cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17652658
title: FR-MIGR-TWIT Corpus
title: Migration Tweets of French Right- and Left-Wing Politics

description: The MIGR-TWIT Corpus is a multilingual corpus of tweets about the topic of migration in Europe, developed within the framework of the OLiNDiNUM (Observatoire LINguistique du DIscours NUMérique, Linguistic Observatory of Online Debate), with the aim of documenting and analyzing online public discourse on (im)migration in contemporary European politics. Considering the global issue of migration over the last decade (2011–2022), and in order to observe discursive evolution accros the political spectrum and in two national contexts (France and the UK), the MIGR-TWIT Corpus was published: 



MIGR-TWIT Corpus. Migration Tweets of right and far-right politics in Europe (Battaglia, Blandino, Jeon & Pietrandrea, 2022)



UK-R-MIGR-RA-TWIT-2012-2022

FR-R-MIGR-TWIT-2011-2022 






FR-L-MIGR-TWIT Corpus. Migration Tweets of French Left-wing Politics (Pietrandrea & Jeon, 2023)



FR-L-MIGR-TWIT-2011-2022




The FR-MIGR-TWIT Corpus, compiled from the FR-R and FR-L modules, comprises 17,395 tweets posted by 39 French political figures and parties (16 right-wing and 23 left-wing) between 2011 and 2022. Tweets containing migr- derivatives were retrieved via the Twitter API v2 Academic Research, and truncated retweets (>140 characters) were restored through targeted verification (for detailed information on each module see the links above).

The FR-MIGR-TWIT Corpus 1.0 offers:



tweet URLs (tweet_url) and 44 types of data retrieved through the Full Archive Search endpoints of the Twitter API v2, such as the textual content of tweets (data__text), posting date (data__created_at), user ID (data__author_id), number of retweets (data__public_metrics__retweet_count) likes (data__public_metrics__like_count), replies (data__public_metrics__reply_count), quotes (data__public_metrics__quote_count). (Non-exhaustive list)


 

Scientific reference:

 

Jeon, S. (2025). Le discours numérique sur l'immigration en France entre 2011 et 2022. Une analyse de corpus (Online Discourse on Immigration in France between 2011 and 2022. A Corpus Analysis), PhD Thesis, Université de Lille, France.

 

Pietrandrea, P., Battaglia, E. (2022). “Migrants and the EU”. The diachronic construction of ad hoc categories in French far-right discourse. Journal of Pragmatics 192, 139-157.
description: The FR-MIGR-TWIT Corpus 1.0 is distributed under the Creative Commons CC-BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/). Its reuse is permitted for non-commercial purposes, including research and education.
description: Series information

FR-MIGR-TWIT 1.0 is compiled from:





FR-R module (© 2022 Battaglia, Blandino, Jeon & Pietrandrea) and

FR-L module (© 2023 Pietrandrea & Jeon).




Changelog  


– Fixed delimiter issues in the FR-R module (2014, 2016, 2019).

– Added tweet URLs.

– Added XML format.
description: The corpus is availabe in CSV and XML formats.
DOI: 10.3535/078-18t-gkq
title: Carex nigra subsp. nigra

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/079-f1e-r3v
title: Dryopteris cristata (L.) A. Gray

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/078-kcc-k75
title: Polypodium vulgare L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/079-4z1-s9c
title: Actaea spicata L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/079-nm1-erv
title: Carex tenuiflora Wahlenb.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/079-q8x-b02
title: Calamagrostis stricta subsp. groenlandica

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/079-qf1-0v6
title: Agrostis gigantea subsp. gigantea

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/079-rn2-ntk
title: Ribes nigrum L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/079-tn8-6th
title: Carex disperma Dewey

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-0zt-na4
title: Thlaspi arvense L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-4re-fa1
title: Leymus arenarius (L.) Hochst.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-aq6-qs2
title: Plantae

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-cq7-gws
title: Rubus aureolus Allander

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-dlt-tbp
title: Equisetum sylvaticum L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-e2z-bxa
title: Euphorbia peplus L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-emm-xh3
title: Salix pentandra L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.5281/zenodo.17830523
title: DEEP FARM : CACAO use case dataset

description: Cacao, an economic pillar for many tropical countries, is of strategic importance to Côte d'Ivoire, the world's leading producer, supplying over 40% of global output. However, the sector is threatened by diseases, including Cacao Swollen Shoot Virus (CSSV), a viral disease transmitted by mealybugs, which causes rapid decline of cacao trees. Traditional control methods, such as eradication of infected plants, are costly and often ineffective, largely due to the difficulty of early detection.

