cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/f9kjfvrw3w.1
title: writing data jasmine

description: A total of 160 students participated in the study. There were two groups, including the experimental and the control group, with 80 students in each group. It is a quasi-experimental study that was statistically analysed through mixed-methods ANOVA, and the results were analysed. There was an enhancement in the writing of the experimental group and better use of the language was evident. There was an effective improvement in sentence formation, punctuation, and vocabulary use in their writing.
Idiom illustrations were employed based on the themes in the textbooks. These were used to improve their comprehension of the idioms and their writing skills through sentence formation, punctuation, and vocabulary activities. The statistical method employed for this study is mixed method ANOVA. It is a statistical analysis technique combining within-subjects (repeated measures) and between-subjects factors. It can be used when there is at least one within-subjects factor (e.g., multiple time points) and one between-subjects factor (e.g., different groups) and while comparing the effectiveness of a treatment over time between different groups (e.g., an experimental group vs. a control group). The within-subjects factor is the aspect where the same participants are measured multiple times under different conditions or over different time points, and the between-subjects factor is the aspect where different groups of participants are compared across the levels of the within-subjects factor (e.g., control vs. experimental groups). This method is employed when the data is normally distributed, and the assumption is that the sphericity (for within-subjects factor) where the variances of the differences between all combinations of related groups (levels of the within-subjects factor) are equal. The homogeneity of variance (for between-subjects factor), where the variances between groups are approximately equal. 

The research design is a quasi-experimental research design. 
There are three hypotheses to be tested. They are -
• H01- There is no progress in learners' Sentence formation (Writing) abilities between the Control and Experimental groups.
• H02- There is no progress in learners' Punctuation (Writing) abilities between the Control and Experimental groups.
• H03- There is no progress in learners' Vocabulary (Writing) abilities between the Control and Experimental groups.
The three hypotheses enumerated aim to assess the effectiveness of the intervention and the differences in performance between the control group and the experimental group in three aspects of writing: sentence formation, punctuation, and vocabulary.
DOI: 10.17632/pf4stttnrg.1
title: Dataset of Real Estate Security Token Offerings and the Secondary Market: Driven by Crypto Hype of Fundamentals?

description: This is the dataset to the paper: Julia Kreppmeier, Ralf Laschinger, Bertram I. Steininger, Gregor Dorfleitner; Real estate security token offerings and the secondary market: Driven by crypto hype or fundamentals?, Journal of Banking & Finance, Volume 154, 2023, 106940, ISSN 0378-4266, https://doi.org/10.1016/j.jbankfin.2023.106940.
It contains all information about the real estate tokens (such as: Rent_Per_Token_per_year, Token_Price, Total_Tokens , Property_Type, Construction_Year, Neighborhood etc.) as well as the corresponding blockchain transactions on the Ethereum and xDai blockchain. The properties and the corresponding transactions are connected via an ID identifier. All other variables used within the paper as control variables can be publicly accessed. The data presented here can also be accessed via public APIs. 
Please cite the paper as well as this document if the data is used. A detailed framework for the calculations stemming from the data can be found in the paper. The paper is open access and can be found here: https://www.sciencedirect.com/science/article/pii/S0378426623001450
DOI: 10.17632/pf4stttnrg.2
title: Dataset of Real Estate Security Token Offerings and the Secondary Market: Driven by Crypto Hype or Fundamentals?

description: This is the dataset to the paper: Julia Kreppmeier, Ralf Laschinger, Bertram I. Steininger, Gregor Dorfleitner; Real estate security token offerings and the secondary market: Driven by crypto hype or fundamentals?, Journal of Banking & Finance, Volume 154, 2023, 106940, ISSN 0378-4266, https://doi.org/10.1016/j.jbankfin.2023.106940.
It contains all information about the real estate tokens (such as: Rent_Per_Token_per_year, Token_Price, Total_Tokens , Property_Type, Construction_Year, Neighborhood etc.) as well as the corresponding blockchain transactions on the Ethereum and xDai blockchain. The properties and the corresponding transactions are connected via an ID identifier. All other variables used within the paper as control variables can be publicly accessed. The data presented here can also be accessed via public APIs. 
Please cite the paper as well as this document if the data is used. A detailed framework for the calculations stemming from the data can be found in the paper. The paper is open access and can be found here: https://www.sciencedirect.com/science/article/pii/S0378426623001450
DOI: 10.17632/23xtbvk6yp.1
title: DOSSPRE 

