cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1228605
title: 30000+ Job Postings From Emedcareers (Europe)

description: eMedCareers is a job search platform for pharmaceutical, biotechnology or healthcare jobs. This dataset contains 30000 latest job postings in Europe from eMedCareers job portal.

Following data fields are included in the dataset:


	category
	location
	company name
	job title
	job description
	job type
	salary offered
	posting date


<strong>Objectives</strong>

The uses of this dataset are endless. Some of the inspirations could be:


	Top paying companies
	Highest number of job posting by a particular company
	State/City with highest job openings
	Salary distribution by state
	Ratio of different job types


<strong>Background</strong>

This data was extracted using JobsPikr - a job data delivery platform that extracts job data from various company sites across the globe on daily basis powered by machine learning techniques.
DOI: 10.5281/zenodo.1228606
title: 30000+ Job Postings From Emedcareers (Europe)

description: eMedCareers is a job search platform for pharmaceutical, biotechnology or healthcare jobs. This dataset contains 30000 latest job postings in Europe from eMedCareers job portal.

Following data fields are included in the dataset:


	category
	location
	company name
	job title
	job description
	job type
	salary offered
	posting date


<strong>Objectives</strong>

The uses of this dataset are endless. Some of the inspirations could be:


	Top paying companies
	Highest number of job posting by a particular company
	State/City with highest job openings
	Salary distribution by state
	Ratio of different job types


<strong>Background</strong>

This data was extracted using JobsPikr - a job data delivery platform that extracts job data from various company sites across the globe on daily basis powered by machine learning techniques.
DOI: 10.5281/zenodo.1228866
title: Underwater Hyperspectral Scan From India, Scene 12

description: Underwater hyperspectral scan from India, scene 12

Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.
DOI: 10.5281/zenodo.1229435
title: Usp5 Zf-Ubd Fluorescence Polarization Displacement Assay #3

description: Development of a fluorescence polarization (FP) assay for USP5 zinc finger ubiquitin binding domain (Zf-UBD) with different lengths of N-terminal FITC-ubiquitin peptides.
description: Funding Acknowledgment: The SGC is a registered charity (number 1097737) that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, Canada Foundation for Innovation, Eshelman Institute for Innovation, Genome Canada through Ontario Genomics Institute [OGI-055], Innovative Medicines Initiative (EU/EFPIA) [ULTRA-DD grant no. 115766], Janssen, Merck KGaA, Darmstadt, Germany, MSD, Novartis Pharma AG, Ontario Ministry of Research, Innovation and Science (MRIS), Pfizer, São Paulo Research Foundation-FAPESP, Takeda, and Wellcome.
DOI: 10.5281/zenodo.1229436
title: Usp5 Zf-Ubd Fluorescence Polarization Displacement Assay #3

description: Development of a fluorescence polarization (FP) assay for USP5 zinc finger ubiquitin binding domain (Zf-UBD) with different lengths of N-terminal FITC-ubiquitin peptides.
description: Funding Acknowledgment: The SGC is a registered charity (number 1097737) that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, Canada Foundation for Innovation, Eshelman Institute for Innovation, Genome Canada through Ontario Genomics Institute [OGI-055], Innovative Medicines Initiative (EU/EFPIA) [ULTRA-DD grant no. 115766], Janssen, Merck KGaA, Darmstadt, Germany, MSD, Novartis Pharma AG, Ontario Ministry of Research, Innovation and Science (MRIS), Pfizer, São Paulo Research Foundation-FAPESP, Takeda, and Wellcome.
DOI: 10.23721/109/1434504
title: internet_address_census_standing_n-20170222 (02/22/2017 to 01/01/2020)

description: Applying to this standing dataset, will allow researchers to get the latest IPv4 census datasets as they become available. This subscription will last for one year, since i's approved. This standing request applies only to censuses conducted from SURFnet, located in Utrecht, The Netherlands. Please see e.g. dataset internet_address_census_it79n-20180213 for an example of a dataset covered by this standing request.
DOI: 10.5281/zenodo.1228880
title: Underwater Hyperspectral Scan From India, Scene 13

