cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1054776.10fu
title: SH1054776.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054779.10fu
title: SH1054779.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054780.10fu
title: SH1054780.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054781.10fu
title: SH1054781.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1054784.10fu
title: SH1054784.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.hxd9b9
title: Occurrence Download

description: A dataset containing 128071 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is University of Michigan Museum of Zoology, Division of Mammals"
 ]
}

 The dataset includes 128071 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020558-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jkbpdb
title: Occurrence Download

description: A dataset containing 110865 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.countrycode = 'TH'
 AND occurrence.occurrencestatus = 'PRESENT'
 AND (occurrence."year" &gt;= 2000 AND occurrence."year" &lt;= 2024)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 110865 records were derived from 693 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020554-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/k38p8gzm4n.2
title: Debt Dynamics in India

description: Interest rates, growth, debt/GDP and inflation for India and 10 biggest states by population.
DOI: 10.17632/vdwn4m75b2.1
title: Ginzburg - Landau - Fluxoids

description: Title of program: GINZBURG-LANDAU-FLUXOIDS Catalogue Id: ACKA_v1_0 Nature of problem The Ginzburg-Landau-equations are solved for a straight single fluxoid containing an (integer) number q of flux quanta, starting from an approximate solution which has to be submitted by the user if q &gt; 1. Versions of this program held in the CPC repository in Mendeley Data ACKA_v1_0; GINZBURG-LANDAU-FLUXOIDS; 10.1016/0010-4655(69)90012-5 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.17632/fkprs4s6xp.1
title: UmUTracker: A versatile MATLAB program for automated particle tracking of 2D light microscopy or 3D digital holography data

description: We present a versatile and fast MATLAB program (UmUTracker) that automatically detects and tracks particles by analyzing video sequences acquired by either light microscopy or digital in-line holographic microscopy. Our program detects the 2D lateral positions of particles with an algorithm based on the isosceles triangle transform, and reconstructs their 3D axial positions by a fast implementation of the Rayleigh-Sommerfeld model using a radial intensity profile. To validate the accuracy and performance of our program, we first track the 2D position of polystyrene particles using bright field and digital holographic microscopy. Second, we determine the 3D particle position by analyzing synthetic and experimentally acquired holograms. Finally, to highlight the full program features, we profile the microfluidic flow in a 100μm high flow chamber. This result agrees with computational fluid dynamic simulations. On a regular desktop computer UmUTracker can detect, analyze, and track multiple particles at 5 frames per second for a template size of 201 × 201 in a 1024 × 1024 image. To enhance usability and to make it easy to implement new functions we used object-oriented programming. UmUTracker is suitable for studies related to: particle dynamics, cell localization, colloids and microfluidic flow measurement.
DOI: 10.17632/rfrw3z4hs7.2
title: Push test database of headed stud connectors embedded in solid concrete slabs

description: This database contains the results of 242 push tests on headed stud shear connectors embedded in solid concrete slabs. The database can be used as a basis for structural reliability analyses, or the development of machine learning models for predicting the resistance of headed stud connectors used in composite steel and concrete construction. 

For details of the changes made between the different versions of the database, please refer to the quality record sheet within the file.
 
[The database was used in the reliability analyses presented in the following paper that was published within the Journal of Constructional Steel Research https://doi.org/10.1016/j.jcsr.2017.09.018]
DOI: 10.17632/ncxcw6g324.2
title: Raw data-Abnormalities of Resting State Cortical EEG Rhythms in Subjects with Mild Cognitive Impairment Due to Alzheimer's and Lewy Body Diseases

description: Research data
DOI: 10.17632/34bhbrt887.1
title: A GLIMPSE INTO AFGHANISTAN THROUGH SYED MUJTABA ALI’S ‘DESHE BIDESHE’: AN INDIAN VOYAGER'S INSIGHTFUL LENS