To address these limitations, the Deep Farm project, supported by an international consortium, proposes the use of innovative technologies such as imaging and artificial intelligence (AI) to improve early detection of CSSV. In Côte d'Ivoire, this project focuses on cacao cultivation and plans to establish a pilot farm, as well as train students in computer science (ESATIC) and agronomy (INP-HB). These students will collect high-quality images to train AI models capable of visually identifying disease symptoms on leaves, stems, and pods.

Acknowledgment:This dataset was collected by the team at ESATIC & INP-HB (Ivory Coast) as part of the Deep Farm Erasmus+ project.



Access to the report is restricted. Interested users may request access via Zenodo for research or educational purposes.
DOI: 10.6084/m9.figshare.30694714
title: Observed decoupling of vegetation greenness and productivity despite regional coupling patterns across the Tibetan Plateau

description: Global vegetation greenness has increased since the 1980s, boosting productivity, facilitating CO<sub>2</sub> absorption, and contributing to climate mitigation. The Tibetan Plateau plays a crucial role in maintaining carbon balance and climate stability. However, the dynamics in the coupling relationship between vegetation greenness and productivity remain poorly understood. In this study, we delved into the coupling relationship between vegetation greenness and productivity across the Tibetan Plateau, utilizing a long time series of MODIS Leaf Area Index (LAI) and Gross Primary Productivity (GPP) data, and further examined how these coupling relationships varied along environmental gradients. The results revealed that 77.9% of the pixels, primarily in northeastern and northern regions, exhibited concurrent increasing trends in both annual mean LAI and GPP during 2000–2021. Conversely, 19.8% of the pixels demonstrated inconsistent trends. Notably, annual mean LAI and GPP displayed a strong coupling strength of 0.73, which weakened during the late growing season. A critical transition threshold was observed, when LAI exceeded 6 m² m<sup>−2</sup>. Additionally, we identified decoupling in the southern and northwestern regions, attributed to pronounced leaf-shading effects and drought adaptation strategies. This study highlights the importance of understanding the coupling relationships of vegetation greenness and productivity in the carbon cycle.
DOI: 10.6084/m9.figshare.30694714.v1
title: Observed decoupling of vegetation greenness and productivity despite regional coupling patterns across the Tibetan Plateau

description: Global vegetation greenness has increased since the 1980s, boosting productivity, facilitating CO<sub>2</sub> absorption, and contributing to climate mitigation. The Tibetan Plateau plays a crucial role in maintaining carbon balance and climate stability. However, the dynamics in the coupling relationship between vegetation greenness and productivity remain poorly understood. In this study, we delved into the coupling relationship between vegetation greenness and productivity across the Tibetan Plateau, utilizing a long time series of MODIS Leaf Area Index (LAI) and Gross Primary Productivity (GPP) data, and further examined how these coupling relationships varied along environmental gradients. The results revealed that 77.9% of the pixels, primarily in northeastern and northern regions, exhibited concurrent increasing trends in both annual mean LAI and GPP during 2000–2021. Conversely, 19.8% of the pixels demonstrated inconsistent trends. Notably, annual mean LAI and GPP displayed a strong coupling strength of 0.73, which weakened during the late growing season. A critical transition threshold was observed, when LAI exceeded 6 m² m<sup>−2</sup>. Additionally, we identified decoupling in the southern and northwestern regions, attributed to pronounced leaf-shading effects and drought adaptation strategies. This study highlights the importance of understanding the coupling relationships of vegetation greenness and productivity in the carbon cycle.
DOI: 10.3535/07a-gfs-cll
title: Anthoxanthum nitens (Weber) Y.Schouten & Veldkamp

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.5281/zenodo.17830522
title: DEEP FARM : CACAO use case dataset

description: Cacao, an economic pillar for many tropical countries, is of strategic importance to Côte d'Ivoire, the world's leading producer, supplying over 40% of global output. However, the sector is threatened by diseases, including Cacao Swollen Shoot Virus (CSSV), a viral disease transmitted by mealybugs, which causes rapid decline of cacao trees. Traditional control methods, such as eradication of infected plants, are costly and often ineffective, largely due to the difficulty of early detection.

To address these limitations, the Deep Farm project, supported by an international consortium, proposes the use of innovative technologies such as imaging and artificial intelligence (AI) to improve early detection of CSSV. In Côte d'Ivoire, this project focuses on cacao cultivation and plans to establish a pilot farm, as well as train students in computer science (ESATIC) and agronomy (INP-HB). These students will collect high-quality images to train AI models capable of visually identifying disease symptoms on leaves, stems, and pods.

