cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/dg2x-1246
title: MIT DriveSeg (Semi-auto) Dataset

description: Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare out- lier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task. We hereby present the MIT DriveSeg (Semi-auto) Dataset: a large-scale video driving scene dataset, which contains 20,100 video frames with pixel-wise semantic annotation. We propose semi-automatic annotation approaches leveraging both manual and computational efforts to annotate the data more efficiently and at lower cost than manual annotation.
DOI: 10.21227/vb68-cz76
title: MIT DriveSeg (Semi-auto) Dataset

description: Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare out- lier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task. We hereby present the MIT DriveSeg (Semi-auto) Dataset: a large-scale video driving scene dataset, which contains 20,100 video frames with pixel-wise semantic annotation. We propose semi-automatic annotation approaches leveraging both manual and computational efforts to annotate the data more efficiently and at lower cost than manual annotation.
DOI: 10.21227/gt8d-wa87
title: MIT DriveSeg (Semi-auto) Dataset

description: Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare out- lier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task. We hereby present the MIT DriveSeg (Semi-auto) Dataset: a large-scale video driving scene dataset, which contains 20,100 video frames with pixel-wise semantic annotation. We propose semi-automatic annotation approaches leveraging both manual and computational efforts to annotate the data more efficiently and at lower cost than manual annotation.
DOI: 10.21227/n64b-nt45
title: MIT DriveSeg (Semi-auto) Dataset

description: Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare outlier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task. We hereby present the MIT DriveSeg (Semi-auto) Dataset: a large-scale video driving scene dataset, which contains 20,100 video frames with pixel-wise semantic annotation. We propose semi-automatic annotation approaches leveraging both manual and computational efforts to annotate the data more efficiently and at lower cost than manual annotation.
DOI: 10.25345/c5z710
title: MassIVE MSV000085547 - Phosphoproteome Profiling of the Receptor Tyrosine Kinase MuSK Identifies Tyrosine Phosphorylation of Rab GTPases.
title: Muscle-specific receptor tyrosine kinase (MuSK) agonist antibodies were developed two decades ago to explore the benefits of receptor activation at the neuromuscular junction. Unlike agrin, the endogenous agonist of MuSK, MuSK agonist antibodies function independently of its co-receptor Lrp4. Recent reports suggest that MuSK agonist antibodies can delay the onset of muscle denervation in mouse models of amyotrophic lateral sclerosis (ALS). To get a deeper understanding of MuSK signaling, and potentially identify Lrp4-independent signaling downstream of MuSK activation, we performed phosphoproteome profiling of myotubes treated with each agonist. Our approach involved quantifying changes in site-specific phosphorylation in orthogonal dose response and time course experiments with each agonist using isobaric mass tags. We observed that both agonists elicited remarkably similar intracellular responses, as evidenced by highly correlating tyrosine phosphorylation on known MuSK signaling components and newly identified cytoskeletal and intracellular trafficking nodes. Among these was inducible phosphorylation on a conserved N-terminal phosphorylation site present on a family of endosomal Rab GTPases. We confirmed that suppression of MuSK signaling by a small molecule reduces Rab10 tyrosine phosphorylation. Importantly, Rab10 mutation at this site disrupts its association with adaptor molecules Mical1 and Mical3. Together these data provide an in depth characterization of the MuSK signaling pathway, describe two small molecule kinase inhibitors, and reveal a novel regulatory mechanism of small Rab GTPases that is poised to regulate intracellular trafficking downstream of receptor tyrosine kinase activation.
DOI: 10.21227/nteh-q788
title: MIT DriveSeg (Semi-auto) Dataset

description: Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare outlier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task. We hereby present the MIT DriveSeg (Semi-auto) Dataset: a large-scale video driving scene dataset, which contains 20,100 video frames with pixel-wise semantic annotation. We propose semi-automatic annotation approaches leveraging both manual and computational efforts to annotate the data more efficiently and at lower cost than manual annotation.
DOI: 10.21227/yygg-gs85
title: MIT DriveSeg (Semi-auto) Dataset

description: Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare outlier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task. We hereby present the MIT DriveSeg (Semi-auto) Dataset: a large-scale video driving scene dataset, which contains 20,100 video frames with pixel-wise semantic annotation. We propose semi-automatic annotation approaches leveraging both manual and computational efforts to annotate the data more efficiently and at lower cost than manual annotation.
DOI: 10.5281/zenodo.3875139
title: Five years of pharmaceutical industry funding of patient organisations in Sweden: cross-sectional study of companies, patient organisations and drugs

