cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.kgwna7
title: Occurrence Download

description: A dataset containing 1731870 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Brachyscome exilis, Epacris browniae, Bulbophyllum schillerianum, Euphorbia arvalis, Euphorbia capitulata, Euphorbia chamaesula, Euphorbia crenulata, Euphorbia hypogaea, Euphorbia margalidiana, Euphorbia pediculifera, Euphorbia rhabdotosperma, Euphorbia saratoi, Euphorbia scandens, Hedera maroccana, Ophrys kotschyi, Ophrys nouletii, Thysanotus rectantherus, Thysanotus acerosifolius, Thysanotus glaucus, Freesia speciosa, Ceropegia stapeliiformis, Elatine hydropiper, Moehringia markgrafii, Ptilotus procumbens, Linaria ficalhoana, Linaria weilleri, Polygonum karacae, Chenopodium suecicum, Epacris obtusifolia, Lotus tenuis, Osmitopsis nana, Brachyscome simulans, Cornus unalaschkensis, Wahlenbergia schlechteri, Wahlenbergia tomentosula, Wahlenbergia compacta, Wahlenbergia namaquana, Wahlenbergia oocarpa, Lotus longisiliquosus, Linaria subbaetica, Polygonum mersinicum, Ptilotus grandiflorus, Euphorbia valliniana, Linaria musilii, Linaria depauperata, Helianthemum bramwelliorum, Helianthemum bystropogophyllum, Paeonia saueri, Epacris apiculata, Epacris coriacea, Belloa chilensis, Brachyscome microcarpa, Ferula daninii, Ferula orientalis, Ferula palmyrensis, Ferula sauvagei, Euphorbia amygdaloides, Euphorbia arizonica, Euphorbia exigua, Euphorbia glareosa, Euphorbia herrei, Euphorbia sulcata, Ophrys apicula, Ophrys atlantica, Ophrys torrensis, Ephedra trifurcata, Paeonia parnassica, Lotus corniculatus, Lotus emeroides, Lotus hirsutus, Trichodesma ehrenbergii, Ophrys salvatoris, Valantia deltoidea, Anthotroche myoporoides, Arctostaphylos glutinosa, Arctostaphylos incognita, Bupleurum exaltatum, Bupleurum falcatum, Bupleurum leucocladum, Bupleurum nanum, Bupleurum anatolicum, Brachyscome dichromosomatica, Ferula hermonis, Hemigenia humilis, Euphorbia bubalina, Euphorbia caudiculosa, Euphorbia clava, Euphorbia denticulata, Euphorbia nevadensis, Euphorbia pentops, Euphorbia physocaulos, Euphorbia polygalifolia, Euphorbia salicifolia, Hemigenia tysonii, Ophrys fernandii, Thysanotus speckii, Freesia leichtlinii, Cynosurus cristatus, Ceropegia macmasteri)",
 "Year is greater than or equal to 1950"
 ]
}

 The dataset includes 1731870 records from 4193 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018656-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12566384
title: Dataset: Vintage Wine Estates, Inc. (VWEWW) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12566385
title: Dataset: Vintage Wine Estates, Inc. (VWEWW) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.7488/ds/7876
title: SUPERSEDED: 3D-printable phantoms for quantitative dynamic contrast-enhanced MRI

description: This dataset contains the experimental data and code required to reproduce results and figures in the article "3D-printable phantoms for quantitative dynamic contrast-enhanced MRI". Abstract ----------- Purpose: A novel 3D-printed phantom design and methodology is proposed, addressing important requirements for technical validation, quality assurance and multi-site harmonisation of quantitative dynamic contrast-enhanced (DCE-) MRI measurements. Methods: Phantoms were produced by 3D-printing gels incorporating channels and pores as proxies for blood vessels and extravascular extracellular space, respectively. A flow circuit was designed to reproduce clinically relevant arterial input functions (AIF). Using nine gels with variable porosity and channel size we evaluated the effect of 3D-printing parameters on DCE-MRI parameters obtained using the extended Tofts (ET) model. Physical gel and fitted model parameters were correlated by multiple linear regression. Results: All phantoms generated realistic AIFs and tissue-like signal enhancement curves were accurately modelled by the ET model. As hypothesised, vp was positively associated with vchan (B = 0.86, p &lt; 0.001) and showed a weaker, negative association with vpore (B = -0.18, p = 0.006); PS was positively associated with both vchan (B = 0.13 min-1, p &lt; 0.001) and vpore (B = 0.051 min-1, p &lt; 0.001). ve was positively associated with vpore (B = 0.89, p &lt; 0.001) but not vchan. Fitted parameters were repeatable (coefficient of variation 2.1 - 3.2 %). Conclusions: Tailorable 3D printed porous gel phantoms generating tissue-mimicking DCE-MRI signals have the potential to support validation, quality assurance and multi-site harmonisation of quantitative DCE-MRI.
description: Refer to readme.txt file.
DOI: 10.7488/ds/7924
title: What's Right with PE: Exploring Positive Narratives in Physical Education

