cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18453350
title: "ARTIFICIAL INTELLIGENCE IN MODERN HEALTHCARE: BENEFITS AND CHALLENGES"

description: Artificial Intelligence (AI) is rapidly transforming the healthcare industry by enhancing diagnostics, treatment planning, and patient care. This study examines the benefits and challenges of AI integration in modern healthcare systems. AI technologies, including machine learning, natural language processing, and robotics, have demonstrated significant potential in improving accuracy, efficiency, and accessibility of medical services. Through a comprehensive review of academic literature, policy reports, and case studies, the research identifies both the positive impacts and limitations of AI in clinical practice.

The findings reveal that AI can enhance diagnostic accuracy, reduce human error, streamline administrative processes, and facilitate personalized medicine. For instance, machine learning algorithms can analyze vast datasets to detect diseases at early stages, while AI-powered robotic systems assist in complex surgeries with precision. Furthermore, AI applications improve patient engagement through digital health platforms and predictive analytics, allowing for proactive care management.

However, the study also highlights significant challenges, including data privacy concerns, algorithmic bias, high implementation costs, and ethical considerations in clinical decision-making. Healthcare professionals require adequate training to collaborate effectively with AI systems, and regulatory frameworks must evolve to ensure safe and equitable use of AI technologies. Additionally, the integration of AI should complement rather than replace human judgment, maintaining the patient-centered approach of healthcare.

In conclusion, AI presents transformative opportunities for modern healthcare but requires careful management to address ethical, technical, and operational challenges. The study underscores the need for balanced adoption strategies, robust governance, and continuous evaluation to maximize the benefits of AI while mitigating potential risks. By understanding both the advantages and limitations, healthcare stakeholders can implement AI solutions that enhance patient outcomes, improve operational efficiency, and support sustainable healthcare delivery.
DOI: 10.15468/dl.th7dkh
title: Occurrence Download

description: A dataset containing 110 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Helietta glaziovii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 110 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006276-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vdgt4h
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Material citation, Material sample)",
 "Geometry POLYGON((69.22909 36.86476,105.53152 36.86476,105.53152 56.97769,69.22909 56.97769,69.22909 36.86476))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Lutra lutra"
 ]
}

 The dataset includes 29 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006277-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18453356
title: СИНДРОМ ГИЙЕНА–БАРРЕ КАК МНОГОФАКТОРНОЕ СИСТЕМНОЕ ЗАБОЛЕВАНИЕ: КЛИНИКО-НЕВРОЛОГИЧЕСКИЙ И ИММУНОЛОГИЧЕСКИЙ АНАЛИЗ

description: индром Гийена–Барре (СГБ) является острым аутоиммунным заболеванием периферической нервной системы, характеризующимся быстрым развитием вялых параличей, сенсорных и вегетативных нарушений. Несмотря на внедрение современных методов иммунной терапии, течение и функциональные исходы СГБ остаются вариабельными. Это обусловлено клиническими формами заболевания, электрофизиологическими подтипами, выраженностью иммуновоспалительной активности, наличием коморбидной патологии и половыми особенностями. В связи с этим актуальной задачей современной неврологии является комплексная оценка клинико-неврологических, иммунологических и электрофизиологических факторов, определяющих тяжесть течения и прогноз СГБ.
DOI: 10.5281/zenodo.18453355
title: СИНДРОМ ГИЙЕНА–БАРРЕ КАК МНОГОФАКТОРНОЕ СИСТЕМНОЕ ЗАБОЛЕВАНИЕ: КЛИНИКО-НЕВРОЛОГИЧЕСКИЙ И ИММУНОЛОГИЧЕСКИЙ АНАЛИЗ

description: индром Гийена–Барре (СГБ) является острым аутоиммунным заболеванием периферической нервной системы, характеризующимся быстрым развитием вялых параличей, сенсорных и вегетативных нарушений. Несмотря на внедрение современных методов иммунной терапии, течение и функциональные исходы СГБ остаются вариабельными. Это обусловлено клиническими формами заболевания, электрофизиологическими подтипами, выраженностью иммуновоспалительной активности, наличием коморбидной патологии и половыми особенностями. В связи с этим актуальной задачей современной неврологии является комплексная оценка клинико-неврологических, иммунологических и электрофизиологических факторов, определяющих тяжесть течения и прогноз СГБ.
DOI: 10.15468/dl.wcr9qd
title: Occurrence Download

description: A dataset containing 1198 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Codonopsis pilosula"
 ]
}

 The dataset includes 1198 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006278-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w3g4kg
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hortia superba",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 16 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006279-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/cc4339m
title: CCDC 122149: Experimental Crystal Structure Determination

description: Related Article: M.Yoshimatsu, S.Gotoh, G.Tanabe, O.Muraoka|1999|Chem.Commun.||909|doi:10.1039/a901707k
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.88hvks
title: Occurrence Download

description: A dataset containing 54 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Material sample, Material citation)",
 "Geometry POLYGON((69.22909 36.86476,105.53152 36.86476,105.53152 56.97769,69.22909 56.97769,69.22909 36.86476))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Saiga tatarica"
 ]
}

