cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34683/sr495
title: StoneRounds Case: SR495
title: Best Disease

description: StoneRounds Case SR495 was diagnosed with Best Disease likely caused by mutations in the gene: BEST1.
DOI: 10.34683/sr2781
title: StoneRounds Case: SR2781
title: Best Disease

description: StoneRounds Case SR2781 was diagnosed with Best Disease likely caused by mutations in the gene: BEST1.
DOI: 10.5281/zenodo.10251953
title: Files for plotting

description: Unzip and run different python notebooks for the plots. Includes input namelists, stats files (timeseries and profiles), and 2d data of albedo and/or plumes at certain altitudes.
DOI: 10.5281/zenodo.10270344
title: Files for plotting

description: Unzip and run different python notebooks for the plots. Includes input namelists, stats files (timeseries and profiles), and 2d data of albedo and/or plumes at certain altitudes.
DOI: 10.48366/r655958
title: Review: Detection of abnormal movements of ships based on AIS data in water transport using machine learning methods

description: The table provides an insight into the scientific papers that dealt with the problem of anomalous movements of ships based on AIS data in water transport using machine learning methods. Anomaly detection in water transport refers to the process of identifying unusual or abnormal behavior in vessels or maritime activities. This can include detecting deviations from typical sailing patterns, unexpected changes in speed or direction, irregular stops or deviations from established routes, and other behaviors that do not conform to expected or normal maritime activities. Anomalies in water transport are significant as they can indicate potential safety hazards, security threats, or non-compliance with maritime regulations. Detecting and addressing anomalies is crucial for ensuring the safety, security, and efficiency of maritime traffic and transportation. Advanced anomaly detection methods, such as kinematic interpolation, Support Vector Machines (SVM), Random Forest, Neural Networks, k-means clustering, DBSCAN,  Parallel Meta-Learning (PML)  can be trained to detect anomalies by learning patterns from historical data and identifying deviations from these patterns. The aim of anomaly detection in water transport is to identify potential safety or security threats, such as illegal activities, accidents, or navigational errors, as well as to improve maritime situational awareness and operational efficiency. By leveraging machine learning methods, anomaly detection systems can help maritime authorities and operators identify and respond to abnormal vessel behavior in a timely manner, thereby enhancing overall maritime security and safety.
DOI: 10.5281/zenodo.10290709
title: IODP Expedition 383 Formation temperature

description: Formation temperature was measured by the advanced piston corer temperature tool (APCT-3), Sediment Temperature Tool (SET), or sediment temperature pressure tool (SETP) and reduced to in situ temperature and (for SETP) pressure estimates. These data are compiled per instrument run and assigned to a core (the core cut with the APCT-3 cutting shoe or the core following the SET deployment).
DOI: 10.5281/zenodo.10290708
title: IODP Expedition 383 Formation temperature

description: Formation temperature was measured by the advanced piston corer temperature tool (APCT-3), Sediment Temperature Tool (SET), or sediment temperature pressure tool (SETP) and reduced to in situ temperature and (for SETP) pressure estimates. These data are compiled per instrument run and assigned to a core (the core cut with the APCT-3 cutting shoe or the core following the SET deployment).
DOI: 10.5281/zenodo.10290718
title: IODP Expedition 383 Section-half images

description: Digital section images were taken of the flat face of split cores on the Section Half Imaging Logger (SHIL) using a linescan camera at a resolution of 20 lines/mm (50 micron pixels). Cores were imaged as soon as possible after splitting to minimize color changes that occur through oxidation and drying. The SHIL produces TIF files as well as reduced-size JPG files. The TIF files are not kept online but users may request them from the IODP-JRSO Data Librarian.
DOI: 10.5281/zenodo.10290719
title: IODP Expedition 383 Section-half images

description: Digital section images were taken of the flat face of split cores on the Section Half Imaging Logger (SHIL) using a linescan camera at a resolution of 20 lines/mm (50 micron pixels). Cores were imaged as soon as possible after splitting to minimize color changes that occur through oxidation and drying. The SHIL produces TIF files as well as reduced-size JPG files. The TIF files are not kept online but users may request them from the IODP-JRSO Data Librarian.
DOI: 10.26093/cds/vizier.36570031
title: Solar-like oscillations in Kepler DR25 SC data

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'Detections of solar-like oscillations in dwarfs and subgiants with Kepler DR25 short-cadence data.' (bibcode: 2022A&A...657A..31M)
DOI: 10.26093/cds/vizier.36600059
title: SAGAN III. New insights into giant radio quasars