description: The dataset is a series of 5 narratives developed from discussions between PE practitioners from Scotland, England, Ireland, Canada and Norway. Their discussions were around ‘what’s right’ and ‘what’s wrong’ with PE. Full details of the analysis process can be found in the following article (just accepted for publication). Gray, S., Lambert, K. & Young, L. (accepted April 2025). What's Right with PE: Exploring Positive Narratives in Physical Education. European Physical Education Review. For decades, physical education (PE) scholars have called for a change to how PE is conceptualised and enacted, highlighting that its current (and persistent) form and focus - organised around physical activities and sport - are not fit for purpose. Much of this change-oriented discourse highlights what is wrong with PE, leading to suggestions about how PE should change in and for the future, for example, by adopting critical approaches and connecting more to the lives of young people. While we do not disagree with these perspectives and ideas, it is important to note that, in general, they have had little impact on PE curricula or pedagogy. In this paper we suggest an alternative, strengths-based approach. Drawing from discussions with a range of professionals from the PE community (teachers, undergraduate and postgraduate pre-service teachers and teacher educators) across five national contexts - Canada, England, Ireland, Norway and Scotland - we generated narratives about ‘what’s right with PE’. The narratives highlighted that PE can be ‘fit for purpose’ when it connects to the wider school and community, when everyone has a shared understanding of its purpose, and when PE teachers enact a broad, holistic and inclusive curriculum. We present the narratives as a reflective tool, encouraging all professionals within the PE community to consider how they align with (or against) their current experiences. We hope that these reflections facilitate critical thinking and problem solving to ensure that the subject is (and remains) fit for purpose now and in the future.
DOI: 10.17909/cve4-9p26
title: JWST data for Chandra SHELLQs paper

description: JWST-NIRSpec data for four narrow-line SHELLQs objects observed with Chandra
DOI: 10.6096/mistrals-hymex-moose.388
title: 0-250 m ocean temperature, mooring line, Lion Buoy

description: Routine operation over a period shorter than 5 years (Enhanced Observation Period, EOP)
DOI: 10.15468/dl.wu32nj
title: Occurrence Download

description: A dataset containing 13529 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Arthrobacter Conn & Dimmick, 1947"
 ]
}

 The dataset includes 13529 records from 335 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003043-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15525291
title: Stabilizing Stock Prices through ESG Data set

description: The data for the STATA .
DOI: 10.5281/zenodo.15525292
title: Stabilizing Stock Prices through ESG Data set

description: The data for the STATA .
DOI: 10.5281/zenodo.13985740
title: IRHMapNet Radargram and Mask Patches Dataset.

description: # README: IRHMapNet Radargram and Mask Patches Dataset

## Dataset Overview

This dataset contains radargram patches and corresponding mask patches used for training and evaluating the **IRHMapNet** model. The dataset is designed for segmentation of internal reflection horizons (IRHs) from radio-echo sounding data. The data is organized into two directories: radargram patches (`grams_patches`) and mask patches (`masks_patches`), with each patch having dimensions of 512x512 pixels.

### Contents

- **grams_patches/**: Contains 600 `.csv` files representing radargram patches. Each file is a 512x512 matrix corresponding to a small section of the radargram image.- **masks_patches/**: Contains 600 `.csv` files representing the ground-truth mask patches for segmentation. Each file is a 512x512 binary mask, where `1` indicates the presence of an internal reflection horizon (IRH), and `0` represents background or ice.