description: Abstract: Syed Mujtaba Ali's Deshe Bideshe, first released in 1948, is a well acclaimed travel narrative that chronicles his encounters in Afghanistan from 1927 to 1929. In this reading, I will focus on the unique characteristic that differentiates it from the conventional travel literature of the era generated via the colonial interaction. Mujtaba Ali's firsthand encounter with colonialism in India, along with his deep understanding of history, endows him with a unique ability to provide insights into Afghanistan's social and cultural metamorphosis at a certain era. The depiction of the Afghan identity in the face of foreign colonial forces and internal turmoil is both stimulating and demanding, since it does not seek to demystify or categorise like colonial works that strive to understand the Oriental. The individual's colonised awareness finds a reason to rejoice in the autonomous Afghan, enabling us to examine his work as a postcolonial piece of literature. Justine D. Edwards and Rune Graulund argue that postcolonial travel writing challenges both the authority of colonial powers in shaping reality and the connection between travel and dominance. They assert this in their book "Postcolonial Travel Writing: Critical Explorations" (3). Hence, although Robert Byron's travelogue "The Road to Oxiana" (1937), which was written a few years after Ali's visit, presents Afghanistan as a historical artefact in need of British safeguarding, an account of Afghanistan by a colonised Indian criticises the connection between travel and domination and aims to foster a dialogue between southern regions, driven by the aspiration for regional cooperation.
DOI: 10.17632/xyt52fhr38.1
title: Green Marketing Study Dataset (1991-2019)

description: The Green Marketing research & publication in Indonesia dataset, which was indexed by Scopus from 1991 to 2019. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
DOI: 10.17632/p3jr4555tf.1
title: Foggy-Hazy License Plates Images

description: The study of locating and reading license plates from foggy or hazy vehicle images is a relatively emerging field of research. Each passing vehicle can be recorded with the help of the license plate reader's ability to automatically read the characters on the plates. In images where fog or haze is present, license plate recognition and detection are hindered. This dataset contains 1001 foggy- hazy images of vehicles with license plates. The dataset will be a research aid for the researchers and the society for performing license plate detection and recognition of foggy or hazy license plates. Images for this dataset were manually collected using Canon-DSLR-1500D Digital SLR cameras. The dataset includes images taken at various locations in Jaipur, Delhi, and Gurugram. Zip files of both foggy- hazy images, along with their corresponding ground truth images are included in the dataset.
DOI: 10.17632/dkksw4jbys.1
title: Meta-analysis on the relationship between sleep deprivation and memory in rodents

description: The data file (cma format) includes 78 reports from 25 experimental studies that analysed the impact of sleep deprivation on memory in rodents. The following information was extracted from studies:
-author of study;
-data format;
-means and standard deviations for the control group;
-means and standard deviations for the sleep deprived group;
-type of memory response;
-memory test;
-sleep deprivation duration;
-learning trials for certain memory tasks;
-sleep deprivation procedure;
-mice species included in study;
-age and sex of mice;
-sample size;
-effect direction;
-effect size indicators.

The general hypothesis is that sleep deprivation has a negative impact on memory performance in rodents. Overall, this hypothesis was supported by the existing data.
DOI: 10.17632/pyftdypmfs.1
title: Indonesian Disaster Dataset Based on Type for Technology-Based Disaster Management and Mitigation

description: We present a comprehensive dataset on natural disasters in Indonesia that occurred from January 2002 to December 2022. This data was collected through a series of open reports obtained from various sources including the Regional Disaster Management Agency. Next, data consolidation and summarization were carried out using Excel. This data set consists of 12 types of disasters such as floods, landslides, floods and landslides, tidal waves / abrasion, tornadoes, droughts, forest and land fires, earthquakes, tsunamis, earthquakes and tsunamis, volcanic eruptions and climate change. as well as the number of people affected over a 21 year period in 34 provinces in Indonesia. Data regarding the number of disasters, number of victims, and infrastructure damage are also provided in this data. From a statistical point of view, disaster data in Indonesia can be used in various data processing applications for various purposes, for example, disaster mitigation planning, preparation of emergency response plans, decision or policy making, development of early warning systems, public education and awareness, impact evaluation and recovery, as well as used for other research and development purposes.
DOI: 10.17632/ynf6n96bw3
title: Cytomegalovirus Reactivation Among Immunocompetent Patients Hospitalized in Medical Intensive Care: Associated Factors and Consequences

description: To determine the associated factors and consequences of CMV reactivation among Moroccan patients hospitalized in a medical ICU, a prospective and monocentric study was carried out on 72 immunocompetent CMV-seropositive patients admitted to the medical ICU. The monitoring of CMV by PCR in plasma was carried out at admission and then weekly.
DOI: 10.17632/584ydjx3xm.1
title: Engineered Cementitious Composites Dataset / Strain Hardening Cementitious Composites Data for FDNN ensemble predictive model

