cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17139917
title: Integrating time-series analysis and deep learning method to reconstruct the RTS dynamics in the Tibetan Plateau over  the past nearly four decades
title: RTS

description: Retrogressive thaw slumps (RTSs) represent a severe form of permafrost degradation exacerbated by climate warming. Current automated monitoring methods primarily depend on object-based deep learning (DL) approaches using very high-resolution (VHR) remote sensing imagery and spectral trajectory time-series analysis applied to medium-resolution imagery. DL methods are constrained by publicly available VHR data and computational limitations, their application at large areas in decadal scale remains challenging, whereas time-series analysis often suffers from low detection accuracy due to noise caused by climate fluctuation or human activities. Here, we propose a novel framework that integrates time-series analysis with DL method. The algorithm first conducts a pixel-based detection of surface disturbance through analysis of abrupt changes in vegetation indices derived from Landsat images. Then, the features of these disturbed clusters in VHR imagery are analyzed to confirm the RTS occurrence by using an object-based DL method
DOI: 10.23728/b2share.f4afc600080a4f3ea857ad0b84a49572
title: LTER Zöbelboden (Austria) - meteorological data 2023

description: LTER Zöbelboden (Austria) - meteorological data 2023
DOI: 10.17630/a2ae04f3-2e28-4277-ba3a-cea7fb682f71
title: The Shocking Stars - Numerical modelling of stellar feedback in Giant Molecular Clouds (thesis data)

description: The attached data files underpin Chapter 5 of the PhD thesis "The Shocking Stars - Numerical modelling of stellar feedback in Giant Molecular Clouds". The following file types and formats are included:
- Simulation input files: .in (can be opened with any code editor)
- Simulation setup files: .setup (can be opened with any code editor)
- Simulation binary dumpfiles: _00000 (can be opened with SPH data visualizers)
- Simulation analysis output files: .dat (can be opened with a text editor)
- Python analysis files: .py (can be opened with a code editor)
More details on the software required are provided in the ReadMe.txt file.

Data underpinning Chapter 3 are available at: https://doi.org/10.17630/5f1e2d5a-bea2-4a69-bcf4-f1f27f96a5fa

Data underpinning Chapter 4 are available at: https://doi.org/10.17630/20d675b9-9ce7-4a1e-b4fe-2cbc34a99b0e
DOI: 10.23728/b2share.e86528d16ae64b65ade1c996c646858c
title: LTER Zöbelboden (Austria) - meteorological data 2024

description: LTER Zöbelboden (Austria) - meteorological data 2024
DOI: 10.23728/b2share.8f0fdd0163f044a082f8c2571205aaaa
title: LTER Zöbelboden (Austria) - meteorological data 1996

description: Zöbelboden (Austria) - meteorological data 1996
DOI: 10.15468/dl.yb2hx8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cultellus scalprum",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010283-250914085247600/datasets/export for details.
DOI: 10.15468/dl.2ufz8j
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Mastacembelus dayi)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010284-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.erxscv
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xanthium saccharatum",
 "Country is Austria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 30 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010285-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qm4adj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is one of (Narcissus pseudonarcissus, Oenanthe crocata, Ophioglossum vulgatum, Orchis mascula, Oxalis acetosella, Persicaria amphibia, Persicaria bistorta, Persicaria hydropiper, Persicaria maculosa, Phleum pratense, Phyllitis scolopendrium, Pinus sylvestris, Plantago lanceolata, Plantago major, Plantago media, Poa annua, Poa nemoralis, Poa pratensis agg., Poa trivialis, Polygonum arenastrum, Polypodium agg., Populus tremula, Potentilla anserina, Potentilla erecta, Potentilla reptans, Potentilla sterilis, Primula veris, Primula vulgaris, Primula x polyantha, Prunella vulgaris, Prunus avium, Prunus cerasifera, Prunus laurocerasus, Prunus spinosa, Pteridium aquilinum, Pulicaria dysenterica, Quercus robur, Ranunculus acris, Ranunculus bulbosus, Ranunculus ficaria, Ranunculus flammula, Ranunculus repens, Ranunculus sardous, Rhododendron ponticum, Ribes rubrum, Ribes uva-crispa, Rorippa nasturtium-aquaticum, Rosa agg., Rosa arvensis, Rosa canina agg.)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010252-250914085247600/datasets/export for details.
DOI: 10.2909/3641fdef-fb65-42a6-b6ec-92d168a4191b
title: Impervious Built-Up Change 2018-2021 (raster 20 m), Europe, 3-yearly, Jun 2025