Acknowledgment:This dataset was collected by the team at ESATIC & INP-HB (Ivory Coast) as part of the Deep Farm Erasmus+ project.



Access to the report is restricted. Interested users may request access via Zenodo for research or educational purposes.
DOI: 10.3535/07a-hrx-c5v
title: Carex elongata L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-kek-8s2
title: Carex maritima Gunnerus

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-khl-v4g
title: Draba glabella Pursh

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-lec-4a4
title: Atocion rupestre (L.) Oxelman

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-ybt-75b
title: Carex flava × lepidocarpa x flava × lepidocarpa var. lepidocarpa

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-yk3-2wh
title: Saxifraga hirculus L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.5281/zenodo.13907140
title: Enumerated results from C-THRU global petrochemicals model
DOI: 10.5281/zenodo.17989749
title: Enumerated results from C-THRU global petrochemicals model
DOI: 10.6084/m9.figshare.30073278
title: Improving next location prediction with inferred activity semantics in mobile phone data

description: Accurately predicting the next location of mobile phone users is essential for various applications such as personalized location-based services and mobile marketing. While previous models have relied primarily on spatiotemporal sequences (e.g. location and time information), recent research has begun to explore the integration of activity semantics, which provides contextual insights into the motivations behind mobility. However, the use of activity semantics remains underexplored in large-scale mobile phone data, where such semantics are not explicitly recorded. This study proposes a semantics-enhanced prediction framework that infers and integrates user activities into a long short-term memory (LSTM) architecture with attention mechanisms and multimodal embeddings. Specifically, we infer six types of activities: home and work using rule-based heuristics and four non-mandatory activities (shopping, leisure, eat out, and personal affairs) using a supervised machine learning approach. These inferred activities are encoded as embeddings and fused with spatiotemporal features within the model. The experimental results on mobile phone data from Guangzhou, China, demonstrate that the proposed model improves the prediction accuracy by 4.3–101% compared with baseline models that lack activity-level contextualization. Notably, users with more stable daily activity patterns benefit most significantly from the integration of activity semantics. This work highlights the potential of integrating inferred human activity types to enhance mobility prediction in data-rich but semantically sparse environments.
DOI: 10.6084/m9.figshare.30073278.v1
title: Improving next location prediction with inferred activity semantics in mobile phone data

description: Accurately predicting the next location of mobile phone users is essential for various applications such as personalized location-based services and mobile marketing. While previous models have relied primarily on spatiotemporal sequences (e.g. location and time information), recent research has begun to explore the integration of activity semantics, which provides contextual insights into the motivations behind mobility. However, the use of activity semantics remains underexplored in large-scale mobile phone data, where such semantics are not explicitly recorded. This study proposes a semantics-enhanced prediction framework that infers and integrates user activities into a long short-term memory (LSTM) architecture with attention mechanisms and multimodal embeddings. Specifically, we infer six types of activities: home and work using rule-based heuristics and four non-mandatory activities (shopping, leisure, eat out, and personal affairs) using a supervised machine learning approach. These inferred activities are encoded as embeddings and fused with spatiotemporal features within the model. The experimental results on mobile phone data from Guangzhou, China, demonstrate that the proposed model improves the prediction accuracy by 4.3–101% compared with baseline models that lack activity-level contextualization. Notably, users with more stable daily activity patterns benefit most significantly from the integration of activity semantics. This work highlights the potential of integrating inferred human activity types to enhance mobility prediction in data-rich but semantically sparse environments.
DOI: 10.15468/a7upu6
title: Zooplankton densities collected from a seasonally hypoxic fjord (Hood Canal, Salish Sea, USA) on 2012-2013 cruises

description: This zooplankton dataset was collected as part of a larger study examining the effects of hypoxia on species composition, distributions, and predator-prey interactions between zooplankton and fish in a pelagic marine ecosystem. Day/night paired zooplankton sampling was conducted in Hood Canal, Puget Sound (Washington state, US), during 10 monthly cruises from June to October, 2012 and 2013, at five stations: Dabob, Union, Hoodsport, Duckabush and Twanoh. An obliquely towed multi-net system was used to collect depth stratified and full water column samples. For the depth-stratified sampling, depth layers were based on the dissolved oxygen profiles from CTD casts. In the laboratory, zooplankton were quantitatively subsampled and microscopically counted, with zooplankton densities calculated. All individuals were identified to species or larger taxonomic grouping, and by life stages for some species, within each sample. This dataset as published with OBIS incorporates corrections and updates to a dataset previously published on BCO-DMO.
DOI: 10.15468/dl.9v9cgy
title: Occurrence Download

description: A dataset containing 3338 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "TaxonKey is Begonia"
 ]
}

