cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10847668
title: Data from Doppler-Wind Lidar (DWL) measurements at Berlin – Rothenburgstrasse (BEROTH) from 2022-10-05 to 2022-10-24 [RAW]

description: Original data files from DWL measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

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The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.10847647
title: Data from Doppler-Wind Lidar (DWL) measurements at Berlin – Rothenburgstrasse (BEROTH) from 2022-10-06 to 2022-10-24 [RAW]

description: Original data files from DWL measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.10847648
title: Data from Doppler-Wind Lidar (DWL) measurements at Berlin – Rothenburgstrasse (BEROTH) from 2022-10-06 to 2022-10-24 [RAW]

description: Original data files from DWL measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the Urbisphere project. 

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). 

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.17632/ptyjxs8k6d.1
title: PeerJ_MinimumloadthresholdsRT-Data

description: Raw Dataset Study PeerJ for peer review, data include:
- Values of RPE, lactate and peak heart rate
-Biodex data for MVC, only a part of the data is uploaded (on the total of more than 800 files) as they include personal information of the participants. The other files are available on request.
-Exported NIRS data, the probe of reference is the B
- 1 representative participant EMG raw data (due to the large file size). Files are best open using the Noraxon software.
DOI: 10.5281/zenodo.13847752
title: Data from Doppler-Wind Lidar (DWL) measurements at Berlin – Rothenburgstrasse (BEROTH) from 2022-05-20 to 2022-06-22 [RAW]

description: Original data files from DWL measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Janet Barlow, Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the FUTURE project and the Urbisphere project.

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the EPSRC in the project "Fluid dynamics of Urban Tall-building clUsters for Resilient built Environments" (FUTURE, grant agreement No EP/V010166/1) and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005).

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.5281/zenodo.13847753
title: Data from Doppler-Wind Lidar (DWL) measurements at Berlin – Rothenburgstrasse (BEROTH) from 2022-05-20 to 2022-06-22 [RAW]

description: Original data files from DWL measurements.
description: TECHNICAL INFO

No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the urbisphere-Berlin campaign technical documentation.

Files are organised in sub-directories per year and day of year (DOY).

COPYRIGHT NOTICE

Copyright Janet Barlow, Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved.

CREATOR NOTICE

This work is owned by the Principal Investigators (PIs) of the FUTURE project and the Urbisphere project.

ATTRIBUTION NOTICE

The [creation and] curation of this work has been funded by the EPSRC in the project "Fluid dynamics of Urban Tall-building clUsters for Resilient built Environments" (FUTURE, grant agreement No EP/V010166/1) and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005).

DISCLAIMER NOTICE

The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected.

MATERIAL NOTICE

The notices cover data in databases, text and images contained in the work.

MATERIAL URI

Urbisphere project
DOI: 10.17632/wfzpdmc5vx.1
title: Banana_Leaf_Disease_Dataset_Bangladesh

description: The Banana leaf disease dataset dataset comprises 424 images of banana leaves collected in Noakhali, Rangamati and Bandarban,Chittagong, Bangladesh. The dataset’s primary directory is named "Banana_Leaf_Disease_Dataset_Bangladesh" across four classes Cordana, healthy (fresh), sigatoka, and pestalotiopsis. The raw images were initially captured in different formats (JPG, PNG, JPEG) and varied resolutions but were resized to a uniform 320x427 pixels at 72 dpi for model compatibility. The dataset was designed to support deep learning-based classification and detection models. Images were captured using a Xiaomi M2006C3MG mobile camera under natural lighting conditions to replicate real-world agricultural settings. The photographs vary in format, including JPG, PNG, and JPEG, with original dimensions of 320x500, 750x1000, and 2448x3264 pixels at a horizontal and vertical resolution of 96 dpi. To ensure consistency and model compatibility, all images were resized to 320x427 pixels at 72 dpi. Each image was checked for clarity, and samples were selected to represent variations in disease severity.
DOI: 10.15468/dl.vrvsyx
title: Occurrence Download

description: A dataset containing 345 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-38.06486 27.05324,-6.38945 27.05324,-6.38945 45.26999,-38.06486 45.26999,-38.06486 27.05324))",
 "TaxonKey is Myctophum punctatum Rafinesque, 1810"
 ]
}

