilation, NIV for noninvasive ventilation. MV label is assigned if the patient received MV within 30 days of diagnosis. Variable 4:  Filled manually by looking at EHR system discharge charts. If the patient is not discharge after 30 days of follow-up and the patient is still alive. The vital status is then reported as Alive. Variables 5-14 are obtained from CBC test results  (within 3 days of DX). Variables 15-26, are annotated for CXR severity by four radiologists at KAMC.  The images are baseline (within 3 days of DX). The radiologist are blinded to the outcome (mortality, or ventilator support requirement). A value of 0 is assigned for no presence of ground glass opacity (GGO) , a value of 1 for moderate GGO, and a value of 2 for severe manifestation of GGO (see included CXR figure for CXR zones). Variables 27-36 are obtained from the EHR system a value of 0 for disease absence and 1 for disease presence.
DOI: 10.17632/553ky673ms.2
title: Phylogenetic and phenotypic relationships in the subcomplex Triatoma sordida (Hemiptera: Reduviidae: Triatominae)”

description: Belintani, Tiago (2019), “Phylogenetic and phenotypic relationships in the subcomplex Triatoma sordida (Hemiptera: Reduviidae: Triatominae)”, Mendeley Data, V1
DOI: 10.6084/m9.figshare.18461567
title: December 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>December, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.18461567.v1
title: December 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>December, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.5517/ccdc.csd.cc27lmk1
title: CCDC 2073483: Experimental Crystal Structure Determination

description: Related Article: Zhen Cheng, Wei Yang, Qing Ma, Zhen-Qi Zhang, Lei Yang, Hongwei Yang, Gui-Juan Fan, Guangbin Cheng|2021|Cryst.Growth Des.|22|334|doi:10.1021/acs.cgd.1c00958
DOI: 10.5517/ccdc.csd.cc2842p6
title: CCDC 2089297: Experimental Crystal Structure Determination

description: Related Article: Zhen Cheng, Wei Yang, Qing Ma, Zhen-Qi Zhang, Lei Yang, Hongwei Yang, Gui-Juan Fan, Guangbin Cheng|2021|Cryst.Growth Des.|22|334|doi:10.1021/acs.cgd.1c00958
DOI: 10.34740/kaggle/dsv/3045102
title: Omicron daily cases by country (COVID-19 variant)
DOI: 10.5281/zenodo.5851794
title: Data of Takei (2022)

description: Experimental data of Takei (2022, JGR-solid Earth) Details are written in Readme.pdf
DOI: 10.5281/zenodo.5851793
title: Data of Takei (2022)

description: Experimental data of Takei (2022, JGR-solid Earth) Details are written in Readme.pdf
DOI: 10.6084/m9.figshare.18218120.v2
title: Dataset of "Growth of the Tian Shan drives migration of the conglomerate-sandstone transition in the southern Junggar foreland basin"

description: The dataset includes the original seismic data presented in the manuscript entitled "Growth of the Tian Shan drives migration of the conglomerate-sandstone transition in the southern Junggar foreland basin"(# 2021GL097545) and the python code for linear regression analysis and lowess smooth in the manuscript. The manuscript are submited to Geophysical Research Letters on December 12th, 2021.
DOI: 10.6084/m9.figshare.18218120.v1
title: Dataset of "Growth of the Tian Shan drives migration of the conglomerate-sandstone transition in the southern Junggar foreland basin"

description: The dataset includes the original seismic data presented in the manuscript entitled "Growth of the Tian Shan drives migration of the conglomerate-sandstone transition in the southern Junggar foreland basin"(# 2021GL097545) and the python code for linear regression analysis and lowess smooth in the manuscript. The manuscript are submited to Geophysical Research Letters on December 12th, 2021.
DOI: 10.5281/zenodo.5851847
title: Reddit WSB Annotated Dataset 2021

description: This is a WIP randomized sample of comments from the Wallstreetbets community on Reddit during the GameStop event during the 2021 rise. The sample contains 5000 observations of which 3000 were annotated and agreed upon by two annotators. The next 600 were annotated by two authors but due to time constraints, only 1 author corrected them. The remaining 1400 have only been annotated by one author and have not been compared. The second file is the annotation ruleset used to annotate the dataset, we briefly summarize the rules here: Annotations are broken into two main categories, support (the comment indicates some level of support for either the company GameStop, the stock price, or the narrative of 'us' vs 'them'.). Support can be either Y= Yes, N= No, U= Unsure, I= Informative. The second category, 'intent' indicates the individual has expressed intentions or interest in the stock, or has already purchased the stock during the event period. Intent can be either Y= Yes, N= No, M= Maybe, U= Unsure, or I= Informative.
DOI: 10.5281/zenodo.5851686
title: Reddit WSB Annotated Dataset 2021