description: The Impervious Built-Up Change (IBUC) 2018-2021 layer is part of the High Resolution Layer (HRL) Imperviousness and provides categorical information on the change of built-up per pixel as derived from the difference between the Impervious Built-Up (IBU) status layers for the reference years 2018 and 2021. The production of the HRL Imperviousness is coordinated by EEA in the frame of Copernicus, the Earth observation component of the European Union’s Space programme.

The product is a raster dataset with 20-meter grid spacing (spatial resolution) that covers the 38 Eionet member and cooperating countries as well as the United Kingdom (i.e. EEA38+UK). It is distributed as 100 x 100 km tiles that are fully conformant with the EEA reference grid.
DOI: 10.15468/dl.96vxwz
title: Occurrence Download

description: A dataset containing 179 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ensis arcuatus",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 179 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010286-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.3n0uccoe
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (911 records)
DOI: 10.17630/1e0a2c75-bcbe-4fff-9e4f-7cd67249eea4
title: Green chemistry-assisted synthesis of metal nanoparticle and fabrication of microstructurally engineered conductive and endurable M@PEO Functional Films (dataset)

description: The open data set includes experimental and theoretical findings from five characterization techniques. 
The article published shows development of Polyethylene oxide based composite film formation using metal nanoparticles. These metal nanoparticles are synthesized using bio herbs like tea and neem leaves.
The data set includes:
1. Thermal characterization (using differential thermal analysis) of the PEO based films. The data is consolidated as EXcell (.csv) file which could be accessed easily using windows/linux processing system
2. X-ray diffraction analysis data reveals the phase formation and purity of PEO-metal based films. The data could be opened using origin software
3. Formation of metal nanoparticles has been studied using UV visible spectroscopy and could be opened using origin software
4. In order to study the interaction of Metal nanoparticles with phytochemicals of each herb type- theoretical study done using DFT. Data could be seen consolidated in orgin file
5. The AC conductivity, dielectric and Nyquist study was up taken using Solatron and could be visible through origin software.
DOI: 10.15468/dl.33cbrt
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010287-250914085247600/datasets/export for details.
DOI: 10.15468/dl.fuzqdt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Benthophilus leobergius)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010289-250914085247600/datasets/export for details.
DOI: 10.6084/m9.figshare.30146242.v1
title: Genetic variation and genome-enabled prediction of white lupin frost resistance in different reference populations

description: Genotypic data from two white lupin reference populations: landrace and cultivar genotypes (reference population 1) and inbred lines (reference population 2).
DOI: 10.6084/m9.figshare.30146242
title: Genetic variation and genome-enabled prediction of white lupin frost resistance in different reference populations

description: Genotypic data from two white lupin reference populations: landrace and cultivar genotypes (reference population 1) and inbred lines (reference population 2).
DOI: 10.5281/zenodo.17140578
title: Atmospheric and oceanic data from a triangle-shaped moored array in the northern South China Sea in 2016

description: This work presents a triangle-shaped moored array dataset comprising three buoys and two moorings with synchronous atmospheric and oceanic data in the northern South China Sea during 2016. The moored array was deployed at late July to early August and recovered at October. The atmospheric data were observed by meteorological sensors and automatic meteorological stations ~2.5 m above sea surface at the buoys. The oceanic data consist temperature and salinity using conductivity, temperature, and depth (CTD) recorders or temperature sensors. It also includes currents observed by acoustic Doppler current profilers (ADCPs) and current meters. The data reveal air–sea interactions and oceanic processes in the upper and bottom ocean. Multiscale processes were recorded, such as air–sea fluxes, tides, internal waves, and low-frequency flows and variations. The data are valuable and may have a lot of potential applications, including analyzing the phenomena and mechanisms of air–sea interactions and ocean dynamics as well as validating and improving numerical model simulations, data reanalysis, and assimilations.
DOI: 10.5281/zenodo.17141354
title: Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings

description: Psycholinguistics framework

It includes the results of the experiment and the framework with the code.

