cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.q7qbaj
title: Occurrence Download

description: A dataset containing 238238 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Castor fiber Linnaeus, 1758"
 ]
}

 The dataset includes 238238 records from 492 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016726-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.apfp2q
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepisiota frauenfeldi (Mayr, 1855)",
 "Country is Australia",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 51 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016731-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.knwjaq
title: Occurrence Download

description: A dataset containing 1996 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Romulea minutiflora Klatt, Salix alba, Romulea minutiflora Klatt, Romulea minutiflora Klatt, Romulea minutiflora Klatt, Romulea minutiflora Klatt, Romulea minutiflora Klatt, 1882)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1996 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016732-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jyvgz9
title: Occurrence Download

description: A dataset containing 860 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Bison bonasus (Linnaeus, 1758)"
 ]
}

 The dataset includes 860 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016733-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.crse2u
title: Occurrence Download

description: A dataset containing 1496 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Simalia boeleni (Brongersma, 1953), Antaresia maculosa (Peters, 1873), Nyctophilopython oenpelliensis (Gow, 1977))"
 ]
}

 The dataset includes 1496 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016734-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17871/atto.437.4.1683
title: Trace gases and isotope ratios (13C, 14C, 18O, 2H) of ambient air collected in flasks at a vertical profile at the ATTO site in October 2019
DOI: 10.15468/dl.6d6jrz
title: Occurrence Download

description: A dataset containing 577 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Banco de Germoplasma Vegetal del Jardín Botánico de Olarizu (Vitoria-Gasteiz)/Olarizuko Lorategi Botanikoaren Landare Germoplasma Bankua (Vitoria-Gasteiz)"
 ]
}

 The dataset includes 577 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016736-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7w5ujm
title: Occurrence Download

description: A dataset containing 227 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "VerbatimScientificName is Centaurea Repens"
 ]
}

 The dataset includes 227 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016737-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25925/20241101
title: Multi-laboratory compilation of atmospheric carbon dioxide data for the period 1957-2023; obspack_co2_1_GLOBALVIEWplus_v10.1_2024-11-13

description: This product is constructed using the Observation Package (ObsPack) framework [Masarie et al., 2014; www.earth-syst-sci-data.net/6/375/2014/]. The framework is designed to bring together atmospheric greenhouse gas (GHG) observations from a variety of sampling platforms, prepare them with specific applications in mind, and package and distribute them in a self-consistent and well-documented product. ObsPack products are intended to support GHG budget studies and represent a new generation of cooperative value-added GHG data products. This product includes 625 atmospheric carbon dioxide datasets derived from observations made by 79 laboratories from 28 countries. Data for the period 1957-2023 (where available) are included.
DOI: 10.25345/c55d8ns5t
title: MassIVE MSV000096428 - Extracellular polymeric substance degradation shapes microbial community diversity
title: Extracellular polymeric substances are degraded by extracellular enzymes purified from seawater communities. Formation of degradation products is measured over time in negative and positive mode with an Agilent 6546 QTOF
DOI: 10.15468/dl.ff2ywr
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepisiota frauenfeldi (Mayr, 1855)",
 "Country is Australia",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 51 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016739-241107131044228/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.27763999
title: Entertainment interspersed with propaganda: how non-legacy-news accounts deliver explicitly political content to mass audiences on Russia’s most popular social network VK

description: Previous research on political communication on Russia's most popular social network VK has concluded that most users avoid news by not following legacy-news accounts. In this study, we expand the universe of scrutinized accounts with the most-followed non-legacy-news accounts (&gt;100,000 followers) that regularly publish what we theorize to be ‘explicitly political content’ (EPC; <i>N</i> = 355). We delineate a typology of six types of EPC accounts, calculate their aggregate follower counts, and determine how many of them were still (1) accessible from Russia and (2) publishing Kremlin-critical content in October 2022. Our findings indicate that non-critical accounts attracted 26 times more followers than Kremlin-critical accounts. Entertainment-focused EPC accounts had seven times more followers than legacy-news accounts. As a result, they became the primary means through which non-critical EPC reached news-avoidant mass audiences. We identify three dimensions through which autocrats can interweave propaganda and entertainment and highlight promising research paths.
DOI: 10.6084/m9.figshare.27763999.v1
title: Entertainment interspersed with propaganda: how non-legacy-news accounts deliver explicitly political content to mass audiences on Russia’s most popular social network VK

