cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6473858
title: Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01

description: This repository contains the following data sets related to Detecting Plumes in Mobile Air Quality Monitoring Time Series with DBSCAN published in . Please cite the following: . Validated_Data.csv: A .csv file containing the validation set used in the study. Column headings are the following: "Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been manually flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day. Labeled_DBSCAN_Anomalies.csv: A .csv file containing points labeled as anomalies by the DBSCAN algorithm described in the manuscript. Columns are the following. "BC": Black carbon measurement (ng/m^3)<br> "CO2": Carbon dioxide measurement (ppm)<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2 (ppb)<br> "UFP": Ultrafine particle count (p/cc)<br> "Anomaly": Whether the DBSCAN algorithm has labeled this point as "Anomaly" (2) or "Normal" (1)<br> "Uniq_Fac": Factor spanning from 1-277 grouping measurements taken on separate days by car. E.g. all measurements with Uniq_Fac=1 were grouped and analyzed together.<br> "LST": Timestamp (US/Central)<br> "Road_Class": TigerLINE census road class designation for the given point. Possible road classes are S1100 - Primary Road, S1200 - Secondary Road, S1400 - Local Road, S1630 - Ramps, S1640 - Service Drives, S1730 - Private Roads<br> "X": Universal Transverse Mercator Easting for Zone 15N (m).<br> "Y": Universal Transverse Mercator Northing for Zone 15N (m). *_To_Be_Validated.csv: A series of files where * denotes the following. "DB": DBSCAN Algorithm<br> "QOR": QOR Algorithm<br> "QAND": QAND Algorithm<br> "Drew": Drewnick Algorithm Each file contains the following columns: "Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.
DOI: 10.5281/zenodo.8071136
title: DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction (Supplementary Data)

description: This dataset contains supplementary replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". In particular, it contains a new version of our `final_raw_dips.tar.gz` protein pair representations which now contain (1) residue-level annotations for intrinsic disorder regions (IDRs) as well as (2) a copy of each protein pair representation in the HDF5 file format for programming language-agnostic read capabilities. In addition, this record also contains raw MSAs (in HDF5 file format) generated for each protein pair using Jackhmmer and AlphaFold's small version of the Big Fantastic Database (BFD).
description: The primary DIPS-Plus dataset can be updated periodically using the instructions contained in our GitHub repository for DIPS-Plus (https://github.com/BioinfoMachineLearning/DIPS-Plus). For data provenance, the complexes curated for DIPS-Plus originate from the RCSB's bound protein complex repository (https://ftp.wwpdb.org/pub/pdb/data/biounit/coordinates/divided/).
DOI: 10.21227/r5zc-7y90
title: Simulation results for "smooth water surface - cloudy atmosphere" system

description: A computational experiment has been performed in order to evaluate systematic errors of atmospheric Total Water Vapor (TWV) and integral Liquid Water Content (LWC) microwave radiometric retrieval from satellites by means of dual-frequency method (inverse problem).  The errors under consideration may arise due to the non-linearity of brightness temperature level on true liquid water and effective cloud temperature dependencies and due to neglecting the spatial distribution of cumulus clouds in the satellite microwave radiometer antenna field-of-view (FOV). Comprehensive evaluation of these errors requires sequential forward and then inverse problem solving in different variations for numerous of possible cloudy atmosphere states (3D), satellite locations and observation angles, various FOV sizes, used frequency channel pairs, etc. It is clear that bruteforce approach would be purblind and short-sighted here. On the other hand, it turns out that cumuli are not fully randomly distributed in the atmosphere, but their number density and power distribution is subject to quite understandable statistics (see, e.g. Plank, 1969).In this dataset, we constrain ourselfs to considering standard atmosphere 3D-cells (i.e. altitude profiles of thermodynamic temperature, pressure and air humidity are standard), but additionally filled with cumuli distributed according to several represantative cases taken from (Plank, 1969), see table 3. A number of modifications for these cases are also considered. Herewith, the shape of clouds is assumed to be cylindrical. The size of the cell is set to 50x50x10 km (Ox x Oy x Oz). An introduced computational grid has 300x300x100 nodes.  A smooth water surface with temperature of 15 deg, Cels and zero salinity is considered as an underlying one. An observation angle is taken to be 0 deg. from zenith. Satellite FOV sizes are 1x1 and 60x60 nodes. The altitude profile of liquid water inside a cloud is approximated according to (Mazin, 1983). The effective cloud temperature parameter used in the course of TWV and LWC retrieval is chosen constant and equal to 0 deg. Cels. All the other parameters necessary while retrieval process are either known in advance or determined from the results of solving the forward problem in case of clear sky. The retrieval process is carried out for three frequency pairs: 22.2 and 27.2 GHz, 22.2 and 36 GHz, 22.2 and 89 GHz.The dataset is generated by this script and using "armrad" framework (Python + TensorFlow GPU).
DOI: 10.34943/01a507da-f4cd-45fe-80ef-c0bf2962efab
title: Main Endeavour Field Video Camera Deployed 2021-08-23

