cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c54f1mv1r
title: MassIVE MSV000091937 - multiplexed quantitative lipidomics using LC-MS/MS
title: The MassIVE Knowledge Base (MassIVE-KB) is a set of peptide spectral libraries. They are distilled from 31TB of human proteomics HCD data. Users can peek at the inside of these libraries, browse the source data, and track full provenance of analysis tasks that created these libraries. Browsing Libraries You can browse MassIVE-KB libraries to explore what is in them, and what is the provenance. Here is a view of the status page of a massive-kb library:
DOI: 10.5281/zenodo.7933987
title: Presumed historical native range of the Eastern Dwarf Tree Frog (Litoria fallax)

description: The presumed native range of <em>Litoria fallax,</em> obtained from the Australian Frog Atlas (Cutajar et al. 2022) with invasive range removed based upon historical (&lt;1980) records of the species in the Atlas of Living Australia (ALA; ala.org.au). Used in Rowley & Callaghan (2023). <strong>Reference</strong> Rowley, J.J.L. & Callaghan, C. T. (2023). Tracking the spread of the eastern dwarf tree frog (<em>Litoria fallax</em>) in Australia using citizen science. <em>Australian Journal of Zoology</em> (Under Review). <strong>Australian Frog Atlas:</strong> Cutajar. T. P., Portway, C. D., Gillard, G. L., & Rowley, J. J. L. (2022) Australian Frog Atlas: Fine-scale species distribution maps informed by the FrogID dataset. <em>Technical Reports of the Australian Museum Online</em>. 36: 1-48. https://doi.org/10.3853/j.1835-4211.36.2022.1789
DOI: 10.5281/zenodo.7933986
title: Presumed historical native range of the Eastern Dwarf Tree Frog (Litoria fallax)

description: The presumed native range of <em>Litoria fallax,</em> obtained from the Australian Frog Atlas (Cutajar et al. 2022) with invasive range removed based upon historical (&lt;1980) records of the species in the Atlas of Living Australia (ALA; ala.org.au). Used in Rowley & Callaghan (2023). <strong>Reference</strong> Rowley, J.J.L. & Callaghan, C. T. (2023). Tracking the spread of the eastern dwarf tree frog (<em>Litoria fallax</em>) in Australia using citizen science. <em>Australian Journal of Zoology</em> (Under Review). <strong>Australian Frog Atlas:</strong> Cutajar. T. P., Portway, C. D., Gillard, G. L., & Rowley, J. J. L. (2022) Australian Frog Atlas: Fine-scale species distribution maps informed by the FrogID dataset. <em>Technical Reports of the Australian Museum Online</em>. 36: 1-48. https://doi.org/10.3853/j.1835-4211.36.2022.1789
DOI: 10.6084/m9.figshare.21975228
title: Interestingness is in the eye of the beholder – the impact of formative assessment on students’ situational interest in chemistry classrooms

description: Students’ interest is considered an important learning outcome, but it is also a relevant predictor for student learning, and future vocational choices. According to numerous studies, however, students’ interest in STEM fields usually declines during the course of secondary education. From the perspective of science education, it is therefore necessary to foster or at least maintain students’ interest. Despite the variety of approaches that have already been examined in order to promote student interest, the problem of low-interested students remains. Prior findings indicate that specific person characteristics and the students’ perception of the situation seem to be moderate the effectiveness of many approaches. The current intensive repeated measure intervention study addresses the investigation of a possible interest trigger (formative assessment) and also the process that influences the perception of this trigger. Based on a sample of 9th-grade chemistry students (<i>N</i> = 200), three different interventions of formative assessment were implemented in regular classrooms. Students’ situational interest was assessed repeatedly in short time intervals. Based on multilevel analyses, not all interventions were perceived as equally interesting by the students. While students’ individual interest influences the perception of all interventions positively, the impact of gender, chemistry grade, and enjoyment varies across the interventions.
DOI: 10.6084/m9.figshare.21975228.v1
title: Interestingness is in the eye of the beholder – the impact of formative assessment on students’ situational interest in chemistry classrooms

