cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.l4era3
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tropaeolum lindenii Wallis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 14 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025854-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3kkvcd
title: Occurrence Download

description: A dataset containing 45434 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sula sula (Linnaeus, 1766)"
 ]
}

 The dataset includes 45434 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011188-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qg42re
title: Occurrence Download

description: A dataset containing 25780 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aristida adscensionis"
 ]
}

 The dataset includes 25780 records from 488 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055279-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.chjsfd
title: Occurrence Download

description: A dataset containing 584 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Russian Federation",
 "GadmGid is RUS.66_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Leonurus quinquelobatus"
 ]
}

 The dataset includes 584 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054309-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14172814
title: textocr
DOI: 10.6084/m9.figshare.28229507
title: Genome Assembly and Annotation of <i>Brainea insignis</i>

description: <b>Decoding the cycad fern genome: implications for its genomic evolution and conservation</b>
DOI: 10.6084/m9.figshare.28229507.v1
title: Genome Assembly and Annotation of <i>Brainea insignis</i>

description: <b>Decoding the cycad fern genome: implications for its genomic evolution and conservation</b>
DOI: 10.15468/dl.e3a2rc
title: Occurrence Download

description: A dataset containing 31165 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((113.7058 29.72084,117.24982 29.72084,117.24982 32.66401,113.7058 32.66401,113.7058 29.72084))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves",
 "Year 2000-2020"
 ]
}

 The dataset includes 31165 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054310-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28530701
title: Genome Assembly and Annotation of <b><i>Ormosia henryi</i></b>

description: Chromosome-level genome assembly and annotation of the endangered species <i>Ormosia henryi</i>
DOI: 10.6084/m9.figshare.28530701.v1
title: Genome Assembly and Annotation of <b><i>Ormosia henryi</i></b>

description: Chromosome-level genome assembly and annotation of the endangered species <i>Ormosia henryi</i>
DOI: 10.15468/dl.txuccj
title: Occurrence Download

description: A dataset containing 1302 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Aucoumea klaineana"
 ]
}

 The dataset includes 1302 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054311-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30892094
title: IPC数据

description: 该数据集作为一项研究论文的支持数据，该论文聚焦于跆拳道测试中缺血预条件与能量代谢之间的相关性。数据已根据编辑要求上传。
DOI: 10.6084/m9.figshare.30892094.v1
title: IPC数据

description: 该数据集作为一项研究论文的支持数据，该论文聚焦于跆拳道测试中缺血预条件与能量代谢之间的相关性。数据已根据编辑要求上传。
DOI: 10.5281/zenodo.17948031
title: Business model canva - Tailor go
DOI: 10.5281/zenodo.17948032
title: Business model canva - Tailor go
DOI: 10.15468/dl.veje4h
title: Occurrence Download

description: A dataset containing 1302 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Aucoumea klaineana"
 ]
}

 The dataset includes 1302 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054313-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.95c7jy
title: Occurrence Download

description: A dataset containing 2861261 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to 59.085278",
 "DecimalLatitude is less than or equal to 64.085278",
 "DecimalLongitude is greater than or equal to 5.0",
 "DecimalLongitude is less than or equal to 10.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 2861261 records from 226 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054299-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18112/openneuro.ds007041.v1.0.0
title: RNS_Epilepsy-iBIDS
DOI: 10.6084/m9.figshare.30892145.v1
title: e types and account sizes. Traders are free to choose their preferred

description: ering the barrier to entry. Traders gain access to funded account opportunities at a
DOI: 10.5281/zenodo.17947896
title: Actual and modeled daily  SSR data with different cloud cover inputs for 2023 for region lake Baikal

description: Observed and modeled daily SSR (Surface Solar Radiation) data with different input datasets (AVHRR/MODIS/weather station data) and daily mean cloud fraction cover for the year 2023 in the Lake Baikal region.The modeled data were generated using a physically based model with different approaches to account for cloud effects.The data are provided in .xlsx format.
DOI: 10.5281/zenodo.17947895
title: Actual and modeled daily  SSR data with different cloud cover inputs for 2023 for region lake Baikal