description: The SubC Imaging 1Cam Mk6 HDC SN 17692 was deployed on 2021-08-23 at Main Endeavour Field. Main Endeavour Field is a vent field located at Endeavour Ridge, where hydrothermal vent systems are observed. This device is a Video Camera. Video cameras record video of characteristics of the surrounding environments and can be deployed on fixed and mobile platforms. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34943/a9ca0520-1320-400f-b592-c2438fc4ed20
title: Main Endeavour Field Video Camera Deployed 2021-08-23

description: The SubC Imaging 1Cam Mk6 HDC SN 17692 was deployed on 2021-08-23 at Main Endeavour Field. Main Endeavour Field is a vent field located at Endeavour Ridge, where hydrothermal vent systems are observed. This device is a Video Camera. Video cameras record video of characteristics of the surrounding environments and can be deployed on fixed and mobile platforms. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 2.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.34740/kaggle/dsv/6126009
title: Sneaker 899-Classification total-50,000-Images
DOI: 10.25345/c5xd0r765
title: MassIVE MSV000092425 - Gentian disaccharide and laminobu disaccharide standards
title: This is the spectrum of the gentian disaccharide and laminobu disaccharide standards (PGC-MS/MS)
DOI: 10.5281/zenodo.8140981
title: DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction (Supplementary Data)

description: This dataset contains supplementary replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". In particular, it contains a new version of our `final_raw_dips.tar.gz` protein pair representations which now contain (1) residue-level annotations for intrinsic disorder regions (IDRs) as well as (2) a copy of each protein pair representation in the HDF5 file format for programming language-agnostic read capabilities. In addition, this record also contains (3) raw MSAs (in HDF5 file format) generated for each protein pair using Jackhmmer and AlphaFold's small version of the Big Fantastic Database (BFD). Lastly, this record contains (4) PDB metadata derived for each DIPS-Plus complex using Graphein's PDBManager API as well as (5) structure-based (i.e., FoldSeek-based) training and validation splits of the dataset's complexes in the form of respective text files containing the file paths of complexes assigned to each split.
description: The primary DIPS-Plus dataset can be updated periodically using the instructions contained in our GitHub repository for DIPS-Plus (https://github.com/BioinfoMachineLearning/DIPS-Plus). For data provenance, the complexes curated for DIPS-Plus originate from the RCSB's bound protein complex repository (https://ftp.wwpdb.org/pub/pdb/data/biounit/coordinates/divided/).
DOI: 10.5281/zenodo.8071135
title: DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction (Supplementary Data)

description: This dataset contains supplementary replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". In particular, it contains a new version of our `final_raw_dips.tar.gz` protein pair representations which now contain (1) residue-level annotations for intrinsic disorder regions (IDRs) as well as (2) a copy of each protein pair representation in the HDF5 file format for programming language-agnostic read capabilities. In addition, this record also contains (3) raw MSAs (in HDF5 file format) generated for each protein pair using Jackhmmer and AlphaFold's small version of the Big Fantastic Database (BFD). Lastly, this record contains (4) PDB metadata derived for each DIPS-Plus complex using Graphein's PDBManager API as well as (5) structure-based (i.e., FoldSeek-based) training and validation splits of the dataset's complexes in the form of respective text files containing the file paths of complexes assigned to each split.
description: The primary DIPS-Plus dataset can be updated periodically using the instructions contained in our GitHub repository for DIPS-Plus (https://github.com/BioinfoMachineLearning/DIPS-Plus). For data provenance, the complexes curated for DIPS-Plus originate from the RCSB's bound protein complex repository (https://ftp.wwpdb.org/pub/pdb/data/biounit/coordinates/divided/).
DOI: 10.13139/ornlnccs/1988139
title: Structural Models and Sequence Alignment Results of the Desulfovibrio vulgaris Proteome