description: Underwater hyperspectral scan from India, scene 13

Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.
DOI: 10.5281/zenodo.1228881
title: Underwater Hyperspectral Scan From India, Scene 13

description: Underwater hyperspectral scan from India, scene 13

Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.
DOI: 10.5281/zenodo.1228890
title: Underwater Hyperspectral Scan From India, Scene 14

description: Underwater hyperspectral scan from India, scene 14

Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.
DOI: 10.5281/zenodo.1228891
title: Underwater Hyperspectral Scan From India, Scene 14

description: Underwater hyperspectral scan from India, scene 14

Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.
DOI: 10.5281/zenodo.1228511
title: Phenotype-Informed Noncoding Element Scoring

description: Functional characterization of the noncoding genome is essential for the biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring) which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. A unique feature of PINES is that analyses may be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal.
description: Annotations required to run the PINES software.
DOI: 10.5281/zenodo.1228512
title: Phenotype-Informed Noncoding Element Scoring

description: Functional characterization of the noncoding genome is essential for the biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring) which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. A unique feature of PINES is that analyses may be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal.
description: Annotations required to run the PINES software.
DOI: 10.5281/zenodo.1228902
title: Underwater Hyperspectral Scan From India, Scene 15

description: Underwater hyperspectral scan from India, scene 15

Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.
DOI: 10.5281/zenodo.1228903
title: Underwater Hyperspectral Scan From India, Scene 15

description: Underwater hyperspectral scan from India, scene 15

Used previously in publication Zimmermann, Nevala, Yoshimatsu et al., 2018, Current Biology, in press.
DOI: 10.5255/ukda-sn-853166
title: Reducing language barriers for Syrian refugees in Lebanon: Students' interviews
DOI: 10.17632/98b5sn2dzx.1
title: Data for: Cobalt Germanide Nanostructure Formation and Memory Characteristic Enhancement in Silicon Oxide Films

description: Figure S1. C-V characteristics of the annealed samples comprising charge trap layer with Co and Ge alone.
DOI: 10.17632/h55fyp7zbm.1
title: Data for: A Meta-analysis of coal mining induced subsidence data and implications for future use in the carbon industry

description: Excel spreadsheet shows the subsidence data used in the paper.
DOI: 10.6073/pasta/1faf076bc9af6ff2bfad9adab5d09970
title: Stream water chemistry data for Albion site, 1982 - ongoing.

description: This is a summary of major ion concentrations for stream water samples collected at the Albion site, at the lower end of Green Lakes Valley at the road crossing (culvert) at the Albion camp.
DOI: 10.48432/xch4vm
title: LiDAR survey on 3000 hectares in Feliz Natal and Nova Ubiratã, Mato Grosso, Brasil in 2017.

description: The LiDAR data Feliz Natal and Nova Ubiratã municipalities (FN3_A01_2017_LiDAR) refers to the survey carried out in Feliz Natal and Nova Ubiratã municipalities, Mato Grosso state, Brazil. The data were collected under the project Sustainable Landscapes, a project supported by the United States Agency for International Development (USAID) and US Department of State. The United States Forest Service working in collaboration with the Brazilian Agricultural Research Corporation (Embrapa) have made possible the provision of high accuracy LiDAR data aiming at developing new methods and generating knowledge in the field. This dataset contains: a. Classified LAS formatted point cloud data (vendor delivered); b. Digital Terrain Model (vendor delivered); c. Map of LiDAR coverage area and block boundaries; “Data were acquired by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (Embrapa), the US Forest Service, USAID and the US Department of State."
DOI: 10.5281/zenodo.8102783
title: Multi-Scale Representation Learning on Proteins