description: This is a WIP randomized sample of comments from the Wallstreetbets community on Reddit during the GameStop event during the 2021 rise. The sample contains 5000 observations of which 3000 were annotated and agreed upon by two annotators. The next 600 were annotated by two authors but due to time constraints, only 1 author corrected them. The remaining 1400 have only been annotated by one author and have not been compared. The second file is the annotation ruleset used to annotate the dataset, we briefly summarize the rules here: Annotations are broken into two main categories, support (the comment indicates some level of support for either the company GameStop, the stock price, or the narrative of 'us' vs 'them'.). Support can be either Y= Yes, N= No, U= Unsure, I= Informative. The second category, 'intent' indicates the individual has expressed intentions or interest in the stock, or has already purchased the stock during the event period. Intent can be either Y= Yes, N= No, M= Maybe, U= Unsure, or I= Informative.
DOI: 10.5281/zenodo.5851857
title: Data

description: None
DOI: 10.5281/zenodo.5256189
title: Data

description: None
DOI: 10.6084/m9.figshare.17710916.v2
title: Supplementary materials.pdf

description: <b>Observational studies have suggested associations between adipokines and cardiovascular disease (CVD), but the roles of certain adipokines remain controversial, and these associations have not yet been ascertained causally. This study aims</b><b> to investigate whether circulating adipokines causally affect the risk of CVD using two-sample Mendelian randomization (MR).</b><br>
DOI: 10.4121/18274532.v1
title: Code and data of research project: The effect of tempo transformations on Essentia’s beat tracking pipelines

description: Code and data of the research project, CSE3000, at TU Delft.Includes music samples used for the research, collected beat annotations and calculated beats.
DOI: 10.4121/18274532
title: Code and data of research project: The effect of tempo transformations on Essentia’s beat tracking pipelines

description: Code and data of the research project, CSE3000, at TU Delft.Includes music samples used for the research, collected beat annotations and calculated beats.
DOI: 10.4121/16860145.v1
title: Experimental data of deposition patterns around buildings at the beach: the effect of building spacing and orientation

description: Experimental data of scale experiments performed at the beach to determine the effect of building configurations on the deposition patterns around buildings. <br><br>Experiments were performed with scale models of 0.5x1x0.5m (WxLxH), placed at the beach for approximately 1 day. Aspects examined are:- Building orientation to the wind- Building spacing- Combinations of building orientation and spacing- Effect of pile heigt for buildings on piling- Effect of building group size<br>Deposition and erosion around buildings are measured using structure-from motion (SfM) photogrammetry. Data contains:- Raw photos used for SfM photogrammetry- Photogrammetry files- Digital elavation models (DEMs) and orthophotos of the deposition and erosion around scale models.- Time lapses of the the experiments- Wind speed and direction measurements- The length of the downwind deposition tails for the experiment on the effect of the building orientation to the wind<br>
DOI: 10.4121/16860145.v2
title: Experimental data of deposition patterns around buildings at the beach: the effect of building spacing and orientation

description: Experimental data of scale experiments performed at the beach to determine the effect of building configurations on the deposition patterns around buildings. <br><br>Experiments were performed with scale models of 0.5x1x0.5m (WxLxH), placed at the beach for approximately 1 day. Aspects examined are:- Building orientation to the wind- Building spacing- Combinations of building orientation and spacing- Effect of pile heigt for buildings on piling- Effect of building group size<br>Deposition and erosion around buildings are measured using structure-from motion (SfM) photogrammetry. Data contains:- Raw photos used for SfM photogrammetry- Photogrammetry files- Digital elavation models (DEMs) and orthophotos of the deposition and erosion around scale models.- Time lapses of the the experiments- Wind speed and direction measurements- The length of the downwind deposition tails for the experiment on the effect of the building orientation to the wind<br>
DOI: 10.4121/16860145
title: Experimental data of Deposition patterns around buildings at the beach: effects of building spacing and orientation

description: Experimental data of scale experiments performed at the beach to determine the effect of building configurations on the deposition patterns around buildings. <br><br>Experiments were performed with scale models of 0.5x1x0.5m (WxLxH), placed at the beach for approximately 1 day. Aspects examined are:- Building orientation to the wind- Building spacing- Combinations of building orientation and spacing- Effect of pile heigt for buildings on piling- Effect of building group size<br>Deposition and erosion around buildings are measured using structure-from motion (SfM) photogrammetry. Data contains:- Raw photos used for SfM photogrammetry- Photogrammetry files- Digital elavation models (DEMs) and orthophotos of the deposition and erosion around scale models.- Time lapses of the the experiments- Wind speed and direction measurements- The length of the downwind deposition tails for the experiment on the effect of the building orientation to the wind<br>