## Data Format

- The files in both directories are named consistently, with matching pairs of radargram and mask patches.  - Example: `grams_patches/patch_001.csv` corresponds to `masks_patches/patch_001.csv`.- Each `.csv` file is a comma-separated values (CSV) file containing 512 rows and 512 columns.

## Directory Structure

```DATA_IRHMapNet/├── grams_patches/      # Radargram patches│   ├── patch_001.csv│   ├── patch_002.csv│   └── ... (600 patches)└── masks_patches/      # Mask patches (Ground truth)    ├── patch_001.csv    ├── patch_002.csv    └── ... (600 patches)```

## Usage Instructions

1. **Loading the data**: Each `.csv` file can be loaded using standard CSV reading functions in Python, such as `numpy.loadtxt()` or `pandas.read_csv()`.      Example in Python using `numpy`:   ```python   import numpy as np   radargram_patch = np.loadtxt('grams_patches/patch_001.csv', delimiter=',')   mask_patch = np.loadtxt('masks_patches/patch_001.csv', delimiter=',')   ```

2. **Model training**: These patches are designed for input into a U-Net or similar convolutional neural network architectures for pixel-wise classification tasks. The radargram patches serve as input, and the mask patches provide the ground-truth labels for training.

## License

This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to:

    Share — copy and redistribute the material in any medium or format.    Adapt — remix, transform, and build upon the material for any purpose, even commercially.

You must give appropriate credit by citing the following publication:

**Citation**: Moqadam, H., et al.  (2024). Going deeper with deep learning: Automatically tracing internal reflection horizons in ice sheets. *Journal of Geophysical Research: Machine Learning and Computation*. DOI: [insert DOI]

## Contact

For questions or further information, please contact Hameed Moqadam at [hameed.moqadam@awi.de].    Data Curator: Hameed Moqadam    Annotator: Hameed Moqadam    Data Manager: Hameed Moqadam
DOI: 10.5281/zenodo.13985741
title: IRHMapNet Radargram and Mask Patches Dataset.

description: # README: IRHMapNet Radargram and Mask Patches Dataset

## Dataset Overview

This dataset contains radargram patches and corresponding mask patches used for training and evaluating the **IRHMapNet** model. The dataset is designed for segmentation of internal reflection horizons (IRHs) from radio-echo sounding data. The data is organized into two directories: radargram patches (`grams_patches`) and mask patches (`masks_patches`), with each patch having dimensions of 512x512 pixels.

### Contents

- **grams_patches/**: Contains 600 `.csv` files representing radargram patches. Each file is a 512x512 matrix corresponding to a small section of the radargram image.- **masks_patches/**: Contains 600 `.csv` files representing the ground-truth mask patches for segmentation. Each file is a 512x512 binary mask, where `1` indicates the presence of an internal reflection horizon (IRH), and `0` represents background or ice.

## Data Format

- The files in both directories are named consistently, with matching pairs of radargram and mask patches.  - Example: `grams_patches/patch_001.csv` corresponds to `masks_patches/patch_001.csv`.- Each `.csv` file is a comma-separated values (CSV) file containing 512 rows and 512 columns.

## Directory Structure

```DATA_IRHMapNet/├── grams_patches/      # Radargram patches│   ├── patch_001.csv│   ├── patch_002.csv│   └── ... (600 patches)└── masks_patches/      # Mask patches (Ground truth)    ├── patch_001.csv    ├── patch_002.csv    └── ... (600 patches)```

## Usage Instructions

1. **Loading the data**: Each `.csv` file can be loaded using standard CSV reading functions in Python, such as `numpy.loadtxt()` or `pandas.read_csv()`.      Example in Python using `numpy`:   ```python   import numpy as np   radargram_patch = np.loadtxt('grams_patches/patch_001.csv', delimiter=',')   mask_patch = np.loadtxt('masks_patches/patch_001.csv', delimiter=',')   ```

2. **Model training**: These patches are designed for input into a U-Net or similar convolutional neural network architectures for pixel-wise classification tasks. The radargram patches serve as input, and the mask patches provide the ground-truth labels for training.

## License

This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. You are free to:

    Share — copy and redistribute the material in any medium or format.    Adapt — remix, transform, and build upon the material for any purpose, even commercially.