English Familiarity results:

It is located in the folder familiarity_english

Framework code:

Features



Run esitamtion of word features and fine-tunings using OpenAI models, Gemini models, Hugging Face models using HF API or local deployment

Fine-tune new versions of LLMs


Setting Up the framework



Copy apis_example.env to apis.env and fill it with your model credentials

Install dependencies with pip install -r requirements.txt


Setting Up a New Experiment

Initial configuration



Create a folder with the name of the experiment, add the data in XLSX format in the data subfolder, and possible prompts in TXT format in the prompts subfolder.

Create a config.yaml file in the experiment folder using the content of config_example.yaml as a template.


Make estimations with some model (including fine-tuning models):



Prepare the experiment by running python3 prepare_experiment.py <EXPERIMENT_PATH> <EXPERIMENT NAME>. -> generates the batches

Save the batches.jsonl files in batches folder

Run the experiment by executing python3 execute_experiment.py <EXPERIMENT_PATH> <EXPERIMENT NAME>. -> executes the batches.


Processing results:



Save the results of batches in results folder

Combine all the batches in a file called batches.jsonl

Combine all the results files in a file called results.jsonl


You can use these commands:

cat batches/*.jsonl >> batches/batches.jsonl
cat results/*.jsonl >> results/results.jsonl




Execute python3 generateResults.py <EXPERIMENT_PATH> {json, num, weighted_sum} [extra-otpions] for the experiment -> for your own experiment is possible that you want to modify it. We recommend creating a new file for reproducibility -> it generates a .xlsx with the results


Make a fine-tuning:



Prepare the fine-tuning dataset by running python3 create_finetuning_dataset.py <EXPERIMENT_PATH> <EXPERIMENT NAME>.

Fine-tune the model by running python3 execute_finetune.py <EXPERIMENT_PATH> <FT_NAME>.

Calculate the correlation by running python3 calculate_correlation.py <EXPERIMENT_PATH>.

To test the finetuning execute the same steps mentioned before.


Other scripts:



execute_individual_api_{google/openai}.py: Instead of batch operations executes individual calls to the model API

combine_excels.py: It makes a left join of file1.xlsx and file2.xlsx using two columns and generates a new excel with the same name as file1.xlsx (saving the initial one as file1_old.xlsx) python3 combine_excels.py <file1.xlsx> <file2.xlsx> <Column-file1,Column-file2> <suffix for columns of file2.xlsx>
DOI: 10.5281/zenodo.17141353
title: Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings

description: Psycholinguistics framework

It includes the results of the experiment and the framework with the code.

English Familiarity results:

It is located in the folder familiarity_english

Framework code:

Features



Run esitamtion of word features and fine-tunings using OpenAI models, Gemini models, Hugging Face models using HF API or local deployment

Fine-tune new versions of LLMs


Setting Up the framework



Copy apis_example.env to apis.env and fill it with your model credentials

Install dependencies with pip install -r requirements.txt


Setting Up a New Experiment

Initial configuration



Create a folder with the name of the experiment, add the data in XLSX format in the data subfolder, and possible prompts in TXT format in the prompts subfolder.

Create a config.yaml file in the experiment folder using the content of config_example.yaml as a template.


Make estimations with some model (including fine-tuning models):



Prepare the experiment by running python3 prepare_experiment.py <EXPERIMENT_PATH> <EXPERIMENT NAME>. -> generates the batches

Save the batches.jsonl files in batches folder

Run the experiment by executing python3 execute_experiment.py <EXPERIMENT_PATH> <EXPERIMENT NAME>. -> executes the batches.