description: This dataset contains the structural models for the primary transcripts of the Desulfovibrio vulgaris proteome as well as sequence alignment results for a subset of the encoded proteins. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the D. vulgaris proteome to those available in the AlphaFold Protein Structure Database (AFDB). This is a bit more complicated since the proteins reporting in the AFDB originate from an outdated form of the D. vulgaris sequence. The different versions of the D. vulgaris gene annotation are collected in the Chronology subdirectory; further consideration of these changes on the structural space of the proteome are currently underway. For proteins that have been annotated as "hypothetical", sequence alignment results from the HHblits and SAdLSA alignment methods are provided. These methods are often more capable to resolve sequence homology than other methods. Therefore, the results from both HHblits and SAdLSA are provided to identify possible homologs for these challenging proteins. Numerous sequence databases are utilized for these alignments.
DOI: 10.13139/ornlnccs/1988165
title: Structural Models of the Rhodopseudomonas palustris Proteome

description: This dataset contains the structural models for the primary transcripts of the Rhodopseudomonas palustris proteome. For each protein, the five models inferred from AlphaFold 2 are provided. The largest pTM-scoring model for each protein was energy minimized; this minimized structure as well as its AlphaFold pickle output file are also provided. This set of structures represent an alternate source of models for the R. palustris proteome to those available in the AlphaFold Protein Structure Database.
DOI: 10.5439/1989094
title: mmcrmode6p1200408121cloth (c1)

description: mmcrmode6p1200408121cloth
DOI: 10.5439/1989095
title: mmcrmode5p0200408121cloth (c1)

description: mmcrmode5p0200408121cloth
DOI: 10.5439/1989096
title: mmcrmode4pr200408121cloth (c1)

description: mmcrmode4pr200408121cloth
DOI: 10.5439/1989097
title: mmcrmode3ge200408121cloth (c1)

description: mmcrmode3ge200408121cloth
DOI: 10.5439/1989098
title: mmcrmode2ci200408121cloth (c1)

description: mmcrmode2ci200408121cloth
DOI: 10.5439/1989099
title: mmcrmode1bl200408121cloth (c1)

description: mmcrmode1bl200408121cloth
DOI: 10.5439/1989100
title: mmcrmode4ro200404141cloth (c1)

description: mmcrmode4ro200404141cloth
DOI: 10.5439/1989101
title: mmcrmode2ci200404141cloth (c1)

description: mmcrmode2ci200404141cloth
DOI: 10.5439/1989102
title: mmcrmode3ge200404141cloth (c1)

description: mmcrmode3ge200404141cloth
DOI: 10.5439/1989103
title: mmcrmode1st200404151cloth (c1)

description: mmcrmode1st200404151cloth
DOI: 10.15121/1989203
title: Dataset for report: Non-Technical Barriers to Geothermal Development in California and Nevada

description: In California and Nevada, geothermal projects are subject to non-technical barriers, which may create development delays leading to higher project costs and risks and decreased competitiveness with other electricity generation technologies. These non-technical barriers may include federal and state permits, authorizations, environmental reviews, and other regulatory requirements that are applicable throughout different phases of geothermal project development. The 2022 National Renewable Energy Laboratory (NREL) report, "Non-Technical Barriers to Geothermal Development in California and Nevada" presents findings of a study on non-technical barriers that may influence geothermal project development. The set of data resources relied upon for the report includes: 1) interviews conducted with relevant geothermal stakeholders including regulators and project developers, 2) federal and state environmental review documents developed for specific projects, 3) a techno-economic analysis conducted using the NREL Annual Technology Baseline (ATB) framework, and 4) an analysis of the impacts of site-specific land access and permitting considerations on project development readiness conducted using the Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT) Socioeconomic Assessment Tool (SEAT).
DOI: 10.6084/m9.figshare.23671839
title: Drip lines at finite temperature