 The dataset includes 345 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049832-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jbv27gkkgb.1
title: Data for article: The limitations of mouth alcohol detection systems in breath alcohol testing - case reports

description: Breath alcohol analyzers can fail to detect mouth alcohol contamination despite having "slope detection" algorithms, potentially leading to falsely elevated results with serious legal consequences. An analysis of three case reports showed significant anomalies in breath expirograms that strongly suggest undetected mouth alcohol contamination.
DOI: 10.17632/c5wxz88cts.1
title: Opportunism SC

description: Data for the research on opportunism in supply chain
DOI: 10.17632/xdnpyt8jn5
title: Darya Yabui Oasis

description: The monthly average index of images captured by phenology cameras in the 2020
DOI: 10.17632/xdnpyt8jn5.1
title: Darya Yabui Oasis

description: The monthly average index of images captured by phenology cameras in the 2020
DOI: 10.17632/79kdnxdztt.1
title: The unspeakable spectacle of death: Post-lethal violence and the deleterious effects of intentionally recorded homicides

description: Research data: tables, figures, appendices.
DOI: 10.15468/dl.q698tt
title: Occurrence Download

description: A dataset containing 427 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hieracium ramondii Griseb."
 ]
}

 The dataset includes 427 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049833-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/vz4xgybxr2.1
title: DriedFishBD: A Detailed Image Dataset of Common Small-sized Dried Fish Varieties in Bangladesh for Identification and Classification.

description: Type of data: 1024x1024 px images of small-sized dried fishes.
Data format: JPEG.

Contents of the dataset: Images of different varieties of small-sized dried fishes in Bangladesh from (i) single fish, (ii) bulk fish, (iii) fish head, and (iv) fish tail perspectives.

Number of classes: Ten small-sized fish varieties – (1) Baim, (2) Chanda, (3) Chapila, (4) Chela, (5) Chepa, (6) Kachki, (7) Loitta, and (8) Tengra.

Number of images: (A) Total images in Original Dataset = 3,313, and (B) Total images in Augmented Dataset = 9,939.

Distribution of instances: 
(A) Number of images in Original Dataset - (1) Baim = 428 images in total. Single = 106, Bulk = 108, Head = 103, Tail = 111. (2) Chanda = 400 images in total. Single = 100, Bulk = 100, Head = 100, Tail = 100. (3) Chapila = 423 images in total. Single = 121, Bulk = 100, Head = 102, Tail = 100. (4) Chela = 400 images in total. Single = 100, Bulk = 100, Head = 100, Tail = 100. (5) Chepa = 400 images in total. Single = 100, Bulk = 100, Head = 100, Tail = 100. (6) Kachki = 455 images in total. Single = 133, Bulk = 107, Head = 107, Tail = 108. (7) Loitta = 400 images in total. Single = 100, Bulk = 100, Head = 100, Tail = 100. (8) Tengra = 407 images in total. Single= 101, Bulk = 100, Head = 100, Tail = 106. 
(B) Number of images in Augmented Dataset - (1) Baim = 1,284 images in total. Single = 318, Bulk = 324, Head = 309, Tail = 333. (2) Chanda = 1,200 images in total. Single = 300, Bulk = 300, Head = 300, Tail = 300. (3) Chapila = 1,269 images in total. Single = 363, Bulk = 300, Head = 306, Tail = 300. (4) Chela = 1,200 images in total. Single = 300, Bulk = 300, Head = 300, Tail = 300. (5) Chepa = 1,200 images in total. Single = 300, Bulk = 300, Head = 300, Tail = 300. (6) Kachki = 1,365 images in total. Single = 399, Bulk = 321, Head = 321, Tail = 324. (7) Loitta = 1,200 images in total. Single = 300, Bulk = 300, Head = 300, Tail = 300. (8) Tengra = 1,221 images in total. Single = 303, Bulk = 300, Head = 300, Tail = 318. 