description: Dataset of Students’ Software Projects Requirements. The dataset has 1317 software requirements, distributed as 801 Non Security Requirements (NSR) and 516 Security Requirements (SR) constituting about 61% and 39% of the dataset respectively.  The requirements were harvested from 105 students projects. Besides the original dataset, there are other 2 versions of it, one labelled for binary classification that is NSR and SR and another for multiclass classification. The later has labels in the following categories Classification for Non-Security Requirements (NSR) include Functional, Usability, Portability, Performance Requirements while Security Requirements (SR) include Availability (AVA), Authentication (THE), Authorization (THO), Immunity (IMM), Integrity (INT), Intrusion detection (IND), Confidentiality (CON), Auditing (AUD), Survivability (SUR) and Maintainability (MAI). Software Maintenance is a process that can not be avoided in the lifetime of a software product. Thus, the idea being exposed is the need to include Maintainability as a Security Requirement to prevent introduction of software vulnerabilities every time there are changes to be made to software.
DOI: 10.17632/377tns888y.1
title: ¬¬Genesis and structure of Cobalt rich Fe-Mnnodules and crust of Lakshadweep Ridge, Arabian Sea, India.

description: Genesis and structure of Cobalt rich Fe-Mn nodules and crust of
Lakshadweep Ridge, Arabian Sea, India.
Lakshadweep crust and nodules are found to be seamount hosted, and their geochemistry reflects the hydrogenetic origin slow growth rate ~1-2mm/Ma. Fe-Mn nodules in the Peremul Par area of Lakshadweep are of considerable size, with an average diameter of 15 cm and a thick oxide layer of 6 cm. Core of Fe-Mn nodules and crust are phosphatised. Internal structures and EPMA analysis show that phosphatisation has occurred in the substrate, the nucleus, and during the later stages of Fe-Mn precipitation.
DOI: 10.14473/csda/fbk2gn
title: Vzdělanostní aspirace mladých v Karlovarském kraji

description: Data z celkem 43 polostrukturovaných hloubkových rozhovorů s mladými lidmi ve věku 15-19 let (31; z toho 17 chlapců a 14 dívek) a s jejich rodiči (12) žijícími ve vyloučených lokalitách (Kraslice, Sokolov) v Karlovarském kraji. Jedná se o tři skupiny respondentů: 1) studenti střední školy (20); 2) mladí, kteří předčasně ukončili střední školu (3) a 3) mladí lidé, kteří na žádnou střední školu nenastoupili a ukončili své vzdělávání základní školou (8). V rámci rozhovorů jsme se zabývali tématy jako studium a spokojenost se studiem v základní škole, rozhodování o dalším směřování a faktory výběru střední školy. V případě studujících nebo těch, kteří předčasně ukončili studium střední školy, nás zajímala pozitiva a negativa studia střední školy a v případě těch, kteří již nestudují, tak co v současné době dělají a jak si hledají nebo hledali práci. Ve všech případech nás zajímaly vedle budoucích plánů i všechny možné okolnosti, jež mohou ovlivňovat jejich studium a aspirace, jako jsou rodinná situace včetně té finanční, spokojenost a charakter bydlení, vzdělání a zaměstnání rodičů, případně dalších členů rodiny.
DOI: 10.15468/dl.4zn996
title: Occurrence Download

description: A dataset containing 40161 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "CoordinateUncertaintyInMeters 0.0-100600.0",
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "StateProvince is one of (ALaska, Yukon)",
 "TaxonKey is Rodentia",
 "Year 1980-2024"
 ]
}