Processing results:



Save the results of batches in results folder

Combine all the batches in a file called batches.jsonl

Combine all the results files in a file called results.jsonl


You can use these commands:

cat batches/*.jsonl >> batches/batches.jsonl
cat results/*.jsonl >> results/results.jsonl




Execute python3 generateResults.py <EXPERIMENT_PATH> {json, num, weighted_sum} [extra-otpions] for the experiment -> for your own experiment is possible that you want to modify it. We recommend creating a new file for reproducibility -> it generates a .xlsx with the results


Make a fine-tuning:



Prepare the fine-tuning dataset by running python3 create_finetuning_dataset.py <EXPERIMENT_PATH> <EXPERIMENT NAME>.

Fine-tune the model by running python3 execute_finetune.py <EXPERIMENT_PATH> <FT_NAME>.

Calculate the correlation by running python3 calculate_correlation.py <EXPERIMENT_PATH>.

To test the finetuning execute the same steps mentioned before.


Other scripts:



execute_individual_api_{google/openai}.py: Instead of batch operations executes individual calls to the model API

combine_excels.py: It makes a left join of file1.xlsx and file2.xlsx using two columns and generates a new excel with the same name as file1.xlsx (saving the initial one as file1_old.xlsx) python3 combine_excels.py <file1.xlsx> <file2.xlsx> <Column-file1,Column-file2> <suffix for columns of file2.xlsx>
DOI: 10.15468/dl.crt9jp
title: Occurrence Download

description: A dataset containing 5740 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ensis ensis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Occurrence evidence)",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is one of (Absent)"
 ]
 }
 }
 ]
}

 The dataset includes 5740 records from 155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010292-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zbg4nh
title: Occurrence Download

description: A dataset containing 7768 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Tulipa agenensis"
 ]
}

 The dataset includes 7768 records from 173 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010291-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14706393
title: Mapping the Landscape: A Bibliometric Analysis of AI and Teacher Collaboration in Educational Research
DOI: 10.5281/zenodo.14706394
title: Mapping the Landscape: A Bibliometric Analysis of AI and Teacher Collaboration in Educational Research
DOI: 10.15468/dl.gw4wkt
title: Occurrence Download

description: A dataset containing 52336 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Quercus L."
 ]
}

 The dataset includes 52336 records from 185 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000134-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.67awv8
title: Occurrence Download

description: A dataset containing 52336 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Quercus L."
 ]
}

 The dataset includes 52336 records from 185 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000142-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3nny2h
title: Occurrence Download

description: A dataset containing 52336 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Quercus L."
 ]
}

 The dataset includes 52336 records from 185 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000144-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6pvuz3
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-76.16824 -18.44066,-68.88782 -18.44066,-68.88782 -14.29533,-76.16824 -14.29533,-76.16824 -18.44066))",
 "HasGeospatialIssue is false",
 "TaxonKey is Phalaropus fulicarius (Linnaeus, 1758)",
 "Year 2021-2025"
 ]
}

 The dataset includes 37 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000147-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17617/4.f0
title: Self-organization of rectangular bipyramidal helical columns by supramolecular orientational memory epitaxially nucleated from a Frank-Kasper σ phase

description: NMR measurements and interpretation.
NMR data is shown in figure 6 of the paper.
DOI: 10.25345/c5tm72c9x
title: MassIVE MSV000096900 - AML Stress Granule BioID by Hope lab
title: The files are associated with a manuscript submitted for publication by Tajik et al. where the role of RNA regulators and stress granules in acute myeloid leukemia primary and stem cells, specifically focusing on core stress granule regulator G3BP1 using BioID.
DOI: 10.15468/dl.r7uwv2
title: Occurrence Download

description: A dataset containing 52336 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Quercus L."
 ]
}

 The dataset includes 52336 records from 184 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000146-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.