cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/4ef76e219da2565476d6f4e749383773
title: UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Tidal Prism

description: These data describe estimates of tidal prism at the San Dieguito Wetland as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program to track long-term patterns of tidal prism. This study began in 2012.
DOI: 10.6073/pasta/f618795e6b53bc4758242c6ce5df6e56
title: UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Water Quality

description: These data describe annual estimates of wetland water quality, measured as the average duration of hypoxia (time dissolved oxygen concentration below 3 mg/l), collected as part of the SONGS San Dieguito Wetland Restoration mitigation monitoring program designed to evaluate compliance of the restoration project with conditions of the SONGS permit. This study began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Point Mugu Lagoon in Ventura County, CA.
DOI: 10.5281/zenodo.7940782
title: Dataset of the study: "Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard"

description: This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot. This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.
DOI: 10.5281/zenodo.7940781
title: Dataset of the study: "Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard"

description: This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot. This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.
DOI: 10.5281/zenodo.7951690
title: Dataset of the study: "Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard"

description: This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot. This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.
DOI: 10.5281/zenodo.7951710
title: Dataset of the study: "Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard"

description: This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot. This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.
DOI: 10.25345/c5hd7p331
title: MassIVE MSV000091995 - MYBPC3_dephosphorylation_PP1_PP2A
title: Bisphosphorylated cMyBP-C following dephosphorylation of pS279,pS288,pS308,pS313-cMyBPC by PP1 or PP2A. Samples analysed by BIOSCIENCE TECHNOLOGY FACILITY METABOLOMICS AND PROTEOMICS LABORATORY (BTF-MAP lab).
DOI: 10.5281/zenodo.7951627
title: Particle filter meets hybrid octrees: an octree-based ground vehicle localization approach without learning

description: This paper proposes an accurate lidar-based outdoor localization method that requires few computational resources, is robust in challenging environments (urban, off-road, seasonal variations) and whose performances are equivalent for two different sensor technologies: scanning LiDAR and flash LiDAR. The method is based on the matching between a pre-built 3D map and the LiDAR measurements. Our contribution lies in the combined use of a particle filter with a hybrid octree to reduce the memory footprint of the map and significantly decrease the computational load for online localization. The design of the algorithm allows it to run on both CPU and GPU with equivalent performance. We have evaluated our approach on the KITTI dataset and obtained good results compared to the state of the art. This paper introduces the baseline performance on a multi-seasonal dataset we are publicly releasing to the community. We have shown that the same localization algorithms and parameters can perform well in urban environments and can be extended to off-road environments. We have also evaluated the robustness of our method when masking angular sectors of the LiDAR field of view to reproduce edgecases scenarios in urban environments where the LiDAR field is partially occulted by another vehicle (bus, truck). Finally, experiments have been carried out with two distinctive scanning and flash LiDAR technologies. The performance achieved with the flash LiDAR is close to the scanning LiDAR despite different resolutions and sensing modalities. The positioning performance is significant with 10cm and 0.12° angular RMSE for both technologies. We validated our approach in an off-road environment from a front view field of view with only 768 LiDAR points.
description: Details about dataset can be found here https://github.com/vauchey/GroundTruthHighAccuracyDataset/tree/master
DOI: 10.5281/zenodo.7951626
title: Particle filter meets hybrid octrees: an octree-based ground vehicle localization approach without learning

description: This paper proposes an accurate lidar-based outdoor localization method that requires few computational resources, is robust in challenging environments (urban, off-road, seasonal variations) and whose performances are equivalent for two different sensor technologies: scanning LiDAR and flash LiDAR. The method is based on the matching between a pre-built 3D map and the LiDAR measurements. Our contribution lies in the combined use of a particle filter with a hybrid octree to reduce the memory footprint of the map and significantly decrease the computational load for online localization. The design of the algorithm allows it to run on both CPU and GPU with equivalent performance. We have evaluated our approach on the KITTI dataset and obtained good results compared to the state of the art. This paper introduces the baseline performance on a multi-seasonal dataset we are publicly releasing to the community. We have shown that the same localization algorithms and parameters can perform well in urban environments and can be extended to off-road environments. We have also evaluated the robustness of our method when masking angular sectors of the LiDAR field of view to reproduce edgecases scenarios in urban environments where the LiDAR field is partially occulted by another vehicle (bus, truck). Finally, experiments have been carried out with two distinctive scanning and flash LiDAR technologies. The performance achieved with the flash LiDAR is close to the scanning LiDAR despite different resolutions and sensing modalities. The positioning performance is significant with 10cm and 0.12° angular RMSE for both technologies. We validated our approach in an off-road environment from a front view field of view with only 768 LiDAR points.
description: Details about dataset can be found here https://github.com/vauchey/GroundTruthHighAccuracyDataset/tree/master
DOI: 10.6084/m9.figshare.22972988
title: Getting a shell data set

description: Data set from paper Getting a Shell From Whom
DOI: 10.6084/m9.figshare.22972988.v1
title: Getting a shell data set