description: Students’ interest is considered an important learning outcome, but it is also a relevant predictor for student learning, and future vocational choices. According to numerous studies, however, students’ interest in STEM fields usually declines during the course of secondary education. From the perspective of science education, it is therefore necessary to foster or at least maintain students’ interest. Despite the variety of approaches that have already been examined in order to promote student interest, the problem of low-interested students remains. Prior findings indicate that specific person characteristics and the students’ perception of the situation seem to be moderate the effectiveness of many approaches. The current intensive repeated measure intervention study addresses the investigation of a possible interest trigger (formative assessment) and also the process that influences the perception of this trigger. Based on a sample of 9th-grade chemistry students (<i>N</i> = 200), three different interventions of formative assessment were implemented in regular classrooms. Students’ situational interest was assessed repeatedly in short time intervals. Based on multilevel analyses, not all interventions were perceived as equally interesting by the students. While students’ individual interest influences the perception of all interventions positively, the impact of gender, chemistry grade, and enjoyment varies across the interventions.
DOI: 10.6084/m9.figshare.22099721
title: Tweet Emotion Embedding

description: Embedding for the tweet eval dataset generated by OpenAI <br> <em>Dataset containing tweets and meta data information for multiclass prediction for emotion of tweet.</em><br> <br> <em>The data has 4653 tweet records with 5 features and a multiclass target column, referring to the emotion of the tweet.</em><br> <br> <em>This dataset is an extension of Cardiff's tweet_eval dataset,</em><br> <em>For additional details about the dataset, please refer to the original source: https://github.com/cardiffnlp/tweeteval</em>
DOI: 10.6084/m9.figshare.22099721.v2
title: Tweet Emotion Embedding

description: Embedding for the tweet eval dataset generated by OpenAI <br> <em>Dataset containing tweets and meta data information for multiclass prediction for emotion of tweet.</em><br> <br> <em>The data has 4653 tweet records with 5 features and a multiclass target column, referring to the emotion of the tweet.</em><br> <br> <em>This dataset is an extension of Cardiff's tweet_eval dataset,</em><br> <em>For additional details about the dataset, please refer to the original source: https://github.com/cardiffnlp/tweeteval</em>
DOI: 10.5281/zenodo.7934026
title: Datsets and RF model for oxygen isotopic compositions at Chichijima

description: The datasets in the uploaded file include quarterly data of observed ions and oxygen-17 excess as well as predicted oxygen-17 excess between 2001 abd 2011. On the other hand, the RF model is also uploaded.
DOI: 10.5281/zenodo.7934025
title: Datsets and RF model for oxygen isotopic compositions at Chichijima

description: The datasets in the uploaded file include quarterly data of observed ions and oxygen-17 excess as well as predicted oxygen-17 excess between 2001 abd 2011. On the other hand, the RF model is also uploaded.
DOI: 10.34740/kaggle/dsv/5681250
title: AutoXGB
DOI: 10.6084/m9.figshare.22817144
title: NETRA Dataset

description: The NETRA dataset is a comprehensive and detailed resource, specifically curated to facilitate the development and evaluation of automated algorithms for glaucoma diagnosis. This dataset originates from the B.P. Koirala Lions Centre for Ophthalmic Studies (BPKLOCS) Hospital in Nepal, a leading institution in the field of ophthalmology. The dataset consists of 519 high-resolution retinal images (3456 x 2304 pixels). Each of these images has been manually annotated by an experienced ophthalmologist. The annotation process, which was meticulously carried out using a tablet and pen, includes various pathological signs such as cup notching, rim thinning, and a fully cupped optic disc. This attention to detail underscores the quality and reliability of the dataset. The NETRA dataset covers an extensive variety of cases, ranging from mild to advanced glaucoma. This wide range enhances the value of the dataset, making it ideal for creating robust and versatile algorithms capable of identifying a broad spectrum of glaucoma conditions. The primary goal of the collection and distribution of the NETRA dataset is to promote the development of next-generation tools for mass glaucoma screening. It is designed to be used for training and evaluating algorithms that can accurately diagnose glaucoma, thereby significantly contributing to advancements in ophthalmology and healthcare.
DOI: 10.6084/m9.figshare.22817183
title: Just Dance Filtered Data

description: <em>Dataset containing comments and metadata information for multilabel predictions for different properties of comments.</em> <em>This dataset is a modification of Just Dance @ YouTube dataset curated by the COIMBRA university,</em> <em>For additional details about the dataset, please refer to the original source:</em> <em>https://www.kaggle.com/datasets/renatojmsantos/just-dance-on-youtube.</em> <em>Dataset used under the following license: https://creativecommons.org/licenses/by/4.0/</em> <em>Original publication:</em> <em>R. Santos, J. P. Arrais and P. A. Silva, "Analysing Games for Health through Users' Opinion Mining,"</em> <em>2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Aveiro, Portugal, 2021, pp. 319-323,</em> <em>doi: 10.1109/CBMS52027.2021.00035.</em>
DOI: 10.5519/qd.k4y7zuwt
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (436 records)
DOI: 10.5281/zenodo.7934085
title: Data for: Selective elimination of enterovirus genotypes by activated sludge and chlorination