Dataset size: (A) Total size of the Original Dataset is 404 MB and the compressed ZIP file size is 391 MB. (B) Total size of the Augmented Dataset is 1.18 GB and the compressed ZIP size is 1.12 GB.

Data acquisition process: Images of various small-sized dried fish categories are captured through high-definition smartphone cameras focusing from different angles.
Data source location: Local wholesale dried fish markets located in different areas of Dhaka, Bangladesh.

Where applicable: Training and evaluating machine learning and deep learning models to identify and classify small-sized dried fish varieties in Bangladesh which can be useful in Food Processing Industry, Fisheries Management, Supply Chain and Logistics, Retail and E-Commerce, Food Safety and Regulatory Compliance, Environmental and Conservation Research, Ethnography and Cultural Studies, Nutritional Science, Artificial Intelligence and Machine Learning, and Agricultural Economics.
DOI: 10.17632/vz4xgybxr2.2
title: DriedFishBD: A Detailed Image Dataset of Common Small-sized Dried Fish Varieties in Bangladesh for Identification and Classification.

description: Type of data: 1024 x 1024 px images of small-sized dried fishes.
Data format: JPEG.

Contents of the dataset: Images of different varieties of small-sized dried fishes in Bangladesh from (i) single fish, (ii) bulk fish, (iii) fish head, and (iv) fish tail perspectives.

Number of classes: Ten small-sized fish varieties – (1) Chanda, (2) Chapila, (3) Chela, (4) Chepa, (5) Guchi, (6) Kachki, (7) Loitta, and (8) Tengra.

Number of images: (A) Total images in the Original Dataset = 3,288, and (B) Total images in the Augmented Dataset = 39,456.

Distribution of instances: 
(A) Number of images in the Original Dataset: 
(1) Chanda: 400 images in total. Single = 100, Bulk = 100, Head = 100, Tail = 100. 
(2) Chapila: 422 images in total. Single = 120, Bulk = 100, Head = 102, Tail = 100. 
(3) Chela: 400 images in total. Single = 100, Bulk = 100, Head = 100, Tail = 100. 
(4) Chepa: 400 images in total. Single = 100, Bulk = 100, Head = 100, Tail = 100. 
(5) Guchi: 427 images in total. Single = 105, Bulk = 108, Head = 103, Tail = 111.
(6) Kachki: 435 images in total. Single = 121, Bulk = 107, Head = 103, Tail = 104.
(7) Loitta: 400 images in total. Single = 100, Bulk = 100, Head = 100, Tail = 100. 
(8) Tengra: 404 images in total. Single = 100, Bulk = 100, Head = 100, Tail = 104. 

(B) Number of images in the Augmented Dataset:
(1) Chanda: 4,800 images in total. Single = 1,200, Bulk = 1,200, Head = 1,200, Tail = 1,200.
(2) Chapila: 5,064 images in total. Single = 1,440, Bulk = 1,200, Head = 1,224, Tail = 1,200.
(3) Chela: 4,800 images in total. Single = 1,200, Bulk = 1,200, Head = 1,200, Tail = 1,200. 
(4) Chepa: 4,800 images in total. Single = 1,200, Bulk = 1,200, Head = 1,200, Tail = 1,200. 
(5) Guchi: 5,124 images in total. Single = 1,260, Bulk = 1,296, Head = 1,236, Tail = 1,332.
(6) Kachki: 5,220 images in total. Single = 1,452, Bulk = 1,284, Head = 1,236, Tail = 1,248.
(7) Loitta: 4,800 images in total. Single = 1,200, Bulk = 1,200, Head = 1,200, Tail = 1,200.
(8) Tengra: 4,848 images in total. Single = 1,200, Bulk = 1,200, Head = 1,200, Tail = 1,248.

Dataset size: (A) Total size of Original Dataset is 403 MB and compressed ZIP file size is 390 MB. (B) Total size of Augmented Dataset is 3.23 GB and compressed ZIP size is 3.01 GB.

Data acquisition process: Images of various small-sized dried fish categories are captured through a high-definition smartphone camera focusing from different angles.
Data source location: Local dried fish markets located in Dhaka, Bangladesh.