description: Observed and modeled daily SSR (Surface Solar Radiation) data with different input datasets (AVHRR/MODIS/weather station data) and daily mean cloud fraction cover for the year 2023 in the Lake Baikal region.The modeled data were generated using a physically based model with different approaches to account for cloud effects.The data are provided in .xlsx format.
DOI: 10.34740/kaggle/dsv/14172894
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.21227/74bw-5f63
title: "CASCADE Training Dataset"

description: "Training dataset generated by Agentdojo framework."
DOI: 10.5281/zenodo.17079840
title: Free energy calculations - Results of mmPBSA calculations

description: Free energy calculations of the interaction of ligands with spx1
DOI: 10.5281/zenodo.17947299
title: Free energy calculations - Results of mmPBSA calculations

description: Free energy calculations of the interaction of ligands with spx1
DOI: 10.18112/openneuro.ds007095.v1.0.0
title: RNS_Epilepsy-iBIDS
DOI: 10.15468/dl.5q8w2k
title: Occurrence Download

description: A dataset containing 242018 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alectura lathami",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))"
 ]
}

 The dataset includes 242018 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054301-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30889526
title: Questionnaire （251216）

description: Amid rising global mental health challenges, particularly among urban populations facing social isolation and role changes, community-based interventions have become critically important. Group arts activities provide a culturally accessible means of enhancing psychological well-being, but their underlying mechanisms remain poorly understood theoretically. Guided by social cognitive theory, this study proposes a triadic framework that explores how personal factors (e.g., emotional intelligence), behavioral factors (e.g., participation in group arts activities), and environmental factors (e.g., servant-oriented organizers) interact to improve community residents' psychological well-being.
DOI: 10.6084/m9.figshare.30889526.v1
title: Questionnaire （251216）

description: Amid rising global mental health challenges, particularly among urban populations facing social isolation and role changes, community-based interventions have become critically important. Group arts activities provide a culturally accessible means of enhancing psychological well-being, but their underlying mechanisms remain poorly understood theoretically. Guided by social cognitive theory, this study proposes a triadic framework that explores how personal factors (e.g., emotional intelligence), behavioral factors (e.g., participation in group arts activities), and environmental factors (e.g., servant-oriented organizers) interact to improve community residents' psychological well-being.
DOI: 10.6084/m9.figshare.29929645
title: The renin-angiotensin system in healthy human platelets: expressed but inactive

description: Platelets play a crucial role in arterial thrombus formation, offering potential for new antiplatelet therapies with reduced bleeding risk. Here, we investigated the role of the renin-angiotensin system (RAS) in human platelets and explored its potential link to COVID-19 coagulopathy. Experiments were performed <i>ex vivo</i> on healthy human platelets. The expression of RAS receptors (Mas, MrgD, ACE, ACE2, AT1 and AT2) was evaluated using western blot and immunofluorescence. Platelets were incubated <i>in vitro</i> with either Captopril or different RAS peptides including Alamandine, Angiotensin-I, Angiotensin-II, Angiotensin-(1–7), and Angiotensin-(1–9). Platelet adhesion was measured by spectrophotometry using BCECF fluorescence. Platelet activation and aggregation were analyzed using aggregometry after stimulation with extracellular matrix proteins. ACE and ACE2 activity were assessed using Fluorescent Peptides (FPS). We demonstrated that healthy human platelets express all the tested RAS receptors. However, RAS peptides did not modulate platelet adhesion or aggregation despite a wide range of concentrations tested. ACE activity was detected in platelet lysates, but it was not inhibited by Captopril, while ACE2 activity was undetectable. Our findings suggest that while RAS receptors are expressed in platelets, RAS peptides do not impact platelet function, at least in our experimental setting. COVID-19 coagulopathy may occur independently of the RAS. Platelets play a crucial role in the formation of a blood clot after vessel injury. However, blood clots can also obstruct arteries ( = arterial thrombosis), resulting in heart attack, stroke, or peripheral ischemia. As platelets play such an important role in the pathological development of arterial thrombosis, antiplatelet therapies aim to reduce the risk of recurrence of these diseases. Antiplatelet therapies usually work by targeting specific receptors to reduce platelet activity, i.e. platelet adhesion, aggregation and activation. As current antiplatelet agents can cause bleeding, research aims to develop new strategies to target only pathological platelet conditions. In this study, we tried to characterize the effect of different peptides belonging to the renin-angiotensin system (RAS) on platelets. As the RAS plays an important role in blood pressure regulation, we sought to establish whether it also plays a role in platelet activity. Furthermore, as SARS-CoV-2 usually enters cells via ACE2, an enzyme belonging to the RAS, we questioned whether the RAS could play a role in peripheral thrombosis associated with severe COVID-19 disease (COVID-19 coagulopathy). We observed that RAS receptors are expressed by platelets, while RAS peptides did not affect platelet adhesion, aggregation, and activation in our experiments. Consequently, we do not think that the RAS plays a significant role in platelet dynamics and COVID-19 coagulopathy.
DOI: 10.6084/m9.figshare.29929645.v1
title: The renin-angiotensin system in healthy human platelets: expressed but inactive