description: Raw data underlying the journal article "Selective elimination of enterovirus genotypes by activated sludge and chlorination" by Larivé et al., <em>Environmental Science: Water Research and Technology</em>, 2023 (doi: 10.1039/d3ew00050h) One CSV file for each of Figures 2-6 of the main manuscript $ One CSV file for each of Figures S5, S6 and S7 of the Supplementary information. The data for Figures S2, S3 and S4 are summarized in a single CSV file.
DOI: 10.5281/zenodo.7934084
title: Data for: Selective elimination of enterovirus genotypes by activated sludge and chlorination

description: Raw data underlying the journal article "Selective elimination of enterovirus genotypes by activated sludge and chlorination" by Larivé et al., <em>Environmental Science: Water Research and Technology</em>, 2023 (doi: 10.1039/d3ew00050h) One CSV file for each of Figures 2-6 of the main manuscript $ One CSV file for each of Figures S5, S6 and S7 of the Supplementary information. The data for Figures S2, S3 and S4 are summarized in a single CSV file.
DOI: 10.5281/zenodo.3633835
title: Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning

description: Dataset accompanying the paper: "Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning"
DOI: 10.5281/zenodo.5153427
title: Modelling impacts of tramlines on soil erosion processes at the catchment scale.

description: The data refers to the following article: Saggau, P., M. Kuhwald, W. B. Hamer, R. Duttmann (2021): Are compacted tramlines underestimated features in soil erosion modelling? A catchment‐scale analysis using a process‐based soil erosion model. In: Land Degradtaion & Development. doi: 10.1002/ldr.4161 The data only contains distributable raw data and R-codes.
description: {"references": ["Saggau, P., M. Kuhwald, W. B. Hamer, R. Duttmann (2021): Are compacted tramlines underestimated features in soil erosion modelling? A catchment\u2010scale analysis using a process\u2010based soil erosion model. In: Land Degradtaion & Development. doi: 10.1002/ldr.4161", "Saggau, Kuhwald, Duttmann. 2019. Integrating Soil Compaction Impacts of Tramlines Into Soil Erosion Modelling: A Field-Scale Approach. Soil Systems3: 51. https://doi.org/10.3390/soilsystems3030051"]}
DOI: 10.5281/zenodo.7535206
title: Anthropogenic Bromoform at the Extratropical Tropopause

description: This dataset contains the new emission inventory used for the simulations presented in the paper and the simulation outputs.
DOI: 10.5287/bodleian:2zvyknyrg
title: Path Dependent Battery Degradation Dataset Part 2
DOI: 10.5287/bodleian:gjpddzvp4
title: Oxford energy trading battery degradation dataset
DOI: 10.5287/bodleian:j1a2ed7ow
title: Path Dependent Battery Degradation Dataset Part 3
DOI: 10.5287/bodleian:v0ervbv6p
title: Path Dependent Battery Degradation Dataset Part 1
DOI: 10.5445/ir/1000094469
title: Frequent Observations from a Battery System with Subunits
DOI: 10.15139/s3/saxvsf
title: National Emission Inventory (NEI) 2016 modeling platform version 2

description: The 2016v2 emissions modeling platform was developed by EPA as an update to the 2016v1 platform because new data, model versions, and methods have become available following the release of 2016v1. The 2016v2 platform incorporates emissions based on: MOVES3, the 2017 NEI nonpoint inventory (both anthropogenic and biogenic), the Western Regional Air Partnership oil and gas inventory, and updated inventories for Canada and Mexico. In addition, 2016v2 makes use of a new inventory method for solvents, includes minor corrections to the wildfire inventory, and corrects for double counting of the airport emissions. The commercial marine vessel and rail inventories are consistent with the 2016v1 inventories. The 2016v2 platform includes emissions for the years 2016, 2023, 2026, and 2032. File Location and Download Instructions: Link to NEI 2016v2 Emission Modeling Platform Data Download instructions
DOI: 10.5281/zenodo.7934100
title: Dataset for Optimal reactive nitrogen control pathways identified for cost-effective PM2.5 mitigation in Europe