You must give appropriate credit by citing the following publication:

**Citation**: Moqadam, H., et al.  (2024). Going deeper with deep learning: Automatically tracing internal reflection horizons in ice sheets. *Journal of Geophysical Research: Machine Learning and Computation*. DOI: [insert DOI]

## Contact

For questions or further information, please contact Hameed Moqadam at [hameed.moqadam@awi.de].    Data Curator: Hameed Moqadam    Annotator: Hameed Moqadam    Data Manager: Hameed Moqadam
DOI: 10.1594/pangaea.982321
title: Passive acoustic monitoring data recorded by recorder AU0085 at mooring AWI251-3 in the Weddell Sea, Southern Ocean, from 2019 to 2021

description: Passive acoustic monitoring (PAM) data were collected by recorder AU0085 of type AURAL (Autonomous Underwater Recorder for Acoustic Listening (AURAL; Model 2, Multi-Électronique) at 61.023° S, 55.978° W, mooring AWI251-3, in the Weddell Sea, Atlantic sector of the Southern Ocean. During a deployment period from February 2019 to April 2022, passive acoustic data were collected from February 2019 to February 2021 by AU0085 as part of the Hybrid Antarctic Float Observing System (HAFOS) in the Weddell Sea. The recorder was moored at 179 m depth and scheduled to record at a duty cycle of 6 min per hour at a sample rate of 32,768 Hz. Further details about the data acquisition and processing of this data set can be found in the accompanying metadata file (see Additional metadata) as well as the data processing report (see Data Processing Report). Passive acoustic data archived here represent data processing Level 1+, according to the standards defined in the associated Standard Operation Procedure (SOP) Glossary (Thomisch et al. 2023a). Further information on data processing with regard to data preparation and standardization can be found in the associated SOP Part 1: Data preparation and standardization (Thomisch et al. 2023b).
description: Each daily .tar contains the 10-minute audio files (.wav format) collected during the respective day.---Please note that this data set may also be listed as AWI251-03_AU0085 at other data portals, such as the Open Portal to Underwater Soundscapes (OPUS), available at https://opus.aq.
DOI: 10.5255/ukda-sn-857826
title: Qualitative Election Study of Britain Party Leader Evaluations Database, 2005-2024

description: In each round of the Qualitative Election study, participants were asked to provide the words and phrases they associate with British political party leaders. The data were collected during pre-election focus groups and interviews conducted with participants from England, Scotland and Wales, during the General Election campaigns of 2010, 2015, 2017, and 2019. For the in person data collection, party leader evaluation pre-election component, participants were provided with head shot pictures of the party leaders (depending on where in Britain they lived) taken from the party’s own website. In 2024 the data collection was moved online and participants filled in the words and phrases as part of the application process to join the study. 

Participants were instructed to write down words or phrases they associate with each person, and indicate if that association was positive, negative or neutral. During the focus groups, the focus group moderator would lead the group in a discussion of the positives, negatives and neutral qualities of each leader. The data included in this dataset only includes the words and phrases written down by the participants, and not the subsequent discussion.

These data are structure for use in sentiment analysis. Each tab contains a column listing participant’s’ words and phrases as a string variable; the next two columns list the election year and leader, affective evaluations (relating to, arising from, or influencing feelings or emotions) as a string variable, and the affective evaluation as a numeric scale from negative –1 to positive +1. These data are suitable for sentiment and discourse analysis, or analytic generalization – establishing that a concept exists within a population regardless of the number of people who hold it. 

The new version contains the 2005 and 2014 supplement data.

Words from the 2005 British General Election are substantively different from the other data. They were collected from the article 'Hearts or Minds: Men, Women and Leader Evaluations in the 2005 General Election' by K Winters, R Campbell in Political Communications: The General Election Campaign of 2005, 184-202. Words coded as 1 indicate the respondent thought the word was important in their considerations.