description: Platelets play a crucial role in arterial thrombus formation, offering potential for new antiplatelet therapies with reduced bleeding risk. Here, we investigated the role of the renin-angiotensin system (RAS) in human platelets and explored its potential link to COVID-19 coagulopathy. Experiments were performed <i>ex vivo</i> on healthy human platelets. The expression of RAS receptors (Mas, MrgD, ACE, ACE2, AT1 and AT2) was evaluated using western blot and immunofluorescence. Platelets were incubated <i>in vitro</i> with either Captopril or different RAS peptides including Alamandine, Angiotensin-I, Angiotensin-II, Angiotensin-(1–7), and Angiotensin-(1–9). Platelet adhesion was measured by spectrophotometry using BCECF fluorescence. Platelet activation and aggregation were analyzed using aggregometry after stimulation with extracellular matrix proteins. ACE and ACE2 activity were assessed using Fluorescent Peptides (FPS). We demonstrated that healthy human platelets express all the tested RAS receptors. However, RAS peptides did not modulate platelet adhesion or aggregation despite a wide range of concentrations tested. ACE activity was detected in platelet lysates, but it was not inhibited by Captopril, while ACE2 activity was undetectable. Our findings suggest that while RAS receptors are expressed in platelets, RAS peptides do not impact platelet function, at least in our experimental setting. COVID-19 coagulopathy may occur independently of the RAS. Platelets play a crucial role in the formation of a blood clot after vessel injury. However, blood clots can also obstruct arteries ( = arterial thrombosis), resulting in heart attack, stroke, or peripheral ischemia. As platelets play such an important role in the pathological development of arterial thrombosis, antiplatelet therapies aim to reduce the risk of recurrence of these diseases. Antiplatelet therapies usually work by targeting specific receptors to reduce platelet activity, i.e. platelet adhesion, aggregation and activation. As current antiplatelet agents can cause bleeding, research aims to develop new strategies to target only pathological platelet conditions. In this study, we tried to characterize the effect of different peptides belonging to the renin-angiotensin system (RAS) on platelets. As the RAS plays an important role in blood pressure regulation, we sought to establish whether it also plays a role in platelet activity. Furthermore, as SARS-CoV-2 usually enters cells via ACE2, an enzyme belonging to the RAS, we questioned whether the RAS could play a role in peripheral thrombosis associated with severe COVID-19 disease (COVID-19 coagulopathy). We observed that RAS receptors are expressed by platelets, while RAS peptides did not affect platelet adhesion, aggregation, and activation in our experiments. Consequently, we do not think that the RAS plays a significant role in platelet dynamics and COVID-19 coagulopathy.
DOI: 10.6084/m9.figshare.30454806
title: Computational modeling of platelet activation signatures in response to diverse immune and hemostatic agonists