 The dataset includes 40161 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033377-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.73r4ra
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hydrogonium gangeticum (Müll.Hal.) P.C.Chen"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033378-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fb3t62
title: Occurrence Download

description: A dataset containing 9523 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aedes aegypti (Linnaeus, 1762)",
 "Country is Chinese Taipei",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 9523 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033379-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4ut7wu
title: Occurrence Download

description: A dataset containing 11712 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Senna obtusifolia (L.) H.S.Irwin & Barneby"
 ]
}

 The dataset includes 11712 records from 412 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033381-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z7uzcs
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hydrogonium gracilentum (Mitt.) P.C.Chen"
 ]
}

 The dataset includes 5 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033382-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/d3n3mgjjbv.1
title: MH-Weed16:An Indian Multiclass Annotated Weed Dataset for Computer Vision Tasks

description: Weeds are invasive plants that compete with crops for vital nutrients and often attract pests, significantly impacting agricultural productivity. They account for approximately 45% of the annual productivity loss in farming. Manual weeding methods, while effective, are labor-intensive and financially cumbersome, particularly for smallholder farmers. On the other hand, excessive reliance on chemical herbicides has led to herbicide resistance in several weed species, causing additional challenges in weed management.Emerging technologies, particularly artificial intelligence (AI) and computer vision, is revolutionizing the agricultural sector by automating labor-intensive tasks. Computer vision, in particular, uses advanced computational models to analyze visual cues from images, enabling the development of autonomous systems capable of performing tasks that often exceed the capabilities of the human visual system. In the context of robotic weeding, computer vision facilitates the precise identification of crops and weeds, allowing targeted herbicide application on weeds. To integrate these technologies high-quality datasets are critical component for development of accurate and robust models. So to address this need, the comprehensive MH-Weed16 Image Dataset is created from soybean fields of Maharashtra region located in India. Data acquisition was conducted from July 2023 to November 2023, ensuring a diverse natural field conditions. The dataset comprises total 18395 images of 16 weed species annotated under the guidance of subject-matter experts from agriculture universities. This dataset aims to serve as a foundational resource for training and evaluating machine learning and deep learning models. It will facilitate computer vision tasks of object detection and classification. Additionally, the dataset includes a total of 7,577 representative samples of crops along with weeds, categorized into three folders, with 6,656 weed samples annotated using bounding boxes. The images of crop with weed are captured from a top-down view to ensure accurate weed area estimation based on the bounding box areas. The MH-Weed16 dataset represents a significant step forward in the integration of technology for weed management strategies contributing towards sustainable agriculture practices
DOI: 10.15468/dl.s225up
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aedes aegypti (Linnaeus, 1762)",
 "Country is Bhutan",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033383-241126133413365/datasets/export for details.
DOI: 10.17632/h4vn2c9gkz.1
title: Socio-Demographic

description: A socio-demographic profile refers to the statistical characteristics of a population or group. It typically includes data on various factors such as:
Age: This can provide insights into the distribution of different age groups within a population, such as children, working adults, and the elderly.
Gender: It refers to the male-to-female ratio in a population and may include gender identity considerations.
Marital Status: Information on whether individuals are single, married, divorced, or widowed
Education: This refers to the level of education achieved, such as high school, undergraduate, or postgraduate education.
Occupation: Details about the kinds of work people engage in, such as their employment status (e.g., employed, unemployed, self-employed) and the sectors they work in (e.g., agriculture, services, manufacturing).
Income Level: This indicates the economic status of individuals or households, often categorized into income brackets.
Ethnicity and Race: This refers to the cultural, ethnic, or racial groups within a population.
Religion: Information about the religious affiliations or beliefs that individuals or communities adhere to
Geographic Location: Where the population resides, such as urban or rural areas, or specific regions within a country.
Household Size: The number of people living in a single household.
DOI: 10.17632/c5xgy3m85w.1
title: E-CIGARETTE USE ON ACADEMIC OF GEN Z

description: This sheet contains raw data from the questionnaire results, including questions about behaviors and views related to e-cigarette use, as well as personal information such as age, gender, and residence.
DOI: 10.17632/5xrhzzdybb.1
title: Characterization of PEA-loaded filmse materials and indicators related to przewinia fruit preservation