description: The dataset contains the nuclear drip lines (limits of nuclear stability) at finite temperatures. Calculations are performed with 7 relativistic energy density functionals including DD-ME2, DD-PC1, DD-PCX, and those constrained to specific symmetry energy at saturation density, DD-PCJ30, DD-PCJ32, DD-PCJ34, and DD-PCJ36. The calculations are performed for temperatures T = 0, 0.5, 1, 1.5, and 2 MeV for all functionals, with additional points T = 0.8, 1.2, and 1.8 for DD-ME2, DD-PC1, and DD-PCX. <br> The data is stored in 44 text (.txt) files corresponding to different functionals and temperatures. The name of each data file consists of two parts: (i) the functional used for calculation, and (ii) temperature. For instance, the file "DDME2_T15.txt" corresponds to calculations with DD-ME2 functional at T = 1.5 MeV (we removed the decimal point from the temperature to avoid confusion with the data format). The data consists of 4 columns, where the first column is the temperature (T) in MeV, the second is the proton number (Z), the third neutron number for the isotope at the proton drip line, and the fourth neutron number of the isotope at the neutron drip line. This information is written in the header of each file as: <br> # Temp [MeV] Z(proton number) N(proton drip neutron number) N(neutron drip neutron number) <br> Using the provided information one can easily plot the drip lines as a function of temperature for different functionals.
DOI: 10.6084/m9.figshare.23671941.v1
title: Fig.1 data.xls These data were used in " <strong>Putative neural mechanisms underlying release-mode-specific abnormalities in dopamine neural activity in a schizophrenia-like model: the distinct roles of glutamate and serotonin in the impaired regulations of dopamine neurons" publised in The European Journal of Neuroscience</strong>

description: The author confirms that the data supporting the findings of this study .
DOI: 10.6084/m9.figshare.23666796.v1
title: Untitled Item

description: A 1-12 month scale NSPEI dataset based on observations from meteorological stations in China
DOI: 10.5281/zenodo.8140900
title: Swift/BAT-GUANO upper limits fits file for IceCube-230708A

description: Swift/BAT-GUANO upper limits fits file for IceCube-230708A
DOI: 10.5281/zenodo.8140899
title: Swift/BAT-GUANO upper limits fits file for IceCube-230708A

description: Swift/BAT-GUANO upper limits fits file for IceCube-230708A
DOI: 10.5281/zenodo.8141107
title: Current velocity data from a continuous survey using a towed ADCP in Doboy Sound, Georgia, USA

description: <strong>Title: </strong>Current velocity data from a continuous survey using a towed ADCP in Doboy Sound, Georgia, USA <strong>Author/Data Collector: </strong>Chunyan Li <strong>Point of Contact, PI, Originator: </strong>Chunyan Li (cli@lsu.edu) <strong>Description:</strong> These are velocity profile data from vessel mounted ADCP obtained in the Doboy Sound during a survey conducted on Mar. 26, 2002, a 12.5-hour continuous survey. The instrument was an RDI 600 KHz Workhorse ADCP. The data presented here are in ASCII with the generic format provided by the RDI’s software WinRiver II output. There is one data file: Doboy_Mar26000_ASC.TXT Here is an example of the data – 50 50 213 30 12 50 1 2 3 25 17 53 58 83 96 3 -1.897 0.760 105.297 18.920 1.99 0.12 -0.17 0.10 0.00 16.00 0.00 18.66 9.13 9.28 9.03 9.37 0.92 24.80 -0.10 0.77 0.77 31.39938333 -81.29868333 6.34 0.00 1.6 -1.4 -0.9 -0.2 -0.0 0.0 -0.0 0.0 3.24 7.74 30 cm BT dB 0.44 0.086 3.24 39.55 347.44 -8.6 38.6 3.3 -7.8 77.1 83.5 80.2 82.1 100 -0.07 3.74 38.34 346.75 -8.8 37.3 3.4 -0.4 80.8 87.8 83.8 85.7 100 -0.10 4.24 41.47 343.01 -12.1 39.7 3.1 0.3 82.4 89.3 85.3 86.9 100 -0.11 4.74 34.01 341.50 -10.8 32.2 2.0 2.8 83.4 89.8 86.1 87.4 100 -0.10 5.24 42.40 352.00 -5.9 42.0 1.9 -8.1 84.0 89.9 87.2 87.6 100 -0.12 5.74 33.01 342.17 -10.1 31.4 2.6 0.7 84.5 90.3 87.5 88.0 100 -0.09 6.24 36.56 340.23 -12.4 34.4 1.7 -2.4 84.8 90.5 87.7 88.7 100 -0.09 6.74 37.87 347.51 -8.2 37.0 2.4 -4.3 85.1 91.5 88.5 89.3 100 -0.09 7.24 33.31 344.43 -8.9 32.1 1.4 -0.7 85.9 92.0 89.4 90.0 100 -0.08 7.74 27.63 342.97 -8.1 26.4 -1.0 1.7 86.1 92.3 89.7 90.1 99 -0.05 8.24 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647 <strong>Acknowledgements</strong> The data were collected on R/V Savannah funded by NSF-LTER.
DOI: 10.5281/zenodo.8141106
title: Current velocity data from a continuous survey using a towed ADCP in Doboy Sound, Georgia, USA