Where applicable: Training and evaluating machine learning and deep learning models to identify and classify small-sized dried fish varieties in Bangladesh which can be useful in Food Processing Industry, Fisheries Management, Supply Chain and Logistics, Retail and E-Commerce, Food Safety and Regulatory Compliance, Environmental and Conservation Research, Ethnography and Cultural Studies, Nutritional Science, Artificial Intelligence and Machine Learning, and Agricultural Economics.
DOI: 10.17632/jymv9btms8.1
title: KurdHope: a Dataset for Hope Speech Detection For Kurdish Language

description: The KurdHope dataset is a groundbreaking resource in the field of sentiment analysis and hope speech detection. It comprises over 90,000 news headlines, meticulously scraped from prominent Kurdish news platforms such as NRTTV, Channel8, Zagros, Rudaw, and others. The dataset's primary objective is to facilitate research and development of models capable of detecting hopeful content in news headlines. 

Annotation Process 

Each headline in the dataset has been labeled as either "hope" or "not-hope". The annotation process was conducted manually by three independent annotators to ensure the highest possible accuracy and reliability. Discrepancies were resolved through consensus, resulting in a robust labeling framework. 

Unique Features 

1. Direct Traceability: 
 The dataset provides direct links to the news websites where each headline was originally scraped. This feature ensures transparency and allows researchers to verify the dataset's sources, making it one of the first datasets in this domain with such traceable references. 

2. First of Its Kind: 
 To the best of our knowledge, KurdHope is the first dataset specifically curated for detecting hope speech in Kurdish news headlines. This pioneering effort opens up new research avenues in sentiment analysis, particularly within the context of Kurdish media. 

3. High Utility for Scholars: 
 Scholars and practitioners can leverage this dataset to train and evaluate more advanced models for detecting hopeful content in textual data. The dataset is expected to significantly contribute to the development of sentiment analysis tools that can discern optimism and positivity in media narratives. 

Research Applications 

The KurdHope dataset holds immense potential for a variety of applications: 

- Sentiment Analysis: Enhancing the accuracy of sentiment classification models by incorporating nuanced understanding of hope. 
- Media Studies: Analyzing trends in Kurdish news headlines to understand the prevalence and portrayal of hope. 
- Multilingual NLP: Expanding the scope of sentiment analysis for underrepresented languages like Kurdish. 

In summary, the KurdHope dataset is a transformative resource, providing a solid foundation for developing innovative NLP tools aimed at detecting and amplifying hopeful narratives in media. Its availability marks a significant milestone in computational linguistics and sentiment analysis, particularly in the Kurdish language context.
DOI: 10.17632/7cr2yznw9z.1
title: Indice de Competitividad Departamental - Colombia

description: El arhivo agrupa los indices de competitividad departamental de Colombia, publicados por la Universidad del Rosario y la Camara Colombiana de Competitividad.
En cada hoja se resumen los indices por cada uno de los pilares.
DOI: 10.17632/mbddrpczg5.1
title: Gender Role Perception and Creativity

description: The aim of the study is to investigate the role of gender role perception and gender in the context of the creativity of participants. A 2 (androgynous and sex-typed) × 2 (male and female) factorial design has been adopted in the study. Initially, 600 participants (300 females and 300 males) in the age group ranging from 30-45 years and belonging to middle socioeconomic status were purposively chosen for the study. The education of the participants was fixed at graduation. Based on the median split of their scores in the Sahoo Sex Role Inventory, males were classified into 150 androgynous and 150 sex-typed groups. The females were divided into similar subgroups. 60 participants from each group with extreme scores were only considered for further analysis. Subsequently, the participants (androgynous males, sex-typed males, androgynous females and sex-typed females) were compared with respect to their level of creativity. Findings revealed that the participants endorsing androgynous gender role orientation were more creative than the sex-typed participants, which was supported by the literature. No difference has been obtained between males and females with regard to creativity. Further, no significant interaction effect has been observed between gender role perception and gender. Keywords: androgyny, gender, gender role perception, creativity, sex-type.