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'Search and analysis of giant radio galaxies with associated nuclei (SAGAN). III. New insights into giant radio quasars.' (bibcode: 2022A&A...660A..59M)
DOI: 10.5281/zenodo.10291775
title: IODP Expedition 383 Gas safety report

description: This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C1/C2�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
DOI: 10.5281/zenodo.10291776
title: IODP Expedition 383 Gas safety report

description: This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C1/C2�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
DOI: 10.34683/sr994
title: StoneRounds Case: SR994
title: AR Retinitis Pigmentosa

description: StoneRounds Case SR994 was diagnosed with AR Retinitis Pigmentosa likely caused by mutations in the gene: ZNF408.
DOI: 10.5281/zenodo.10291768
title: IODP Expedition 383 Core drilling summary

description: Report includes detailed drilling data for each core: pump(s) used, mud pumped, strokes, shear pins/pressure, bit size/rotation, weight on bit, top drive torque, rate of penetration, core jams, winch and wirelines, core catcher/shoe and barrel, and whether core orientation, drillover, formation temperature, tracers, liners were used.
DOI: 10.5281/zenodo.10291767
title: IODP Expedition 383 Core drilling summary

description: Report includes detailed drilling data for each core: pump(s) used, mud pumped, strokes, shear pins/pressure, bit size/rotation, weight on bit, top drive torque, rate of penetration, core jams, winch and wirelines, core catcher/shoe and barrel, and whether core orientation, drillover, formation temperature, tracers, liners were used.
DOI: 10.34740/kaggle/dsv/7149973
title: Top Spotify Songs in 73 Countries (Daily Updated)
DOI: 10.5281/zenodo.10258685
title: Sim 3 Plumes data

description: Sim 3 (PINACLES 5 m simulation) plumes at different altitudes, 3-4 hrs. Copy into Sim_3 directory to recreate all plots
DOI: 10.5281/zenodo.10269673
title: Sim 3 Plumes data

description: Sim 3 (PINACLES 5 m simulation) plumes at different altitudes, 3-4 hrs. Copy into Sim_3 directory to recreate all plots
DOI: 10.5281/zenodo.10291832
title: IODP Expedition 383 Bulk Density (GRA)

description: Gamma ray attenuation (GRA) data were acquired using a Cs-137 collimated source and a sodium iodide (thallium), or NaI(Tl), scintillation detector. The signal was calibrated using water and aluminum standards to provide a proxy for bulk density. This measurement was performed by a sensor mounted on either the Whole-Round Multisensor Logger (WRMSL) or the Special Task Multisensor Logger (STMSL); which track was used for a given data set is indicated in the data.
DOI: 10.5281/zenodo.10291833
title: IODP Expedition 383 Bulk Density (GRA)

description: Gamma ray attenuation (GRA) data were acquired using a Cs-137 collimated source and a sodium iodide (thallium), or NaI(Tl), scintillation detector. The signal was calibrated using water and aluminum standards to provide a proxy for bulk density. This measurement was performed by a sensor mounted on either the Whole-Round Multisensor Logger (WRMSL) or the Special Task Multisensor Logger (STMSL); which track was used for a given data set is indicated in the data.
DOI: 10.34740/kaggle/dsv/7149980
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.5281/zenodo.10291843
title: IODP Expedition 383 Hole drilling summary

description: Report includes detailed drilling data: BHA length, bit parameters, and primary depth measurements.
DOI: 10.5281/zenodo.10291842
title: IODP Expedition 383 Hole drilling summary

description: Report includes detailed drilling data: BHA length, bit parameters, and primary depth measurements.
DOI: 10.5281/zenodo.10291849
title: IODP Expedition 383 ICP-AES elemental analysis (interstitial water)

description: Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
DOI: 10.5281/zenodo.10291850
title: IODP Expedition 383 ICP-AES elemental analysis (interstitial water)

description: Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
DOI: 10.5281/zenodo.10291857
title: IODP Expedition 383 ICP-AES elemental analysis (solids)

description: Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
DOI: 10.5281/zenodo.10291858
title: IODP Expedition 383 ICP-AES elemental analysis (solids)

description: Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.
DOI: 10.5281/zenodo.10291865
title: IODP Expedition 383 Ion chromatography

description: Cation and anion concentration in interstitial water samples was measured by ion chromatography using a conductivity detector. Cation analytes include calcium, magnesium, potassium, and sodium. Anion analytes include chloride, sulfate, and bromide.
DOI: 10.5281/zenodo.10291866
title: IODP Expedition 383 Ion chromatography