In addition, a supplementary dataset is provided of leaders’ evaluation data from a study with residents of Dundee after the Scottish Independence Referendum in 2014 (N = 287).
DOI: 10.15468/dl.2jy7cb
title: Occurrence Download

description: A dataset containing 1193 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammillaria heyderi Muehlenpf."
 ]
}

 The dataset includes 1193 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003047-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15046016
title: Dataset for article "Continuous-wave nonlinear polarization control and signatures of criticality in a perovskite cavity"

description: This dataset contains data and code to replicate the figures for the article "Continuous-wave nonlinear polarization control and signatures of criticality in a perovskite cavity" published in Nature Photonics:

Keijsers, G., de Boer, R. M., Verdonschot, B., Peters, K. J. H., Geng, Z. & Rodriguez, S. R. K. Continuous-wave nonlinear polarization control and signatures of criticality in a perovskite cavity. Nat. Photon. (2025). https://doi.org/10.1038/s41566-025-01692-3

https://doi.org/10.1038/s41566-025-01692-3https://www.nature.com/articles/s41566-025-01692-3
DOI: 10.5281/zenodo.15046017
title: Dataset for article "Continuous-wave nonlinear polarization control and signatures of criticality in a perovskite cavity"

description: This dataset contains data and code to replicate the figures for the article "Continuous-wave nonlinear polarization control and signatures of criticality in a perovskite cavity" published in Nature Photonics:

Keijsers, G., de Boer, R. M., Verdonschot, B., Peters, K. J. H., Geng, Z. & Rodriguez, S. R. K. Continuous-wave nonlinear polarization control and signatures of criticality in a perovskite cavity. Nat. Photon. (2025). https://doi.org/10.1038/s41566-025-01692-3

https://doi.org/10.1038/s41566-025-01692-3https://www.nature.com/articles/s41566-025-01692-3
DOI: 10.15468/dl.kkh575
title: Occurrence Download

description: A dataset containing 1096903129 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1096903129 records from 5268 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003025-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57907/mirri/jalryu/5xpcfv
title: MIRRI0116529.json

description: Strain json file
DOI: 10.57907/mirri/nls8bx/isqp6n
title: MIRRI0117179.json

description: Strain json file
DOI: 10.57907/mirri/p5c4o1/piyy5v
title: MIRRI0116494.json

description: Strain json file
DOI: 10.57907/mirri/pveokv
title: MIRRI0117223

description: Organism type: Bacteria ; Taxon name: Campylobacter concisus ; Collection: LMG 7972 ; Country: United Kingdom
DOI: 10.57907/mirri/tsg5sj/ohx8ph
title: MIRRI0116449.json

description: Strain json file
DOI: 10.57907/mirri/uxsomr
title: MIRRI0116446

description: Organism type: Bacteria ; Taxon name: Psychrobacter immobilis ; Collection: LMG 7066 ; Country: Sweden
DOI: 10.57907/mirri/wdpsfy/2vbn2f
title: MIRRI0116495.json

description: Strain json file
DOI: 10.57907/mirri/z4ueme/h2lxm7
title: MIRRI0116453.json

description: Strain json file
DOI: 10.3535/c5v-c6k-wet
title: Diospyros mindanaensis Merr.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/t6x-gm9-ln6
title: Panicum fluviicola Steud.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.57907/mirri/1opksa/dth64l
title: MIRRI0114068.json

description: Strain json file
DOI: 10.57907/mirri/1paof0/ci3tzz
title: MIRRI0115904.json

description: Strain json file
DOI: 10.57907/mirri/91ueli
title: MIRRI0114008

description: Organism type: Bacteria ; Taxon name: Ochrobactrum anthropi ; Collection: LMG 3308 ; Country: France
DOI: 10.57907/mirri/ancfjr/sl2vfd
title: MIRRI0114114.json

description: Strain json file
DOI: 10.57907/mirri/br8x06/s7l3v7
title: MIRRI0116461.json

description: Strain json file
DOI: 10.57907/mirri/djtwn6
title: MIRRI0114063

description: Organism type: Bacteria ; Taxon name: Alcaligenes faecalis ; Collection: LMG 3365 ; Country: Denmark
DOI: 10.57907/mirri/ew9epe/kqvryo
title: MIRRI0114014.json

description: Strain json file
DOI: 10.57907/mirri/hybq3k/vkb2n0
title: MIRRI0115894.json

description: Strain json file
DOI: 10.57907/mirri/ioxt0c/st1f0w
title: MIRRI0115912.json

description: Strain json file
DOI: 10.57907/mirri/l7crgx
title: MIRRI0115905

description: Organism type: Bacteria ; Taxon name: Xanthomonas oryzae ; Collection: LMG 636 ; Country: India