description: <strong>Title: </strong>Current velocity data from a continuous survey using a towed ADCP in Doboy Sound, Georgia, USA <strong>Author/Data Collector: </strong>Chunyan Li <strong>Point of Contact, PI, Originator: </strong>Chunyan Li (cli@lsu.edu) <strong>Description:</strong> These are velocity profile data from vessel mounted ADCP obtained in the Doboy Sound during a survey conducted on Mar. 26, 2002, a 12.5-hour continuous survey. The instrument was an RDI 600 KHz Workhorse ADCP. The data presented here are in ASCII with the generic format provided by the RDI’s software WinRiver II output. There is one data file: Doboy_Mar26000_ASC.TXT Here is an example of the data – 50 50 213 30 12 50 1 2 3 25 17 53 58 83 96 3 -1.897 0.760 105.297 18.920 1.99 0.12 -0.17 0.10 0.00 16.00 0.00 18.66 9.13 9.28 9.03 9.37 0.92 24.80 -0.10 0.77 0.77 31.39938333 -81.29868333 6.34 0.00 1.6 -1.4 -0.9 -0.2 -0.0 0.0 -0.0 0.0 3.24 7.74 30 cm BT dB 0.44 0.086 3.24 39.55 347.44 -8.6 38.6 3.3 -7.8 77.1 83.5 80.2 82.1 100 -0.07 3.74 38.34 346.75 -8.8 37.3 3.4 -0.4 80.8 87.8 83.8 85.7 100 -0.10 4.24 41.47 343.01 -12.1 39.7 3.1 0.3 82.4 89.3 85.3 86.9 100 -0.11 4.74 34.01 341.50 -10.8 32.2 2.0 2.8 83.4 89.8 86.1 87.4 100 -0.10 5.24 42.40 352.00 -5.9 42.0 1.9 -8.1 84.0 89.9 87.2 87.6 100 -0.12 5.74 33.01 342.17 -10.1 31.4 2.6 0.7 84.5 90.3 87.5 88.0 100 -0.09 6.24 36.56 340.23 -12.4 34.4 1.7 -2.4 84.8 90.5 87.7 88.7 100 -0.09 6.74 37.87 347.51 -8.2 37.0 2.4 -4.3 85.1 91.5 88.5 89.3 100 -0.09 7.24 33.31 344.43 -8.9 32.1 1.4 -0.7 85.9 92.0 89.4 90.0 100 -0.08 7.74 27.63 342.97 -8.1 26.4 -1.0 1.7 86.1 92.3 89.7 90.1 99 -0.05 8.24 -32768 -32768 -32768 -32768 -32768 -32768 255 255 255 255 0 2147483647 <strong>Acknowledgements</strong> The data were collected on R/V Savannah funded by NSF-LTER.
DOI: 10.25345/c5sn01f3w
title: MassIVE MSV000092426 - GNPS - &lt;Gentian disaccharide and laminobu disaccharide standards&gt;
title: This is the spectrum of the gentian disaccharide and laminobu disaccharide standards. (PGC-MS/MS)
DOI: 10.26207/1t9z-ep92
title: Delta-9-Tetrahydrocannabinol (∆9-THC) and Cannabidiol (CBD) Drug-Drug Interactions
DOI: 10.5281/zenodo.8141111
title: AdriSC Climate Model Data - For the article: Projecting expected growth period of bivalves in a coastal temperate sea

description: The recent implementation, development and successful runs of the kilometer-scale atmosphere-ocean Adriatic Sea and Coast (AdriSC) climate model for the historical period of 1987-2017 and for an extreme climate projection (RCP 8.5) for the 2070-2100 period, have provided the necessary dataset to better understand the potential impact of climate change within the Adriatic basin. Here, temperature, salinity and ocean currents were extracted and formatted from the AdriSC ocean model at 1 km resolution. This dataset was then used to reproduce in the past (1987-2017 period) and project in the future (2070-2100 period) the expected growth of five bivalve species in the northern Adriatic Sea at two different locations: Barbariga and along the western coast of Istria.
description: For any enquiries about the AdriSC model contact: Clea Denamiel at cdenami@irb.hr