description: <strong>Abstract</strong> <strong>Background:</strong> Many patient organisations collaborate with drug companies, resulting in concerns about commercial agendas influencing patient advocacy. We contribute to an international body of knowledge on patient organisation-industry relations by considering payments reported in the industry’s centralised ‘collaboration database’ in Sweden. We also investigate possible commercial motives behind the funding by assessing its association with drug commercialisation. <strong>Methods:</strong> Our primary data source were 1,337 payment reports from 2014-2018. After extraction and coding, we analysed the data descriptively, calculating the number, value and distribution of payments for various units of analysis, e.g. individual companies, diseases and payment goals. The association between drug commercialisation and patient organisation funding was assessed by, first, the concordance between leading companies marketing drugs in specific diseases and their funding of corresponding patient organisations and, second, the correlation between new drugs in broader condition areas and payments to corresponding patient organisations. <strong>Results: </strong>46 companies reported paying €6,449.224 (median €2,411; IQR €1,024-4,569) to 77 patient organisations, but ten companies provided 67% of the funding. Small payments dominated, many of which covered costs of events organised by patient organisations. An association existed between drug commercialisation and industry funding. Companies supported patient organisations in diseases linked to their drug portfolios, with the top 3 condition areas in terms of funding – cancer endocrine, nutritional and metabolic disorders; and infectious and parasitic disorders – accounting for 63% of new drugs and 56% of the funding. <strong>Conclusion:</strong> This study reveals close and widespread ties between patient organisations and drug companies. Relatively few companies dominated the funding landscape by supporting patient organisations in disease areas linked to their drug portfolios. This commercially motivated funding may contribute to inequalities in resource and influence between patient organisations. The association between drug commercialisation and industry funding is also worrying because of the therapeutic uncertainty of many new drugs. Our analysis benefited from the existence of a centralised database of payments – which should be adopted by other countries too – but databases should be downloadable in an analysable format to permit efficient and independent analysis.
DOI: 10.5281/zenodo.3876532
title: Supplementary data : Heritable gene expression variability and stochasticity govern clonal heterogeneity in circadian period

description: BAM files from exome sequencing of 7 U2OS clonal cells used in the publication titled: Heritable gene expression variability and stochasticity govern clonal heterogeneity in circadian period. Details of samples names are in 'sample details.txt'
DOI: 10.5281/zenodo.3876533
title: Supplementary data : Heritable gene expression variability and stochasticity govern clonal heterogeneity in circadian period

description: BAM files from exome sequencing of 7 U2OS clonal cells used in the publication titled: Heritable gene expression variability and stochasticity govern clonal heterogeneity in circadian period. Details of samples names are in 'sample details.txt'
DOI: 10.21227/4e51-ys30
title: MIT DriveSeg (Semi-auto) Dataset

description: Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare outlier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task. We hereby present the MIT DriveSeg (Semi-auto) Dataset: a large-scale video driving scene dataset, which contains 20,100 video frames with pixel-wise semantic annotation. We propose semi-automatic annotation approaches leveraging both manual and computational efforts to annotate the data more efficiently and at lower cost than manual annotation.
DOI: 10.21227/drvv-w057
title: MIT DriveSeg (Semi-auto) Dataset

description: Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare outlier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task. We hereby present the MIT DriveSeg (Semi-auto) Dataset: a large-scale video driving scene dataset, which contains 20,100 video frames with pixel-wise semantic annotation. We propose semi-automatic annotation approaches leveraging both manual and computational efforts to annotate the data more efficiently and at lower cost than manual annotation.
DOI: 10.5281/zenodo.3876143
title: The rat brain transcripts after cocaine abstinence and relapse: data from the Maj Institute of Pharmacology Polish Academy of Sciences

description: Substantially altered transcripts in the rat PFC during early (3. day) cocaine abstinence with extinction training identify by microarray analysis – supplementary material – Neurotoxicity research, 2017. Supported by the National Science Centre grant no. UMO-2012/06/A/NZ3/00022
DOI: 10.5281/zenodo.3876144
title: The rat brain transcripts after cocaine abstinence and relapse: data from the Maj Institute of Pharmacology Polish Academy of Sciences

description: Substantially altered transcripts in the rat PFC during early (3. day) cocaine abstinence with extinction training identify by microarray analysis – supplementary material – Neurotoxicity research, 2017. Supported by the National Science Centre grant no. UMO-2012/06/A/NZ3/00022
DOI: 10.18154/rwth-2020-02899
title: Documentation of Supplementary Data-Set on Listening Experiments published in the PhD-thesis: "Examining Auditory Selective Attention: From Dichotic towards Realistic Environments"

description: Eight listening experiments are described and analyzed in the PhD-thesis "Examining Auditory Selective Attention: From Dichotic towards Realistic Environments" by Josefa Oberem. This documentation offers a detailed description of processing and analyzing steps as well as open access to the supplementary data-set of these experiments.
DOI: 10.21227/22v4-de79
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about "corona", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that contained the exact location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 150+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.25387/g3.12001344
title: Supplemental Material for Baker et al., 2020

description: These files are the genotyping datasets that were used to perform the analyses in Baker et al. 2020. The filesets represent individuals that were recruited from University of Wisconsin-Madison and from Cornell University. Filesets that include 0.7 in the filename have been pruned for LD at r<sup>2</sup> greater than or equal to 0.7. This file contains the code used for all genomic prediction analyses in R markdown .htm format.
DOI: 10.3886/e115211
title: Data and Code for: The Marginal Propensity to Consume over the Business Cycle