description: Protein-ligand structures from PDBBind (v2019), and protein structures from the enzyme dataset as described in the paper: "Multi-Scale Representation Learning on Proteins" with associated code at https://github.com/vsomnath/holoprot. <strong>Paper Abstract</strong> Proteins are fundamental biological entities mediating key roles in cellular function and disease. This paper introduces a multi-scale graph construction of a protein – HoloProt – connecting surface to structure and sequence. The surface captures coarser details of the protein, while sequence as primary component and structure – comprising secondary and tertiary components – capture finer details. Our graph encoder then learns a multi-scale representation by allowing each level to integrate the encoding from level(s) below with the graph at that level. We test the learned representation on different tasks, (i.) ligand binding affinity (regression), and (ii.) protein function prediction (classification). On the regression task, contrary to previous methods, our model performs consistently and reliably across different dataset splits, outperforming all baselines on most splits. On the classification task, it achieves a performance close to the top-performing model while using 10x fewer parameters. To improve the memory efficiency of our construction, we segment the multiplex protein surface manifold into molecular superpixels and substitute the surface with these superpixels at little to no performance loss.
DOI: 10.5281/zenodo.8102782
title: Multi-Scale Representation Learning on Proteins

description: Protein-ligand structures from PDBBind (v2019), and protein structures from the enzyme dataset as described in the paper: "Multi-Scale Representation Learning on Proteins" with associated code at https://github.com/vsomnath/holoprot. <strong>Paper Abstract</strong> Proteins are fundamental biological entities mediating key roles in cellular function and disease. This paper introduces a multi-scale graph construction of a protein – HoloProt – connecting surface to structure and sequence. The surface captures coarser details of the protein, while sequence as primary component and structure – comprising secondary and tertiary components – capture finer details. Our graph encoder then learns a multi-scale representation by allowing each level to integrate the encoding from level(s) below with the graph at that level. We test the learned representation on different tasks, (i.) ligand binding affinity (regression), and (ii.) protein function prediction (classification). On the regression task, contrary to previous methods, our model performs consistently and reliably across different dataset splits, outperforming all baselines on most splits. On the classification task, it achieves a performance close to the top-performing model while using 10x fewer parameters. To improve the memory efficiency of our construction, we segment the multiplex protein surface manifold into molecular superpixels and substitute the surface with these superpixels at little to no performance loss.
DOI: 10.6084/m9.figshare.23600310
title: Repurposing Public 23Q2

description: This data release contains the most recent PRISM Repurposing screens: Repurposing-1M. All Repurposing-1M [REP1M] compounds (1280 REP1M) were screened in the PRISM assay at a dose of 2.5 μM with a 5-day treatment against 906 cancer cell lines (859 of them passed quality checks -QC- for all tested compound with two high quality replicates). Two PRISM cell line collections were used in the assay: PR500A, which includes only adherent cell lines, and PR500B, which has adherent and suspension cell lines. Together, these PRISM cell line collections form the PR1000 cell line collection. All compounds were run in triplicate, and each plate contained positive (Bortezomib, 20μM) and negative (DMSO) controls. The screen can be considered an extension of the PRISM Repurposing Primary Screen, with PR500A mainly covering the existing cell line panel, while PR500B extends the cell line collection with new subtypes and lineages. For the assay details, please refer to Corsello et al., 2020 (doi.org/10.1038/s43018-019-0018-6) and https://www.theprismlab.org.
DOI: 10.48432/lxoxcc
title: LiDAR survey on 500 hectares of Talismã, Rio Branco, Acre, Brasil in 2018.

description: The LiDAR data Talismã (TAL_A01_2018_LiDAR) refers to the survey carried out in Rio Branco municipality, Acre state, Brazil. The data were collected under the project Sustainable Landscapes, a project supported by the United States Agency for International Development (USAID) and US Department of State. The United States Forest Service working in collaboration with the Brazilian Agricultural Research Corporation (Embrapa) have made possible the provision of high accuracy LiDAR data aiming at developing new methods and generating knowledge in the field. This dataset contains: a. Classified LAS formatted point cloud data (vendor delivered); b. Digital Terrain Model (vendor delivered); c. Map of LiDAR coverage area and block boundaries; “Data were acquired by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (Embrapa), the US Forest Service, USAID and the US Department of State."
DOI: 10.48366/r600109
title: Efficiency of Landsat mutispectral data in crop type mapping, health monitoring and classification