description: This folder includes six folders: The "Nr anthropogenic emissions" folder includes the anthropogenic Nr emissions used in the baseline simulations for January, April, July, and October 2015. The "Concentration" folder includes daily surface ammonia (NH<sub>3</sub>), nitric acid (HNO<sub>3</sub>), nitrate (NO<sub>3</sub><sup>-</sup>), ammonium (NH<sub>4</sub><sup>+</sup>), sulfate (SO<sub>4</sub><sup>2-</sup>), and fine particulate matter (PM<sub>2.5</sub>) concentrations for January, April, July, and October 2015 from WRF-Chem simulations (one BASE simulation and twelve sensitivity simulations as stated in Method). The "N-share" folder includes the N-share caused by anthropogenic Nr (NH<sub>3</sub> and NO<sub>x</sub>)/ NH<sub>3</sub>/NO<sub>x</sub> emissions in 2015. The "Health" folder includes the PM2.5-related premature deaths from WRF-Chem combined with GEMM simulations in 2015. The "Instant efficiency" folder includes the instant efficiency of Nr/ NH<sub>3</sub>/NO<sub>x</sub> emission controls from WRF-Chem simulations in 2015. The "G ratio" folder includes the G ratio from WRF-Chem simulations for the year 2015 and the indicator months (January, April, July, and October). For any questions, please contact Zehui Liu<br> Email: liuzh18@pku.edu.cn<br>
DOI: 10.5281/zenodo.7934101
title: Dataset for Optimal reactive nitrogen control pathways identified for cost-effective PM2.5 mitigation in Europe

description: This folder includes six folders: The "Nr anthropogenic emissions" folder includes the anthropogenic Nr emissions used in the baseline simulations for January, April, July, and October 2015. The "Concentration" folder includes daily surface ammonia (NH<sub>3</sub>), nitric acid (HNO<sub>3</sub>), nitrate (NO<sub>3</sub><sup>-</sup>), ammonium (NH<sub>4</sub><sup>+</sup>), sulfate (SO<sub>4</sub><sup>2-</sup>), and fine particulate matter (PM<sub>2.5</sub>) concentrations for January, April, July, and October 2015 from WRF-Chem simulations (one BASE simulation and twelve sensitivity simulations as stated in Method). The "N-share" folder includes the N-share caused by anthropogenic Nr (NH<sub>3</sub> and NO<sub>x</sub>)/ NH<sub>3</sub>/NO<sub>x</sub> emissions in 2015. The "Health" folder includes the PM2.5-related premature deaths from WRF-Chem combined with GEMM simulations in 2015. The "Instant efficiency" folder includes the instant efficiency of Nr/ NH<sub>3</sub>/NO<sub>x</sub> emission controls from WRF-Chem simulations in 2015. The "G ratio" folder includes the G ratio from WRF-Chem simulations for the year 2015 and the indicator months (January, April, July, and October). For any questions, please contact Zehui Liu<br> Email: liuzh18@pku.edu.cn<br>
DOI: 10.6084/m9.figshare.22817186
title: ductal breast cancer_for paper_medicina.xlsx

description: These data for paper submission on medicina journal <br>
DOI: 10.6084/m9.figshare.22817186.v1
title: ductal breast cancer_for paper_medicina.xlsx

description: These data for paper submission on medicina journal <br>
DOI: 10.21227/gkc0-3b82
title: SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation

description: Self-supervised pre-training bears potential to generate expressive representations from large-scale Earth observation (EO) data without human annotation. However, most existing pre-training in the field is based on ImageNet or medium-size, labeled remote sensing (RS) datasets. In this paper, we share an unlabeled dataset SSL4EO-S12: Self-Supervised Learning for Earth Observation - Sentinel-1/2, to assemble a large-scale, global, multimodal, and multi-seasonal corpus of satellite imagery. We demonstrate SSL4EO-S12 to succeed in self-supervised pre-training for a set of representative methods: MoCo-v2, DINO, MAE and data2vec, and multiple downstream applications including scene classification, semantic segmentation and change detection. Our benchmark results prove the effectiveness of SSL4EO-S12 compared to existing datasets. The dataset, related source code, and pre-trained models are available at https://github.com/zhu-xlab/SSL4EO-S12.
DOI: 10.5281/zenodo.7629946
title: Data Anomaly Detection in Cyber-Physical Energy Systems