 The dataset includes 54 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006280-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mk9cmw
title: Occurrence Download

description: A dataset containing 482340 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cenchrus longispinus, Lantana camara, Liriomyza sativae, Phthorimaea absoluta, Mimosa bimucronata, Praxelis clematidea, Cyclachaena xanthiifolia, Lactuca serriola)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 482340 records from 3175 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006253-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n3pk2v
title: Occurrence Download

description: A dataset containing 5079430 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Carassius auratus, Carassius carassius, Ctenopharyngodon idella, Cyprinus carpio, Esox lucius, Leuciscus idus, Leuciscus leuciscus, Micropterus salmoides, Oreochromis mossambicus, Oreochromis niloticus, Perca fluviatilis, Rutilus rutilus, Sander lucioperca, Scardinius erythrophthalmus, Silurus glanis, Tinca tinca, Carassius gibelio, Polyodon spathula, Gobio gobio)",
 "HasCoordinate is true",
 "DecimalLatitude is less than 60",
 "DecimalLatitude is greater than -56",
 "DecimalLongitude is less than 180",
 "DecimalLongitude is greater than -145"
 ]
}

 The dataset includes 5079430 records from 1531 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025418-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2hvyeh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vachellia farnesiana",
 "Country is Vanuatu",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024952-250811113504898/datasets/export for details.
DOI: 10.15468/dl.ubavc4
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vachellia farnesiana",
 "Country is Viet Nam",
 "HasCoordinate is true"
 ]
}

 The dataset includes 27 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024953-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5xk72p
title: Occurrence Download

description: A dataset containing 1471535 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "BasisOfRecord is Human Observation",
 "Year is greater than or equal to 2012",
 "Year is less than or equal to 2012",
 "Month is January",
 "Day is greater than or equal to 15",
 "Day is less than or equal to 28"
 ]
}

 The dataset includes 1471535 records from 402 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001764-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18739/a2t43j45z
title: Plant phenology of species in moist acidic tundra around Toolik Field Station, Alaska, 2007-2023

description: The Toolik Field Station (TFS) plant phenology program monitors the timing of specific phenological developmental stages of plant species commonly found in the moist acidic tundra plant community. The TFS phenology program began in response to TFS research community requests to collect baseline environmental data that would be broadly applicable and provide context to research projects conducted near TFS. The TFS plant phenology data collection protocol is based on the International Tundra Experiment (ITEX, www.geog.ubc.ca/itex) protocol for the Toolik Snowfence Experiment. This moist acidic tundra dataset began in 2007 and continues through 2023.
DOI: 10.18739/a2pc2tb29
title: Plant phenology of species in dry heath tundra around Toolik Field Station, Alaska, 2011-2023

description: The Toolik Field Station (TFS) plant phenology program monitors the timing of specific phenological developmental stages of plant species commonly found in the dry heath tundra plant community. The TFS phenology program began in response to TFS research community requests to collect baseline environmental data that would be broadly applicable and provide context to research projects conducted near TFS.
 The TFS plant phenology data collection protocol is based on the International Tundra Experiment (ITEX) (www.geog.ubc.ca/itex) protocol for the Toolik Snowfence Experiment.
 This dry heath tundra dataset began in 2011 and continues through 2023.
DOI: 10.15468/dl.s2wku9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Mionectes macconnelli (Chubb, 1919))",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016382-250525065834625/datasets/export for details.
DOI: 10.15468/dl.2k72dv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Mionectes macconnelli (Chubb, 1919))",
 "Country is one of (Åland Islands, Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Republic of, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016383-250525065834625/datasets/export for details.
DOI: 10.15468/dl.dtfwef
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Mionectes macconnelli (Chubb, 1919))",
 "Country is one of (Algeria, Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Congo, Democratic Republic of the, Côte d’Ivoire, Djibouti, Egypt, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, French Southern Territories, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mayotte, Morocco, Mozambique, Namibia, Niger, Nigeria, Réunion, Rwanda, Saint Helena, Ascension and Tristan da Cunha, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Tanzania, United Republic of, Togo, Tunisia, Uganda, Western Sahara, Zambia, Zimbabwe)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016384-250525065834625/datasets/export for details.
DOI: 10.5061/dryad.ghx3ffbww
title: A highly magnetized long-period radio transient exhibiting unusual emission features