description: Previous research on political communication on Russia's most popular social network VK has concluded that most users avoid news by not following legacy-news accounts. In this study, we expand the universe of scrutinized accounts with the most-followed non-legacy-news accounts (&gt;100,000 followers) that regularly publish what we theorize to be ‘explicitly political content’ (EPC; <i>N</i> = 355). We delineate a typology of six types of EPC accounts, calculate their aggregate follower counts, and determine how many of them were still (1) accessible from Russia and (2) publishing Kremlin-critical content in October 2022. Our findings indicate that non-critical accounts attracted 26 times more followers than Kremlin-critical accounts. Entertainment-focused EPC accounts had seven times more followers than legacy-news accounts. As a result, they became the primary means through which non-critical EPC reached news-avoidant mass audiences. We identify three dimensions through which autocrats can interweave propaganda and entertainment and highlight promising research paths.
DOI: 10.6084/m9.figshare.27188481
title: CASPER: A Phase I trial combining calaspargase pegol-mnkl and cobimetinib in pancreatic cancer

description: Asparagine synthetase (ASNS) catalyzes the biosynthesis of asparagine from aspartate and glutamine. Cells lacking ASNS, however, are auxotrophic for asparagine. Use of L-asparaginase to promote asparagine starvation in solid tumors with low ASNS levels, such as pancreatic ductal adenocarcinoma (PDAC), is a rationale treatment strategy. However, tumor cell resistance to L-asparaginase has limited its clinical utility. Our preclinical studies show that RAS/MAPK signaling circumvents L-asparaginase-induced tumor killing, but L-asparaginase and MEK inhibition potentiated tumor killing; suggesting that this combination may provide meaningful clinical benefit to patients with PDAC. This Phase I trial (NCT05034627) will evaluate the safety and tolerability of the MEK inhibitor, cobimetinib, in combination with pegylated L-asparaginase, L calaspargase pegol-mknl, in patients with locally-advanced or metastatic PDAC. Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor prognosis. The majority (50–80%) of PDAC lack asparagine synthetase and ability to synthesize asparagine; and are dependent on extracellular sources of asparagine. Use of L-asparaginase to promote asparagine starvation in solid tumors can be exploited as a rationale treatment strategy. Under conditions of nutrient stress, aberrant RAS/MEK/ERK signaling induces ATF4 and in turn, asparagine synthetase to bolster intracellular biosynthesis of asparagine. Targeted inhibition of RAS/MEK/ERK signaling along with an L-asparaginase has the potential to provide meaningful clinical benefit to patients with advanced PDAC. There is no prior research that has provided a safety profile for a MEK inhibitor in combination with L-asparaginase. This Phase IB trial will evaluate the safety and tolerability of the MEK inhibitor, cobimetinib, in combination with the pegylated L-asparaginase, L calaspargase pegol-mknl, in patients with locally-advanced or metastatic PDAC. The trial uses a Bayesian optimal interval design to assess the maximum tolerated dose for combining cobimetinib and calaspargase pegol-mknl. The primary end point estimates the incidence of dose limiting toxicities associated with combination regimen of cobimetinib and calaspargase pegol-mknl. Secondary objectives estimate the preliminary objective response rate and clinical benefit rate. Adults with locally-advanced or metastatic PDAC that have progressed on, been intolerant to, or refused systemic therapy that is consistent with institutional standards (e.g., Gemcitabine-based, or FOLFIRINOX). A maximum of 15 patients will be enrolled in cohorts of 3 to receive their assigned dose of calaspargase pegol-mknl and cobimetinib. Up to 6 core-needle biopsies at baseline and on Cycle 2 Day 14.
DOI: 10.6084/m9.figshare.27188481.v1
title: CASPER: A Phase I trial combining calaspargase pegol-mnkl and cobimetinib in pancreatic cancer