description: Platelets are increasingly recognized as key players not only in hemostasis, but also in immunity and inflammation. However, the mechanisms and markers underlying their activation remain incompletely understood. This study aimed to decipher how platelets respond to different stimuli and to identify specific molecular signatures using computational approaches. Platelets from 10 healthy donors were stimulated under seven conditions, including TRAP (PAR-1), AYPGKF (PAR-4), ADP, collagen, sCD40L, fibrinogen, and a control. A total of 47 markers—encompassing membrane proteins, soluble mediators, and intracellular signals—were analyzed. Statistical and machine learning methods, including hierarchical clustering and random forest algorithms, were used to classify and interpret the data. Distinct activation profiles emerged for each agonist. A reduced panel of six markers (AKT, CD40L, CD62P, PKC, RANTES, and TSLP) enabled identification of the stimulus with 86.8% accuracy. Machine learning further improved classification (87.9% multiclass accuracy). Differences were also observed across donors, highlighting inter-individual variability. This work supports a new paradigm in which platelets act as “biological sensors,” fine-tuning their responses to environmental cues. The identified biomarker panel provides a basis for further investigation into the characterization of platelet activation profiles, with potential relevance for future diagnostic and therapeutic applications in thromboinflammatory and immune-mediated conditions. Platelets are small blood cells best known for stopping bleeding, but they also help defend the body and control inflammation. This study explored whether platelets can adapt their responses depending on the type of threat—such as injury or infection. Using computer models, we showed that platelets can sense different signals and adjust what they release accordingly. While this helps protect the body, it can also worsen diseases like heart conditions, infections, or cancer. Understanding these responses could lead to new treatments that guide platelet behavior in a beneficial direction, especially in illnesses linked to inflammation or immune dysfunction.
DOI: 10.6084/m9.figshare.30454806.v1
title: Computational modeling of platelet activation signatures in response to diverse immune and hemostatic agonists

description: Platelets are increasingly recognized as key players not only in hemostasis, but also in immunity and inflammation. However, the mechanisms and markers underlying their activation remain incompletely understood. This study aimed to decipher how platelets respond to different stimuli and to identify specific molecular signatures using computational approaches. Platelets from 10 healthy donors were stimulated under seven conditions, including TRAP (PAR-1), AYPGKF (PAR-4), ADP, collagen, sCD40L, fibrinogen, and a control. A total of 47 markers—encompassing membrane proteins, soluble mediators, and intracellular signals—were analyzed. Statistical and machine learning methods, including hierarchical clustering and random forest algorithms, were used to classify and interpret the data. Distinct activation profiles emerged for each agonist. A reduced panel of six markers (AKT, CD40L, CD62P, PKC, RANTES, and TSLP) enabled identification of the stimulus with 86.8% accuracy. Machine learning further improved classification (87.9% multiclass accuracy). Differences were also observed across donors, highlighting inter-individual variability. This work supports a new paradigm in which platelets act as “biological sensors,” fine-tuning their responses to environmental cues. The identified biomarker panel provides a basis for further investigation into the characterization of platelet activation profiles, with potential relevance for future diagnostic and therapeutic applications in thromboinflammatory and immune-mediated conditions. Platelets are small blood cells best known for stopping bleeding, but they also help defend the body and control inflammation. This study explored whether platelets can adapt their responses depending on the type of threat—such as injury or infection. Using computer models, we showed that platelets can sense different signals and adjust what they release accordingly. While this helps protect the body, it can also worsen diseases like heart conditions, infections, or cancer. Understanding these responses could lead to new treatments that guide platelet behavior in a beneficial direction, especially in illnesses linked to inflammation or immune dysfunction.
DOI: 10.6084/m9.figshare.29290427
title: Interpretable machine-learning diagnosis of forest gross primary productivity patterns in China’s protected areas

description: A Python script used for modeling forest GPP in China´s Protected Areas, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), implementation of four machine learning models to predict forest GPP, XAI and causality analysis.All code was written by Chenxi Zhu except the causality analysis which was written by Emmanuel Yeboah.
DOI: 10.6084/m9.figshare.29290427.v2
title: Interpretable machine-learning diagnosis of forest gross primary productivity patterns in China’s protected areas

description: A Python script used for modeling forest GPP in China´s Protected Areas, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), implementation of four machine learning models to predict forest GPP, XAI and causality analysis.All code was written by Chenxi Zhu except the causality analysis which was written by Emmanuel Yeboah.
DOI: 10.6084/m9.figshare.29290427.v1
title: Leveraging explainable causal artificial intelligence to study forest gross primary productivity dynamics in China's protected areas

description: A Python script used for modeling forest GPP in China´s Protected Areas, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), implementation of four machine learning models to predict forest GPP, XAI and causality analysis.All code was written by Chenxi Zhu except the causality analysis which was written by Emmanuel Yeboah.