description: Long-period radio transients are a new class of astrophysical objects that
 exhibit periodic radio emission on timescales of tens of minutes. Their
 true nature remains unknown; possibilities include magnetic white dwarfs,
 binary systems, or long-period magnetars; the latter class are predicted
 to produce fast radio bursts (FRBs). Using the MeerKAT radio telescope, we
 conducted follow-up observations of the long-period radio transient GPM
 J1839–10. Here we report the source exhibits a wide range of unusual
 emission properties, including polarization characteristics indicative of
 magnetospheric origin, linear-to-circular polarization conversion, and
 drifting sub-structures closely resembling those observed in repeating
 FRBs. These radio characteristics provide clear evidence in support of the
 long-period magnetar model and establish the missing link between
 long-period radio transients, magnetars, and FRBs.
description: # A highly magnetized long-period radio transient exhibiting unusual
 emission features
 [https://doi.org/10.5061/dryad.ghx3ffbww](https://doi.org/10.5061/dryad.ghx3ffbww) The dataset includes MeerKAT follow-up observation data used in the analysis of GPM1839-10 in the paper [https://www.science.org/doi/10.1126/sciadv.adp6351](https://www.science.org/doi/10.1126/sciadv.adp6351). ## Description of the data and file structure The data are structured in the order corresponding to the plots in the manuscript. The compressed data archive follows this structure: dryad_data.tar.gz \|- data/ \| |- plot1/ \| |- plot2/ \| |- plot3/ \| |- plot4/ , where each subdirectory contains Stokes data used to reproduce Figures 1, 2, 3, and 4 in the paper, respectively. The suffix "vt"* represents the array of time samples. "vf" represents the array of frequency channels. "mxI", "*mxQ", "mxU", "mxV' correspond to the Stokes parameters I, Q, U, and V respectively. ## Sharing/Access information NA ## Code/Software This dataset also includes a Jupyter notebook, python_scripts.ipynb, which provides the necessary code to reproduce the figures presented in the paper.
DOI: 10.15483/3014716
title: Baseline vs. DER Scenario

description: Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).
DOI: 10.5281/zenodo.18404948
title: Data for vandalt/syw-test [main]

description: Data automatically uploaded by the showyourwork workflow. Each of the files in this deposit were generated from a user-defined Snakemake rule with the same name and hash specified below. Please visit github.com/showyourwork and/or the article source repository for more information.
description: simulation: 546bedad29d8c36c94c4686bc3cddf32995d89fed4e5b1d6625f28b4cec40f64.dat
DOI: 10.5061/dryad.m0cfxppbj
title: Solitary humpback whales manufacture bubble-nets as tools to increase prey intake

description: Several animal species use tools for foraging; however, very few
 manufacture and/or modify those tools. Humpback whales, which manufacture
 bubble-net tools while foraging, are among these rare species. Using
 animal-borne tag and unoccupied aerial system technologies, we examine
 bubble-nets manufactured by solitary humpback whales (Megaptera
 novaeangliae) in Southeast Alaska while feeding on krill. We demonstrate
 that the nets consist of internally- tangential rings and suggest that
 whales actively control the number of rings in a net, net size and depth,
 and the horizontal spacing between neighbouring bubbles. We argue that
 whales regulate these net structural elements to increase per-lunge prey
 intake by, on average, seven-fold. We measured breath rate and swimming
 and lunge kinematics to show that the resulting increase in prey density
 does not increase energetic expenditure. Our results provide a novel
 insight into how bubble-net tools manufactured by solitary foraging
 humpback whales act to increase foraging efficiency.
description: # Solitary humpback whales manufacture bubble-nets as tools to increase
 prey intake
 [https://doi.org/10.5061/dryad.m0cfxppbj](https://doi.org/10.5061/dryad.m0cfxppbj) These datasets encompass the analyses of CATS (Customized Animal Tracking Solutions) biologging tag and UAS drone data collected on five solitary bubble-netting humpback whales in Southeast Alaska during the summer of 2019. All Data is included with R scripts to process the data in to the formats used to create figures present in this article. ## Description of the data and file structure All .csv files contain the data analyzed directly from processing Matlab files (CATS biologging tag data), image analysis (UAS drone data), or video analysis (CATS biologging tag and UAS drone data). R scripts can be used in any order to import and adjust .csv files into a more user-friendly format for creation of figures or further analysis. Columns in the primary data files: "Data_From_CATS_Bubblenets.csv" (For data from CATS accelerometry tags) and "Data_From_UAS_Bubblenets.csv" (For data from UAS drone operations) can be understood using the associated "KEY" files. Speed and depth profiles data files ("Speed_Profiles_From_CATS_Bubblenets.csv"; "Depth_Profiles_From_CATS_Bubblenets.csv") contain the corresponding datastream for each dive in the dataset (one dive = one row; each column is equal to one data time point at 10Hz, so 10 columns = 1 second), aligned the the moment of maximal speed. These were used to create Figure 4 in the article. The units for speed profiles are "meters per second" and the units for depth profiles are "meters". The metadata file "Metadata_From_CATS_Bubblenets.csv" includes the number of dives performed by each individual whale. ## Sharing/Access information Datasets can also be accessed through the following Github repository, updated at the time of article acceptance: [https://github.com/wgough/SolitaryBubbleNets](https://github.com/wgough/SolitaryBubbleNets)