 The dataset includes 3338 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060673-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/079-zgg-bg7
title: Thalictrum aquilegiifolium L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07a-tnv-qf0
title: Nuphar pumila (Timm) DC.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.34740/kaggle/dsv/14226180
title: BOS Kemdikbud
DOI: 10.3535/07b-20k-gfg
title: Allium oleraceum L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07b-8lv-wvk
title: Deschampsia cespitosa subsp. bottnica

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.6084/m9.figshare.30695061
title: Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy

description: PM<sub>2.5</sub> pollution significantly impedes sustainable development in large urban agglomerations. Understanding its driving mechanisms allows the development of targeted management strategies. However, focusing on static factors alone limits the ability to regulate the pollution reduction pace. Achieving stable and efficient PM<sub>2.5</sub> reduction in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is challenging. This study employed time series information entropy, random forest, partial dependency plot, and multiscale geographically weighted regression (MGWR) to explore the nonlinearity and spatial heterogeneity of static and dynamic influences on PM<sub>2.5</sub>. We then applied K-means clustering to design region-specific control strategies. Results revealed that temperature (46.39%) and wind speed (44.12%) primarily drove static PM<sub>2.5</sub> intensity, while changes in gross domestic product (GDP; 47.50%) influenced its evolution. Enhancing road networks and building density fosters compact cities that steadily reduce PM<sub>2.5</sub>. Minor fluctuations in population density and nighttime lighting significantly reduced PM<sub>2.5</sub>, indicating that stable demographic and economic transitions support sustained reductions. At the subregional level, cooling strategies should be prioritized in the central GBA, while regulating population growth, GDP, and road construction is key in peripheral areas. This study clarifies PM<sub>2.5</sub>,s dynamic driving mechanisms and offers actionable insights for managing both the level and timing of reductions.
DOI: 10.6084/m9.figshare.30695061.v1
title: Nonlinear driving mechanism of PM<sub>2.5</sub> and spatial governance considering time series information entropy

description: PM<sub>2.5</sub> pollution significantly impedes sustainable development in large urban agglomerations. Understanding its driving mechanisms allows the development of targeted management strategies. However, focusing on static factors alone limits the ability to regulate the pollution reduction pace. Achieving stable and efficient PM<sub>2.5</sub> reduction in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is challenging. This study employed time series information entropy, random forest, partial dependency plot, and multiscale geographically weighted regression (MGWR) to explore the nonlinearity and spatial heterogeneity of static and dynamic influences on PM<sub>2.5</sub>. We then applied K-means clustering to design region-specific control strategies. Results revealed that temperature (46.39%) and wind speed (44.12%) primarily drove static PM<sub>2.5</sub> intensity, while changes in gross domestic product (GDP; 47.50%) influenced its evolution. Enhancing road networks and building density fosters compact cities that steadily reduce PM<sub>2.5</sub>. Minor fluctuations in population density and nighttime lighting significantly reduced PM<sub>2.5</sub>, indicating that stable demographic and economic transitions support sustained reductions. At the subregional level, cooling strategies should be prioritized in the central GBA, while regulating population growth, GDP, and road construction is key in peripheral areas. This study clarifies PM<sub>2.5</sub>,s dynamic driving mechanisms and offers actionable insights for managing both the level and timing of reductions.
DOI: 10.3535/07b-8sa-eay
title: Milium effusum L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07b-8yl-fcg
title: Sedum acre L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07b-aq8-j5r
title: Plantae

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07b-era-fkz
title: Sparganium glomeratum (Laest. ex Beurl.) Beurl.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07b-eyx-2j0
title: Alnus incana (L.) Moench

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07b-l1m-fvn
title: Potamogeton pusillus L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07b-q01-p3j
title: Plantae

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07b-t0n-xp1
title: Cakile maritima Scop.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07b-wdw-8mn
title: Agrostis mertensii Trin.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07b-zdf-hrn
title: Equisetum palustre L.

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07c-5rh-f0v
title: Alchemilla filicaulis Buser

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/07c-6yh-9zq
title: Carex nigra (L.) Reichard

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.