description: Characterization of PEA-loaded filmse materials and indicators related to przewinia fruit preservation
DOI: 10.17632/6j8xr5gst8.1
title: MM research data

description: A Review article of the risk of infections associated with the use of Bispecific antibodies in Multiple myeloma patients. The uploaded file contains the references where I got the data for my manuscript
DOI: 10.17632/z5nb869fhh.1
title: Colaborações entre Governo Federal Brasileiro e Organização da Sociedade Civil de Interesse Público

description: Esse conjunto de dados tem o objetivo de analisar os valores financeiros transferidos pelo governo federal brasileiro às Organização da Sociedade Civil de Interesse Público (OSCIPs), no âmbito de colaborações formais. Esses dados viabilizam análises de recursos transferidos, objetos dos instrumentos (convênios, contratos, termos de parceria, termos de fomento etc), órgãos que transferem mais recursos etc.
DOI: 10.5284/1124322
title: Images from an Evaluation and Open Area Excavation near Sowerby Sports Village, Gravel Hole Lane, Sowerby, North Yorkshire 2018

description: This collection comprises images from an evaluation and open area excavation at land off Sowerby Sports Village, Gravel Hole Lane, Sowerby, North Yorkshire undertaken by On-Site Archaeology Ltd. in March and December 2018.
DOI: 10.5281/zenodo.14422427
title: Voyage data for the manuscript "Ship-based lidar evaluation of Southern Ocean clouds in the storm-resolving general circulation model ICON, and the ERA5 and MERRA-2 reanalyses"

description: This repository contains voyage data for the manuscript “Ship-based lidar evaluation of Southern Ocean clouds in the storm-resolving general circulation model ICON, and the ERA5 and MERRA-2 reanalyses” (DOI: 10.5281/zenodo.14071808), which have not already been published elsewhere. It includes ceilometer, radiosonde and surface observations collected by the University of Cantebrury, Christchurch, Aotearoa/New Zealand and collaborators on the following Southern Ocean voyages [abbreviation, ship name (time period, track)]:



AA15-16: RSV Aurora Australis (22 October 2015–22 February 2016, Hobart, Tasmania, AU–East Antarctica–Macquarie Island–Hobart, Tasmania, AU)

HMNZSW16: HMNZS Wellington (20 November 2016–20 December 2016, Auckland, NZ–Ross Sea–Auckland, NZ)

NBP1704: RV Nathaniel B. Palmer (11 April 2017–14 June 2017, Lyttelton, NZ–Terra Nova Bay–Lyttelton, NZ)

TAN1502: RV Tangaroa (20 January 2015–12 March 2015, Wellington, NZ–Ross Island–Wellington, NZ)

TAN1702: RV Tangaroa (16 March 2017–1 April 2017, Wellington, NZ–Campbell Plateau–Wellington, NZ)


See also Table 1 in the manuscript for more information.

Included are data from the following instruments (abbreviation, instrument name, file format):



chm15k: Lufft CHM 15k ceilometer (NetCDF)

cl51: Vaisala CL51 ceilometer (DAT)

rs: Radiosonde profiles (MATLAB and InterMet Systems)

surf: Surface observations from the ship information system and weather instruments (CSV, MATLAB and NetCDF)


The ZIP archives in this repository are named “voyage abbreviation_instrument.zip”.

The ceilometer files can be processed with the ALCF (https://alcf.peterkuma.net, DOI: 10.5281/zenodo.3774785, and https://github.com/alcf-lidar/alcf). The CL51 files can be converted to NetCDF with cl2nc (https://github.com/peterkuma/cl2nc, DOI: 10.5281/zenodo.4409715). The InterMet Systems radiosonde files can be converted to NetCDF with rstool (https://github.com/peterkuma/rstool, DOI: 10.5281/zenodo.5040217).

If you use the data in this repository, the following attribution is required:



AA15-16: Australian Antarctic Division and University of Canterbury.