description: Data set from paper Getting a Shell From Whom
DOI: 10.34740/kaggle/dsv/5725794
title: Easiest Diabetes Classification Dataset
DOI: 10.6084/m9.figshare.22973168
title: smartphone SPSS Data.sav

description: Neck pain associated with smartphone usage among university students-Set of Data
DOI: 10.6084/m9.figshare.22969568
title: L3MBTL2_CUTRUN_mESC_WT_rep1.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969568.v1
title: L3MBTL2_CUTRUN_mESC_WT_rep1.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.34740/kaggle/dsv/5720394
title: USGS Oak Creek near Sedona, AZ (1986-2023)
DOI: 10.6084/m9.figshare.22969571
title: L3MBTL2_CUTRUN_mESC_Zmym2KO_IgG_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969571.v1
title: L3MBTL2_CUTRUN_mESC_Zmym2KO_IgG_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969574
title: L3MBTL2_CUTRUN_mESC_WT_IgG_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969574.v1
title: L3MBTL2_CUTRUN_mESC_WT_IgG_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969577
title: L3MBTL2_CUTRUN_mESC_Zmym2KO_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969577.v1
title: L3MBTL2_CUTRUN_mESC_Zmym2KO_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.34740/kaggle/dsv/5725830
title: USGS Mississippi River at Baton Rouge (2004-2023)
DOI: 10.6084/m9.figshare.22969586
title: L3MBTL2_CUTRUN_mESC_Zmym2KO_rep1.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969586.v1
title: L3MBTL2_CUTRUN_mESC_Zmym2KO_rep1.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969295
title: H3K4me3_EmbryoidBody_WT_rep1.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969295.v1
title: H3K4me3_EmbryoidBody_WT_rep1.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969298
title: H3K4me3_EmbryoidBody_WT_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969298.v1
title: H3K4me3_EmbryoidBody_WT_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969301
title: H3K4me3_EmbryoidBody_Zmym2KO_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969301.v1
title: H3K4me3_EmbryoidBody_Zmym2KO_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969304
title: H3K4me3_input_EmbryoidBody_WT_rep1.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969304.v1
title: H3K4me3_input_EmbryoidBody_WT_rep1.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969307
title: H3K4me3_input_mESC_WT_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969307.v1
title: H3K4me3_input_mESC_WT_rep2.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969310
title: H3K4me3_EmbryoidBody_Zmym2KO_rep1.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22969310.v1
title: H3K4me3_EmbryoidBody_Zmym2KO_rep1.bwZmym2 NGS Data

description: NGS WGBS and ChIPseq data processed into bigWigs
DOI: 10.6084/m9.figshare.22973654
title: TEST

description: TEST XYZ STRUCTURES
DOI: 10.6084/m9.figshare.22815887
title: Comparative proteomic analysis of cerebral cortex revealed neuroprotective mechanism of esculentoside A on Alzheimer’s disease.

description: Esculentoside A (EsA), isolated from phytolacca esculenta, is a saponin showing neuroprotective effect in the mouse models of Alzheimer’s disease (AD). To investigate its action target and underlying mechanism, this study used isobaric tags for relative and absolute quantification (iTRAQ) proteomics to analyze the differentially expressed proteins (DEPs) in the cerebral cortex of EsA-treated and untreated triple-transgenic 3×Tg-AD model mice. Proteomic comparison revealed 250, 436, and 903 DEPs in three group pairs, i.e. AD/WT, AD+5mg/kg EsA/AD, AD+10mg/kg EsA /AD, respectively. Among them 28 DEPs were commonly shared by three group pairs, and 25 of them showed reversed expression levels in the diseased group under the treatment both doses of EsA. Bioinformatics analysis revealed that these DEPs were mainly linked to metabolism, synapses, apoptosis, learning and memory. EsA treatment restored the expression of these proteins, including amyloid precursor protein (APP), Cathepsin B (Cstb), 4-aminobutyrate aminotransferase (Abat), 3-phosphoinositide-dependent protein kinase-1 (PDK1), carnitine palmitoyltransferase1 (Cpt1) and synaptotagmin 17 (Syt17), thereby ameliorated the spatial learning and memory of AD mice. Collectively, this study reveals for the first time the profound effect of EsA on the cerebral cortex of AD mice, which might be a potential therapeutic agent for the treatment of AD.
DOI: 10.6084/m9.figshare.22815887.v1
title: Comparative proteomic analysis of cerebral cortex revealed neuroprotective mechanism of esculentoside A on Alzheimer’s disease.