description: The data is created in an agent-system, used for controlling distributed energy systems. The agents follow the Lightweight Power Exchange Protocol to negotiate whenever an agent detects a planning problem. The protocol is explained in "A lightweight distributed software agent for automatic demand—supply calculation in smart grids" by Veith, Steinbach and Windeln. Different kinds of anomalies were created by manipulating an agent accordingly: anomalies in the values of the exchanged messages and anomalies in the communication behavior. The implementation of the power exchange used for the data generation can be found here: https://gitlab.com/mango-agents/mango-library/-/tags/Integration_of_the_LPEP. Anomalies in the values are manipulated by 500 % of the original value (dataset 500_p.csv). Anomalies in the communication behavior are anomalously started every minute for 8996 seconds in the future (dataset 1m_8996_50p.csv) and every 15 minutes for 2012 seconds in the future (15m_2012_50p.csv). For anomalies in the communication topology, an agent was chosen to which selected agent does not send any messages, although the agent is part of the neighborhood (Topology anomalies/agent_removed.csv). Furthermore, an agent was manipulated to send messages to an agent which is normally not part of its neighborhood (Topologie anomalies/agent_added.csv). Normal data is also given.
DOI: 10.5281/zenodo.7629947
title: Data Anomaly Detection in Cyber-Physical Energy Systems

description: The data is created in an agent-system, used for controlling distributed energy systems. The agents follow the Lightweight Power Exchange Protocol to negotiate whenever an agent detects a planning problem. The protocol is explained in "A lightweight distributed software agent for automatic demand—supply calculation in smart grids" by Veith, Steinbach and Windeln. Different kinds of anomalies were created by manipulating an agent accordingly: anomalies in the values of the exchanged messages and anomalies in the communication behavior. The implementation of the power exchange used for the data generation can be found here: https://gitlab.com/mango-agents/mango-library/-/tags/Integration_of_the_LPEP. Anomalies in the values are manipulated by 500 % of the original value (dataset 500_p.csv). Anomalies in the communication behavior are anomalously started every minute for 8996 seconds in the future (dataset 1m_8996_50p.csv) and every 15 minutes for 2012 seconds in the future (15m_2012_50p.csv). Normal data is also given.
DOI: 10.5281/zenodo.7934270
title: Data Anomaly Detection in Cyber-Physical Energy Systems

description: The data is created in an agent-system, used for controlling distributed energy systems. The agents follow the Lightweight Power Exchange Protocol to negotiate whenever an agent detects a planning problem. The protocol is explained in "A lightweight distributed software agent for automatic demand—supply calculation in smart grids" by Veith, Steinbach and Windeln. Different kinds of anomalies were created by manipulating an agent accordingly: anomalies in the values of the exchanged messages and anomalies in the communication behavior. The implementation of the power exchange used for the data generation can be found here: https://gitlab.com/mango-agents/mango-library/-/tags/Integration_of_the_LPEP. Anomalies in the values are manipulated by 500 % of the original value (dataset 500_p.csv). Anomalies in the communication behavior are anomalously started every minute for 8996 seconds in the future (dataset 1m_8996_50p.csv) and every 15 minutes for 2012 seconds in the future (15m_2012_50p.csv). For anomalies in the communication topology, an agent was chosen to which selected agent does not send any messages, although the agent is part of the neighborhood (Topology anomalies/agent_removed.csv). Furthermore, an agent was manipulated to send messages to an agent which is normally not part of its neighborhood (Topologie anomalies/agent_added.csv). Normal data is also given.
DOI: 10.5281/zenodo.7932604
title: Ps dataset

description: Ps dataset
DOI: 10.5281/zenodo.7932603
title: Ps dataset

description: Ps dataset
DOI: 10.34740/kaggle/dsv/5681922
title: YouTube Daily
DOI: 10.6084/m9.figshare.22817201
title: Date of FT-ICP-MS.

description: The FT-ICR-MS analysis of SPE-DOM activated sludge extracellular polymeric substances.
DOI: 10.6084/m9.figshare.22817201.v1
title: Date of FT-ICP-MS.

description: The FT-ICR-MS analysis of SPE-DOM activated sludge extracellular polymeric substances.
DOI: 10.6084/m9.figshare.22817252
title: Data for: Hydroclimatic extremes contribute to asymmetric trends in ecosystem productivity loss

description: Data for: Hydroclimatic extremes contribute to asymmetric trends in ecosystem productivity loss
DOI: 10.6084/m9.figshare.21128035.v2
title: Data for "Spatiotemporal sizes of specific types of lightning flashes"

description: This dataset is for a manuscript titled "Spatiotemporal sizes of specific types of lightning flashes". The data of our results are included in the 'Data for Figure xx.fig'. These files can be opened by MATLAB R2021a(or later). The data supports the aforementioned manuscript and can be used freely for scientific purposes with appropriate citations.