description: Landsat multispectral data is widely recognized as a valuable and efficient resource for crop type mapping, health monitoring, and classification due to its unique combination of spatial, spectral, and temporal characteristics. This comparison shows the capabilities of different Landsat sensors in agricultural applications.
DOI: 10.25345/c5794146d
title: MassIVE MSV000092323 - Metabolism of halauxifen-methyl is regulated by genes on chromosome 5A of wheat (Triticum aestivum L.) [alien substitution lines]
title: Metabolism of halauxifen-methyl is regulated by genes on chromosome 5A of wheat (Triticum aestivum L.) [alien substitution lines] experiment 1
DOI: 10.5281/zenodo.8102860
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Friedrich Merklein, http://www.wikidata.org/entity/Q36555949. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q36555949
DOI: 10.5281/zenodo.8102859
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Friedrich Merklein, http://www.wikidata.org/entity/Q36555949. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q36555949
DOI: 10.5281/zenodo.8102862
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Herman Mervielde, http://www.wikidata.org/entity/Q62990521. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q62990521
DOI: 10.5281/zenodo.8102861
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Herman Mervielde, http://www.wikidata.org/entity/Q62990521. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q62990521
DOI: 10.5281/zenodo.8102865
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Richard Liron Mestayer, http://www.wikidata.org/entity/Q107373824. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q107373824
DOI: 10.5281/zenodo.8102864
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Richard Liron Mestayer, http://www.wikidata.org/entity/Q107373824. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q107373824
DOI: 10.5281/zenodo.8102805
title: Planet Four Data Catalog

description: This is the data catalog for the paper: Planet Four: Probing springtime winds on Mars by mapping the southern polar CO2 jet deposits The catalog can be automatically retrieved from here using the Python package p4tools
DOI: 10.5281/zenodo.8102804
title: Planet Four Data Catalog

description: This is the data catalog for the paper: Planet Four: Probing springtime winds on Mars by mapping the southern polar CO2 jet deposits The catalog can be automatically retrieved from here using the Python package p4tools
DOI: 10.5281/zenodo.8102867
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Franz Meyen, http://www.wikidata.org/entity/Q61546. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q61546
DOI: 10.5281/zenodo.8102866
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Franz Meyen, http://www.wikidata.org/entity/Q61546. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q61546
DOI: 10.6084/m9.figshare.23612016
title: Supplemental File 1 - Figure Data.xlsx

description: Raw data from evaluation of a novel CCR5 inhibitor that was evaluated for potential to block SARS-CoV-2 replication in a hamster model of infection.
DOI: 10.6084/m9.figshare.23612016.v1
title: Supplemental File 1 - Figure Data.xlsx

description: Raw data from evaluation of a novel CCR5 inhibitor that was evaluated for potential to block SARS-CoV-2 replication in a hamster model of infection.
DOI: 10.5281/zenodo.8102870
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Otto Meyer, http://www.wikidata.org/entity/Q114897053. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q114897053
DOI: 10.5281/zenodo.8102869
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Otto Meyer, http://www.wikidata.org/entity/Q114897053. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q114897053
DOI: 10.5281/zenodo.8102872
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Evangelia Micheli-Tzanakou, http://www.wikidata.org/entity/Q5415626. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q5415626
DOI: 10.5281/zenodo.8102871
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Evangelia Micheli-Tzanakou, http://www.wikidata.org/entity/Q5415626. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q5415626
DOI: 10.7925/drs1.duchas_4924873
title: Weather-Lore

description: Story collected by Michael Parker, a student at Cluain Catha school (Clooncah, Co. Roscommon) from informant Mary E. Parker.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher Dll. Ó -- Eoghain, Ss. Mac Módhráin.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_5054798
title: Folklore - Forts

description: Story collected by Senan Cushen, a student at Liathdruim, Cill Mhichíl school (Leitrim, Co. Clare) from informant Michael Cushen.
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher P. Ó Conbhaidhe.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.
DOI: 10.7925/drs1.duchas_4650411
title: Hidden Treasures

description: Story collected by a student at An Clochar, Cara Droma Ruisc school (Carrick-on-Shannon, Co. Leitrim) (no informant identified).
description: Collected as part of the Schools' Folklore scheme, 1937-1938, under the supervision of teacher An tSr. Emerentia.
description: Supported by funding from the Department of Arts, Heritage and the Gaeltacht (Ireland), University College Dublin, and the National Folklore Foundation (Fondúireacht Bhéaloideas Éireann), 2014-2016.