description: Esculentoside A (EsA), isolated from phytolacca esculenta, is a saponin showing neuroprotective effect in the mouse models of Alzheimer’s disease (AD). To investigate its action target and underlying mechanism, this study used isobaric tags for relative and absolute quantification (iTRAQ) proteomics to analyze the differentially expressed proteins (DEPs) in the cerebral cortex of EsA-treated and untreated triple-transgenic 3×Tg-AD model mice. Proteomic comparison revealed 250, 436, and 903 DEPs in three group pairs, i.e. AD/WT, AD+5mg/kg EsA/AD, AD+10mg/kg EsA /AD, respectively. Among them 28 DEPs were commonly shared by three group pairs, and 25 of them showed reversed expression levels in the diseased group under the treatment both doses of EsA. Bioinformatics analysis revealed that these DEPs were mainly linked to metabolism, synapses, apoptosis, learning and memory. EsA treatment restored the expression of these proteins, including amyloid precursor protein (APP), Cathepsin B (Cstb), 4-aminobutyrate aminotransferase (Abat), 3-phosphoinositide-dependent protein kinase-1 (PDK1), carnitine palmitoyltransferase1 (Cpt1) and synaptotagmin 17 (Syt17), thereby ameliorated the spatial learning and memory of AD mice. Collectively, this study reveals for the first time the profound effect of EsA on the cerebral cortex of AD mice, which might be a potential therapeutic agent for the treatment of AD.
DOI: 10.7910/dvn/pdz08b
title: Replication Data for: Does Economics Make You Sexist?

description: Review of Economics and Statistics: Forthcoming
DOI: 10.5519/qd.ll1ixg7d
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1037833 records)
DOI: 10.25921/2gnq-e445
title: EM710 Water Column Sonar Data Collected During NF1502-03
DOI: 10.5519/qd.z3284ch6
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (6640 records)
DOI: 10.5445/ir/1000158825
title: Laboratory Equipment, Software, Consumables, and Tribological Specimens for Tribological Experiments - Sapphire on Copper - FAIR Dataset
DOI: 10.34740/kaggle/dsv/5726056
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.6084/m9.figshare.22413970
title: Polygenic scoring accuracy varies across the genetic ancestry continuum

description: This folder contains the MCMC sampling of posterior genetic effects for the 84 traits investigated in the manuscript "Polygenic scoring accuracy varies across the genetic ancestry continuum".
DOI: 10.6084/m9.figshare.22413970.v3
title: Polygenic scoring accuracy varies across the genetic ancestry continuum

description: This folder contains the MCMC sampling of posterior genetic effects for the 84 traits investigated in the manuscript "Polygenic scoring accuracy varies across the genetic ancestry continuum".
DOI: 10.23641/asha.22056701.v1
title: The effects of occupational noise exposure and age (Shehabi et al., 2023)

description: <strong>Purpose: </strong>Many workers in developing countries are exposed to unsafe occupational noise due to inadequate health and safety practices. We tested the hypotheses that occupational noise exposure and aging affect speech-perception-in-noise (SPiN) thresholds, self-reported hearing ability, tinnitus presence, and hyperacusis severity among Palestinian workers. <strong>Method:</strong> Palestinian workers (<em>N</em> = 251, aged 18–70 years) without diagnosed hearing or memory impairments completed online instruments including a noise exposure questionnaire; forward and backward digit span tests; hyperacusis questionnaire; the short-form Speech, Spatial and Qualities of Hearing Scale (SSQ12); the Tinnitus Handicap Inventory; and a digits-in-noise (DIN) test. Hypotheses were tested via multiple linear and logistic regression models, including age and occupational noise exposure as predictors, and with sex, recreational noise exposure, cognitive ability, and academic attainment as covariates. Familywise error rate was controlled across all 16 comparisons using the Bonferroni–Holm method. Exploratory analyses evaluated effects on tinnitus handicap. A comprehensive study protocol was preregistered. <strong>Results: </strong>Nonsignificant trends of poorer SPiN performance, poorer self-reported hearing ability, greater prevalence of tinnitus, greater tinnitus handicap, and greater severity of hyperacusis as a function of higher occupational noise exposure were observed. Greater hyperacusis severity was significantly predicted by higher occupational noise exposure. Aging was significantly associated with higher DIN thresholds and lower SSQ12 scores, but not with tinnitus presence, tinnitus handicap, or hyperacusis severity. <strong>Conclusions: </strong>Workers in Palestine may suffer from auditory effects of occupational noise and aging despite no formal diagnosis. These findings highlight the importance of occupational noise monitoring and hearing-related health and safety practices in developing countries. <strong>Supplemental Material S1. </strong>The findings of the original multiple regression models for all primary and secondary outcome measures treating both occupational noise exposure and age as continuous predictor variables. Shehabi, A. M., Prendergast, G., Guest, H., & Plack, C. J. (2023). Noise exposure in Palestinian workers without a diagnosis of hearing impairment: Relations to speech-perception-in-noise difficulties, tinnitus, and hyperacusis. <em>Journal of Speech, Language, and Hearing Research, 66</em>(3), 1085–1109. https://doi.org/10.1044/2022_JSLHR-22-00461
DOI: 10.6084/m9.figshare.22975787
title: supercon_chem

description: https://github.com/vstanev1/Supercon https://www.nature.com/articles/s41524-018-0085-8