description: Cation and anion concentration in interstitial water samples was measured by ion chromatography using a conductivity detector. Cation analytes include calcium, magnesium, potassium, and sodium. Anion analytes include chloride, sulfate, and bromide.
DOI: 10.5281/zenodo.10291871
title: IODP Expedition 383 Interstitial water composite report

description: This composite report includes data from numerous interstitial water (IW) analyses (e.g., ICP-LIQUIDS and IONCHROM) in a variable set of columns. Each row combines the data from several tests (groups of measurements made on the same sample at the same time) for a particular IW sample. If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate analysis (ALKALINITY, ICP-LIQUIDS, IONCHROM, SALINITY, SPEC, TITRATOR, and potentially others). Major and minor elements were determined by inductively coupled plasma-atomic emission spectrocsopy (ICP-LIQUIDS). Cations and anions were measured by ion chromatography (IONCHROM). Complex ions were measured by colorimetric measurements using a UV-VIS spectrometer (SPEC). Alkalinity and chloride was measured by titration (ALKALINITY and TITRATOR). Dissolved carbon (DC), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC) were measured by combustion in a total organic carbon (TOC) analyzer. Salinity was measured by refractometer (SALINITY). pH was measured by ion-selective electrode as part of the alkalinity procedure and is reported in the ALKALINITY analysis.
DOI: 10.5281/zenodo.10291872
title: IODP Expedition 383 Interstitial water composite report

description: This composite report includes data from numerous interstitial water (IW) analyses (e.g., ICP-LIQUIDS and IONCHROM) in a variable set of columns. Each row combines the data from several tests (groups of measurements made on the same sample at the same time) for a particular IW sample. If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate analysis (ALKALINITY, ICP-LIQUIDS, IONCHROM, SALINITY, SPEC, TITRATOR, and potentially others). Major and minor elements were determined by inductively coupled plasma-atomic emission spectrocsopy (ICP-LIQUIDS). Cations and anions were measured by ion chromatography (IONCHROM). Complex ions were measured by colorimetric measurements using a UV-VIS spectrometer (SPEC). Alkalinity and chloride was measured by titration (ALKALINITY and TITRATOR). Dissolved carbon (DC), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC) were measured by combustion in a total organic carbon (TOC) analyzer. Salinity was measured by refractometer (SALINITY). pH was measured by ion-selective electrode as part of the alkalinity procedure and is reported in the ALKALINITY analysis.
DOI: 10.5281/zenodo.10291887
title: IODP Expedition 383 Moisture and Density

description: Moisture and density (MAD) data were acquired on ~10 mL sediment or rock samples by measuring three out of four material parameters: wet (saturated) mass, wet volume, dry mass, and/or dry volume after 24 h drying in a convection oven at 105 degrees C. From the moisture and volume measurements, the following phase relationships are calculated: wet and dry water content, wet bulk density, dry bulk density, grain density, porosity, and void ratio. The combination of measurements is defined by the submethod chosen: A, B, C, or D. Wet (A, B, or C) and dry (A, B, C, or D) mass is determined using motion-compensated balances.  Wet volume is determined either by helium pycnometry (A) or by the sample's geometric dimensions using calipers (A or D).  Dry volume (C or D) is measured by helium pycnometry.  Submethods A and B are not recommended by IODP.  Submethod C is suitable for saturated materials such as fine-grained sediments.  Submethod D is suitable for unsaturated porous material such as certain limestones and basalts.
DOI: 10.5281/zenodo.10291888
title: IODP Expedition 383 Moisture and Density

description: Moisture and density (MAD) data were acquired on ~10 mL sediment or rock samples by measuring three out of four material parameters: wet (saturated) mass, wet volume, dry mass, and/or dry volume after 24 h drying in a convection oven at 105 degrees C. From the moisture and volume measurements, the following phase relationships are calculated: wet and dry water content, wet bulk density, dry bulk density, grain density, porosity, and void ratio. The combination of measurements is defined by the submethod chosen: A, B, C, or D. Wet (A, B, or C) and dry (A, B, C, or D) mass is determined using motion-compensated balances.  Wet volume is determined either by helium pycnometry (A) or by the sample's geometric dimensions using calipers (A or D).  Dry volume (C or D) is measured by helium pycnometry.  Submethods A and B are not recommended by IODP.  Submethod C is suitable for saturated materials such as fine-grained sediments.  Submethod D is suitable for unsaturated porous material such as certain limestones and basalts.
DOI: 10.34740/kaggle/dsv/7150032
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.7924/r4qf8x909
title: Data and scripts from: Urban demand for cooking fuels in two major African cities and implications for policy