HMNZSW16: Royal New Zealand Navy and University of Canterbury.

NBP1704: National Science Foundation, Cooperative Institute for Research in Environmental Sciences, University of Colorado and University of Canterbury.

TAN1502 and TAN1702: National Institute of Water and Atmospheric Research and University of Canterbury.


See also the Acknowledgements section in the manuscript for details.
DOI: 10.5281/zenodo.14422428
title: Voyage data for the manuscript "Ship-based lidar evaluation of Southern Ocean clouds in the storm-resolving general circulation model ICON, and the ERA5 and MERRA-2 reanalyses"

description: This repository contains voyage data for the manuscript “Ship-based lidar evaluation of Southern Ocean clouds in the storm-resolving general circulation model ICON, and the ERA5 and MERRA-2 reanalyses” (DOI: 10.5281/zenodo.14071808), which have not already been published elsewhere. It includes ceilometer, radiosonde and surface observations collected by the University of Cantebrury, Christchurch, Aotearoa/New Zealand and collaborators on the following Southern Ocean voyages [abbreviation, ship name (time period, track)]:



AA15-16: RSV Aurora Australis (22 October 2015–22 February 2016, Hobart, Tasmania, AU–East Antarctica–Macquarie Island–Hobart, Tasmania, AU)

HMNZSW16: HMNZS Wellington (20 November 2016–20 December 2016, Auckland, NZ–Ross Sea–Auckland, NZ)

NBP1704: RV Nathaniel B. Palmer (11 April 2017–14 June 2017, Lyttelton, NZ–Terra Nova Bay–Lyttelton, NZ)

TAN1502: RV Tangaroa (20 January 2015–12 March 2015, Wellington, NZ–Ross Island–Wellington, NZ)

TAN1702: RV Tangaroa (16 March 2017–1 April 2017, Wellington, NZ–Campbell Plateau–Wellington, NZ)


See also Table 1 in the manuscript for more information.

Included are data from the following instruments (abbreviation, instrument name, file format):



chm15k: Lufft CHM 15k ceilometer (NetCDF)

cl51: Vaisala CL51 ceilometer (DAT)

rs: Radiosonde profiles (MATLAB and InterMet Systems)

surf: Surface observations from the ship information system and weather instruments (CSV, MATLAB and NetCDF)


The ZIP archives in this repository are named “voyage abbreviation_instrument.zip”.

The ceilometer files can be processed with the ALCF (https://alcf.peterkuma.net, DOI: 10.5281/zenodo.3774785, and https://github.com/alcf-lidar/alcf). The CL51 files can be converted to NetCDF with cl2nc (https://github.com/peterkuma/cl2nc, DOI: 10.5281/zenodo.4409715). The InterMet Systems radiosonde files can be converted to NetCDF with rstool (https://github.com/peterkuma/rstool, DOI: 10.5281/zenodo.5040217).

If you use the data in this repository, the following attribution is required:



AA15-16: Australian Antarctic Division and University of Canterbury.

HMNZSW16: Royal New Zealand Navy and University of Canterbury.

NBP1704: National Science Foundation, Cooperative Institute for Research in Environmental Sciences, University of Colorado and University of Canterbury.

TAN1502 and TAN1702: National Institute of Water and Atmospheric Research and University of Canterbury.


See also the Acknowledgements section in the manuscript for details.
DOI: 10.5281/zenodo.14446869
title: Precipitation and Water Depth Data
DOI: 10.5281/zenodo.14446868
title: Precipitation and Water Depth Data
DOI: 10.5281/zenodo.14204323
title: De-identified Covid-19 Data (Portugal)
DOI: 10.5281/zenodo.14204322
title: De-identified Covid-19 Data (Portugal)
DOI: 10.5281/zenodo.14445570
title: UFPR-Close-Up

description: Scientific data review
DOI: 10.5281/zenodo.14445571
title: UFPR-Close-Up

description: Scientific data review
DOI: 10.15468/dl.rrdy88
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hydrogonium inflexum (Duby) P.C.Chen"
 ]
}

 The dataset includes 38 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033384-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.