description: The data is for the research titled "An Ensemble Method for Predicting the Mechanical Properties of Strain Hardening Cementitious Composites" in CONSTRUCTION AND BUILDING MATERIALS journal. The folder "Models" Contains the models in the project organized as follows: * "Comparison Models" is the folder Containing the models currently available for comparison with our FDNN. * "FDNN Ensemble" Folder contains the neural network models for the FDNN ensemble * "FRST" Folder contains the random forest regressors component of the FDNN ensemble Two ".xlsx" files are provided in this project. * "Full SHCC Dataset.xlsx" is the Raw dataset collected from literature * "sep_dataset.xlsx" is the processed dataset used for training the models Five other files are provided in this project, as: * "Data description.txt" contains a small description of the data * "FDNN Training.ipynb" contains the FDNN training algorithm * "FDNN predictor.ipynb" contains a small demo of how to use the FDNN models to predict the properties of SHCC * "normalization_coefficiants.py" contains the normalization values that can be used to encode and decode the inputs for prediction purposes * "predict SHCC mix.py" is a python implementation of the "FDNN predictor.ipynb" file * "sep_dataset.xlsx" is the processed dataset used for training the models Finally a Description of the parameters is given in the file "Data description.txt" To develop the model, data were collected from a comprehensive literature review of articles. The dataset consists of several parameters related to the mix design and mechanical properties of SHCC. The mix design parameters included in the database are the amount of cement, water, fine aggregate, fly ash, silica fume, cenosphere, blast furnace slag, superplasticiser, coarse aggregate, accelerating admixtures, phase change materials, fibre weight, all represented in percentage weight. The parameters related to fibre properties in the dataset are fibre type, volume (% volume), diameter (Micro-meter), length (mm), tensile strength (MPa), and elastic modulus (GPa). The dataset also included rubber, light weight aggregate, viscosity agent hydroxypropyl methylcellulose, Air entraining admixture, oiling agent, graphene oxide, and defoamer represented in binary forms, i.e. 1 (was used in mix design) or 0 (was not used). Other experimental conditions are included, they are water curing time (days), air curing time (days), high frequency & velocity casting (1 or 0), and temperature (⸰C). Finally, the mechanical properties recorded in the dataset are the tensile stress at the first crack (MPa), tensile strain at the first crack (%), peak tensile stress (MPa), peak tensile strain (%), flexural strength at the first crack (MPa), flexural strain at the first crack (%), peak flexural Strength (MPa), peak flexural strain (%), and Compressive strength (MPa). In total, the resulting data set contains 38 parameters, divided into 29 features and nine targets.
DOI: 10.17632/rr3bj974ry.1
title: (Dataset) Artificial intelligence applied to education

description: Dataset related to surveys on the use of artificial intelligence in higher education
DOI: 10.17632/w8x7j5rbc4.3
title: Effect of changing moisture content on mineral spectra

description: This dataset contains spectral data and a supplementary document of our original research article: The Influence of Changing Moisture Content on Laboratory Acquired Spectral Feature Parameters and Mineral Classification. The spectral data are spectra we measured from six mineral powders: iron phosphate, gypsum, malachite, calcite, kaolinite, and montmorillonite in six ASCII files. We also publish photos on all out samples with increasing moisture content in the sample_photos_moisture.zip file. For the whole story, please refer to our paper.
DOI: 10.17632/ndb6t28xbk.4
title: Dataset of Annotated Rice Panicle Image from Bangladesh

description: This dataset focuses on drone-based rice panicle detection in Gazipur, Bangladesh, offering valuable visual data to researchers in agricultural studies. Captured using an advanced drone with a 4K resolution camera, the dataset comprises 2193 high-resolution images of rice fields and 5701 images after augmentation. All the images are annotated with precision to aid in automated rice panicle identification. Its main purpose is to support the development of algorithms and systems for critical agricultural tasks like crop monitoring and yield estimation, as well as disease identification and plant health evaluation. The dataset's creation involved extracting frames from drone-recorded video footage and meticulously annotating them with manual and deep learning algorithms using a semi-automatic approach.