description: Nearly 2.3 billion people lack access to clean cooking fuels and technologies worldwide, representing a critical failure to achieve SDG7's cooking energy access goal. Dependence on polluting cooking fuels is particularly high in Sub-Saharan Africa, where it generates considerable environmental, health, and time-related costs. In the region, progress has been greatest in urban areas, but understanding of the dynamics of urban cooking energy transitions remains limited. Though higher average incomes and greater availability of alternative fuels, relative to rural areas, helps to explain urban populations generally higher access rates, different cities display divergent paths, and the impacts of policy instruments in fostering household energy transition remain unclear. This paper considers the demand for several fuels among low-income households in two such contrasting cities - Nairobi, where the transition is well advanced (N=354), and Dar es Salaam, where progress has been slower (N=1,100). Household preference data from our double-bounded, dichotomous choice contingent valuation experiment helps us understand how urban households respond to changes in the price of their preferred cooking fuels. We find that fuel price responses vary across the income distribution and across the two cities. Specifically, the willingness to pay for the most commonly used cooking fuel in Nairobi - liquefied petroleum gas - is nearly twice that in Dar es Salaam, where more households prefer charcoal. In Dar es Salaam, low-income charcoal users appear especially entrenched in their choice of cooking fuel. The extent to which different policy tools (such as bans, taxes, or clean fuel subsidies) can be effective depends on these price sensitivities, enforcement, and also on the readiness of supply-side enablers to meet increased demand. Importantly, though policies are designed at the national-level, policy-makers need to understand nuances in the local demand context very well to choose the most appropriate instruments to support energy transition among their most vulnerable citizens.
DOI: 10.18739/a2251fm86
title: Alaska remote community survey dataset on COVID-19 impacts (2020-2021)

description: This work is part of a larger longitudinal study of the impact of COVID-19 on the daily life of people living in remote Alaskan communities. The parent study began in September 2020, five and half months after the Governor of Alaska issued the first COVID-19 health mandate. At the start of the study, we conducted 23 key informant interviews with residents in remote Alaskan communities in leadership positions, who were involved in the COVID-19 response, and/or who could provide a cultural perspective of ongoing events in their community. These conversations, along with consultation of Tribal and state representatives involved in Alaska’s pandemic response, guided the development of three waves of online surveys for individuals living in remote Alaskan communities. Data were collected via three survey waves (Wave 1: November 9 through 15 December 2020; Wave 2: March 9 to 25 March 2021; Wave 3: September 2–27). The surveys included questions regarding life changes due to COVID-19, coping strategies, emotions and worries related to the virus, perceived risks, sources of information, vaccine and testing perceptions, and questions regarding age, gender, level of education, household income, occupation, healthcare access, number of people living in the household, and access to water and sanitation services. Survey questions are available upon request.
DOI: 10.34740/kaggle/dsv/7150057
title: Full TMDB Movies Dataset 2023 (930K Movies)
DOI: 10.48366/r655960
title: Review: Interpolation of missing AIS data in water transport using machine learning

description: The table gives an insight into the scientific papers that dealt with the problem of insufficient number of AIS data when processing ship trajectories. Missing AIS data of a ship refers to the gaps or missing information in the Automatic Identification System (AIS) data transmitted by the ship, which can occur due to various factors such as signal interference, equipment malfunction, or other technical issues. These missing data points can lead to gaps in the vessel's trajectory, which can affect the accuracy of the ship's position, course, and speed information, and can potentially impact maritime safety and navigation. Interpolating missing AIS data can help to recover the lost data and produce useful information for VTS stations or other ships. Interpolation is a mathematical technique used to estimate the value of a function or data point between two known values. Interpolation methods are used to fill in missing data points or to estimate values at points where data is not available. There are various interpolation methods, including linear interpolation, polynomial interpolation, spline interpolation and others types of interpolation like Long Short-Term Memory (LSTM) network, Random Forest (RF) algorithm and U-Net. The choice of interpolation method depends on the nature of the data and the desired level of accuracy.
DOI: 10.5281/zenodo.10292129
title: Replication Package for 'Lessons Learned from Mining the Hugging Face Repository'

description: Replication Package attached to the 'Lessons Learned from Mining the Hugging Face Repository' article. Within the README and accompanying scripts, you will find detailed instructions to guide you through the analysis conducted in the article. Please, remember to cite the original paper if the replication package is used.
DOI: 10.5281/zenodo.10292130
title: Replication Package for 'Lessons Learned from Mining the Hugging Face Repository'

description: Replication Package attached to the 'Lessons Learned from Mining the Hugging Face Repository' article. Within the README and accompanying scripts, you will find detailed instructions to guide you through the analysis conducted in the article. Please, remember to cite the original paper if the replication package is used.