description: Asparagine synthetase (ASNS) catalyzes the biosynthesis of asparagine from aspartate and glutamine. Cells lacking ASNS, however, are auxotrophic for asparagine. Use of L-asparaginase to promote asparagine starvation in solid tumors with low ASNS levels, such as pancreatic ductal adenocarcinoma (PDAC), is a rationale treatment strategy. However, tumor cell resistance to L-asparaginase has limited its clinical utility. Our preclinical studies show that RAS/MAPK signaling circumvents L-asparaginase-induced tumor killing, but L-asparaginase and MEK inhibition potentiated tumor killing; suggesting that this combination may provide meaningful clinical benefit to patients with PDAC. This Phase I trial (NCT05034627) will evaluate the safety and tolerability of the MEK inhibitor, cobimetinib, in combination with pegylated L-asparaginase, L calaspargase pegol-mknl, in patients with locally-advanced or metastatic PDAC. Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor prognosis. The majority (50–80%) of PDAC lack asparagine synthetase and ability to synthesize asparagine; and are dependent on extracellular sources of asparagine. Use of L-asparaginase to promote asparagine starvation in solid tumors can be exploited as a rationale treatment strategy. Under conditions of nutrient stress, aberrant RAS/MEK/ERK signaling induces ATF4 and in turn, asparagine synthetase to bolster intracellular biosynthesis of asparagine. Targeted inhibition of RAS/MEK/ERK signaling along with an L-asparaginase has the potential to provide meaningful clinical benefit to patients with advanced PDAC. There is no prior research that has provided a safety profile for a MEK inhibitor in combination with L-asparaginase. This Phase IB trial will evaluate the safety and tolerability of the MEK inhibitor, cobimetinib, in combination with the pegylated L-asparaginase, L calaspargase pegol-mknl, in patients with locally-advanced or metastatic PDAC. The trial uses a Bayesian optimal interval design to assess the maximum tolerated dose for combining cobimetinib and calaspargase pegol-mknl. The primary end point estimates the incidence of dose limiting toxicities associated with combination regimen of cobimetinib and calaspargase pegol-mknl. Secondary objectives estimate the preliminary objective response rate and clinical benefit rate. Adults with locally-advanced or metastatic PDAC that have progressed on, been intolerant to, or refused systemic therapy that is consistent with institutional standards (e.g., Gemcitabine-based, or FOLFIRINOX). A maximum of 15 patients will be enrolled in cohorts of 3 to receive their assigned dose of calaspargase pegol-mknl and cobimetinib. Up to 6 core-needle biopsies at baseline and on Cycle 2 Day 14.
DOI: 10.6084/m9.figshare.27089532
title: Locally Adaptive Shrinkage Priors for Trends and Breaks in Count Time Series

description: Nonstationary count time series characterized by features such as abrupt changes and fluctuations about the trend arise in many scientific domains including biophysics, ecology, energy, epidemiology, and social science domains. Current approaches for integer-valued time series lack the flexibility to capture local transient features while more flexible models for continuous data types are inadequate for universal applications to integer-valued responses such as settings with small counts. We present a modeling framework, the negative binomial Bayesian trend filter (NB-BTF), that offers an adaptive model-based solution to capturing multiscale features with valid integer-valued inference for trend filtering. The framework is a hierarchical Bayesian model with a dynamic global-local shrinkage process. The flexibility of the global-local process allows for the necessary local regularization while the temporal dependence induces a locally smooth trend. In simulation, the NB-BTF outperforms a number of alternative trend filtering methods. Then, we demonstrate the method on weekly power outage frequency in Massachusetts townships. Power outage frequency is characterized by a persistent low level trend with occasional spikes. These illustrations show the estimation of a smooth, nonstationary trend with adequate uncertainty quantification.
DOI: 10.6084/m9.figshare.27089532.v1
title: Locally Adaptive Shrinkage Priors for Trends and Breaks in Count Time Series

description: Nonstationary count time series characterized by features such as abrupt changes and fluctuations about the trend arise in many scientific domains including biophysics, ecology, energy, epidemiology, and social science domains. Current approaches for integer-valued time series lack the flexibility to capture local transient features while more flexible models for continuous data types are inadequate for universal applications to integer-valued responses such as settings with small counts. We present a modeling framework, the negative binomial Bayesian trend filter (NB-BTF), that offers an adaptive model-based solution to capturing multiscale features with valid integer-valued inference for trend filtering. The framework is a hierarchical Bayesian model with a dynamic global-local shrinkage process. The flexibility of the global-local process allows for the necessary local regularization while the temporal dependence induces a locally smooth trend. In simulation, the NB-BTF outperforms a number of alternative trend filtering methods. Then, we demonstrate the method on weekly power outage frequency in Massachusetts townships. Power outage frequency is characterized by a persistent low level trend with occasional spikes. These illustrations show the estimation of a smooth, nonstationary trend with adequate uncertainty quantification.
DOI: 10.5281/zenodo.11477730
title: PROSPECT PTMs: Test-PTM

description: High-quality labeled datasets for applying machine and deep learning to several tasks in MS based proteomics.
description: Bundesministerium für Bildung und Forschung – BMBF; Grant Number 031L0008A
DOI: 10.5281/zenodo.11477731
title: PROSPECT PTMs: Test-PTM

description: High-quality labeled datasets for applying machine and deep learning to several tasks in MS based proteomics.
description: Bundesministerium für Bildung und Forschung – BMBF; Grant Number 031L0008A
DOI: 10.5281/zenodo.10048187
title: tFood: Semantic Table Annotations Benchmark for Food Domain

description: tFood is a dataset for tabular data to knowledge graph matching. It is derived for the Food domain and has two types of tables. On the one hand, Horizontal Relational Tables are where each table represents a collection of entities. On the other hand, Entity Tables are where each of which represents a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).