description: We estimate how the marginal propensity to consume (MPC) out of liquidity varies over the business cycle. Ten years after a Chapter 7 bankruptcy, the bankruptcy flag is removed from the filer's credit report, generating an increase in credit score. In the year following flag removal, credit card limits increase by $778 and credit card balances increase by $290, implying an MPC of 0.37. Using cohorts of flag removals, we find that the MPC was 20 to 30 percent higher during the Great Recession, increased during the 2001 recession, and is positively correlated with the local unemployment rate. <br>
DOI: 10.3886/e115211v1
title: Data and Code for: The Marginal Propensity to Consume over the Business Cycle

description: We estimate how the marginal propensity to consume (MPC) out of liquidity varies over the business cycle. Ten years after a Chapter 7 bankruptcy, the bankruptcy flag is removed from the filer's credit report, generating an increase in credit score. In the year following flag removal, credit card limits increase by $778 and credit card balances increase by $290, implying an MPC of 0.37. Using cohorts of flag removals, we find that the MPC was 20 to 30 percent higher during the Great Recession, increased during the 2001 recession, and is positively correlated with the local unemployment rate. <br>
DOI: 10.21227/jhee-3t59
title: MIT DriveSeg (Semi-auto) Dataset

description: Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare outlier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task. We hereby present the MIT DriveSeg (Semi-auto) Dataset: a large-scale video driving scene dataset, which contains 20,100 video frames with pixel-wise semantic annotation. We propose semi-automatic annotation approaches leveraging both manual and computational efforts to annotate the data more efficiently and at lower cost than manual annotation.
DOI: 10.6073/pasta/d3ef0be753a12d2b3c0224914b4b5265
title: SIC01 Isotopic composition of select archived soil cores from Konza Prairie

description: The concentration and isotopic composition of soil carbon and nitrogen were measured from select archived soil cores originally collected for the NSC01 dataset using an isotope ratio mass spectrometer coupled with an elemental analyzer. These soil cores were collected from the lowlands (25 cm depth) of four experimental watersheds in 1982, 1987, 2002, 2010, and 2015. The four experimental watersheds are 001d, n01b, 020b, and n20b.
DOI: 10.21227/11pr-6b75
title: Robot Dynamics Identification

description: This matlab code allows to reproduce some of the constrained and unconstrained dynamic identification techniques for open-chain robots. Below you can find an summary of the underlying research."Identification of dynamic parameters of robots is a long standing topic in robot control. Recently, the research witnessed a renewal of the activity due to some interesting results concerning issues involving the physical consistency of the obtained parameters leading to constraints, for instance, on mass, first moment of mass and inertia matrix of each rigid link of the robot. This is motivated by high performance required by model-based control of, for example, legged or surgical robots and to obtain realistic simulations of the same systems. The objective of this article is twofold. First, we review and experimentally compare on a Jaco 7 Degrees of Freedom anthropomorphic arm the latest results on the identification of the dynamic parameters which range from Least Mean Squares to constrained non-linear optimisation and Linear-Matrix-Inequality Semi-Definite-Programming methods. Second, motivated by the increasing attention paid to measurable robotics and for the sake of reproducibility, data and code (which can be easily adapted to consider any open-chain manipulator) are made available to the community for improvement and to run further comparisons. As indirect but not less important contribution, this paper provides a first physical fully consistent identified rigid dynamic model of theJaco robot publicly made available to the robotic community."
DOI: 10.21227/9rdm-9s26
title: MIT DriveSeg (Semi-auto) Dataset

description: Solving the external perception problem for autonomous vehicles and driver-assistance systems requires accurate and robust driving scene perception in both regularly-occurring driving scenarios (termed “common cases”) and rare outlier driving scenarios (termed “edge cases”). In order to develop and evaluate driving scene perception models at scale, and more importantly, covering potential edge cases from the real world, we take advantage of the MIT-AVT Clustered Driving Scene Dataset and build a subset for the semantic scene segmentation task. We hereby present the MIT DriveSeg (Semi-auto) Dataset: a large-scale video driving scene dataset, which contains 20,100 video frames with pixel-wise semantic annotation. We propose semi-automatic annotation approaches leveraging both manual and computational efforts to annotate the data more efficiently and at lower cost than manual annotation.
DOI: 10.21227/z78q-b367
title: Mold Materials for an Artificial Pancreas Phantom

description: This data set contains all relevant 3D mold files for modeling a pancreatic phantom described in the publication:Benjamin Eigl et al.: A Multimodal Pancreas Phantom for Computer-Assisted Surgery Training.https://ieeexplore.ieee.org/document/9107339
DOI: 10.5281/zenodo.3877517
title: QualyGridS project abstract

description: Project abstract as submitted to the Call for proposals
DOI: 10.5281/zenodo.3877518
title: QualyGridS project abstract

description: Project abstract as submitted to the Call for proposals
DOI: 10.15468/nqcxmf
title: A revision of Xylopia L. (Annonaceae): the species of Madagascar and the Mascarene islands

description: This dataset contains the digitized treatments in Plazi based on the original journal article Johnson, David M., Murray, Nancy A. (2020): A revision of Xylopia L. (Annonaceae): the species of Madagascar and the Mascarene islands. Adansonia s?r. 3 42 (1): 1-88, DOI: 10.5252/adansonia2020v42a1