The supported tasks for semantic table annotations are: 



	
Topic Detection (TD) links the entire table to an entity or a class from the target KG.
	
Cell Entity Annotation (CEA) maps individual table cells to entities from the target KG.
	
Column Type Annotation (CTA) links individual table columns to classes from the target KG.
	
Column Property Annotation (CPA) detects the relations between column pairs from the target knowledge graph.



This dataset version will be used during SemTab 2023 - Round 1. So, the ground truth data for the test set is currently hidden. We will add such ground truth after the conclusion of the challenge. 


 


 
description: Ground truth for testing data (Entity and Horizontal) tables have been added.
DOI: 10.5281/zenodo.10277790
title: tFoodL: Larger Semantic Table Annotations Benchmark for Food Domain

description: tFoodL is the successor work of tFood that is generated by KG2Tables using 10 levels of a recursive hierarchy of related concepts in Wikidata.
Similar to tFood, it is a dataset for tabular data to knowledge graph matching. It is derived for the Food domain and has two types of tables. On the one hand, Horizontal Relational Tables are where each table represents a collection of entities. On the other hand, Entity Tables represent a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).
tFoodL contains 43,255 entity and horizontal tables, while this repository contains only the validation fold (10%) of the entire benchmark with its ground truth data (gt). 
The supported tasks for semantic table annotations are: 

Topic Detection (TD) links the entire table to an entity or a class from the target KG.
Cell Entity Annotation (CEA) maps individual table cells to entities from the target KG.
Column Type Annotation (CTA) links individual table columns to classes from the target KG.
Column Property Annotation (CPA) detects the relations between column pairs from the target knowledge graph.
Row Annotation (RA) annotates the entire row to a KG entity or property.
DOI: 10.5281/zenodo.10283015
title: tBiodiv: Semantic Table Annotations Benchmark for Biodiversity Domain

description: tBiodiv is a dataset for tabular data to knowledge graph matching. It is derived for the Biodiversity domain and has two types of tables. On the one hand, Horizontal Relational Tables are where each table represents a collection of entities. On the other hand, Entity Tables represent a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).
tBiodiv is generated by KG2Tables using two levels of a recursive hierarchy of related concepts in Wikidata.
tBiodiv contains 57,426 entity and horizontal tables, while this repository contains only a sample of 1% of the total generated tables of the entire benchmark with its ground truth data (gt). The Full size of this dataset is 122 GB. We will update this repository with the full dataset in the Future.
Please get in touch if you are interested in the full dataset, 
The supported tasks for semantic table annotations are: 

Topic Detection (TD) links the entire table to an entity or a class from the target KG.
Cell Entity Annotation (CEA) maps individual table cells to entities from the target KG.
Column Type Annotation (CTA) links individual table columns to classes from the target KG.
Column Property Annotation (CPA) detects the relations between column pairs from the target knowledge graph.
Row Annotation (RA) annotates the entire row to a KG entity or property.
DOI: 10.5281/zenodo.10283083
title: tBiodivL: Larger Semantic Table Annotations Benchmark for Biodiversity Domain

description: tBiodivL is a dataset for tabular data to knowledge graph matching. It is derived from the Biodiversity domain and has two types of tables. On the one hand, Horizontal Relational Tables are where each table represents a collection of entities. On the other hand, Entity Tables represent a single entity. We supported ground truth data from Wikidata as a target knowledge graph (KG).
tBiodivL is generated by KG2Tables using 10 levels of a recursive hierarchy of related concepts in Wikidata. It is the successor work of tBiodiv
tBiodivL contains 222,353 entity and horizontal tables, while this repository contains only a sample of 1% of the total generated tables of the entire benchmark with its ground truth data (gt). The Full size of this dataset is 312 GB. We will update this repository with the full dataset in the Future.
Please get in touch if you are interested in the full dataset, 
The supported tasks for semantic table annotations are: 

Topic Detection (TD) links the entire table to an entity or a class from the target KG.
Cell Entity Annotation (CEA) maps individual table cells to entities from the target KG.
Column Type Annotation (CTA) links individual table columns to classes from the target KG.
Column Property Annotation (CPA) detects the relations between column pairs from the target knowledge graph.
Row Annotation (RA) annotates the entire row to a KG entity or property.