cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3883025
title: Facebook presence of Bucharest Metropolitan Library during pandemic

description: This file contains some data regarding the activity of the Bucharest Metropolitan Library on facebook during the pandemic
DOI: 10.5281/zenodo.3883026
title: Facebook presence of Bucharest Metropolitan Library during pandemic

description: This file contains some data regarding the activity of the Bucharest Metropolitan Library on facebook during the pandemic
DOI: 10.15468/44tydb
title: Frogs of the genus Platypelis from the Sorata massif in northern Madagascar: description of a new species and reports of range extensions

description: This dataset contains the digitized treatments in Plazi based on the original journal article Rakotoarison, Andolalao, Scherz, Mark D., Koehler, Joern, Ratsoavina, Fanomezana M., Hawlitschek, Oliver, Megson, Steven, Vences, Miguel, Glaw, Frank (2020): Frogs of the genus Platypelis from the Sorata massif in northern Madagascar: description of a new species and reports of range extensions. Zoosystematics and Evolution 96 (1): 263-274, DOI: http://dx.doi.org/10.3897/zse.96.47088, URL: http://dx.doi.org/10.3897/zse.96.47088
DOI: 10.21227/3e24-mt42
title: Reconstruct Clear Image for High-Speed Motion Scene with Retina-Inspired Spike Camera - Supplementary Material

description: Supplementary material for the paper: 'Reconstruct Clear Image for High-Speed Motion Scene with Retina-Inspired Spike Camera' PAPER ABSTRACT: Conventional digital cameras typically accumulate all the photoelectric information within an exposure window to form a snapshot image. It requires the scene to be quite still during the exposure interval, otherwise it would result in blurry image for the moving objects. Recently, a retina-inspired spike camera has been proposed and shown great potential for recording high-speed motion scene. Instead of capturing the visual scene by a single snapshot, the spike camera records the dynamic changing process of light intensity continuously. Each pixel on spike camera sensor accumulates the incoming photons independently and persistently, and fires spikes once the dispatch threshold is reached, producing a continuous stream of spikes recorded at very high temporal resolution. To recover the dynamic scene from the captured spike data, we present an image reconstruction approach for spike camera. In order to generate high-quality reconstruction, we investigate the temporal correlation along motion trajectories and exploit it via an adaptive temporal auto-regressive model. In particular, we present a hierarchical motion-aligned temporal filtering scheme, combining short-term filtering with long-term filtering to take advantage of long-term temporal correlation with much lower model complexity. Experimental results demonstrate that the proposed method produces high-quality images for high-speed motion scenes, improving the reconstruction images in terms of both objective and subjective qualities. This dataset provides a set of results and demos for our work on spike camera image reconstruction. The work is submitted to IEEE Transactions on Image Processing.
DOI: 10.21227/3qcx-gp64
title: Reconstruct Clear Image for High-Speed Motion Scene with Retina-Inspired Spike Camera - Supplementary Material

description: Supplementary material for the paper: 'Reconstruct Clear Image for High-Speed Motion Scene with Retina-Inspired Spike Camera' PAPER ABSTRACT: Conventional digital cameras typically accumulate all the photoelectric information within an exposure window to form a snapshot image. It requires the scene to be quite still during the exposure interval, otherwise it would result in blurry image for the moving objects. Recently, a retina-inspired spike camera has been proposed and shown great potential for recording high-speed motion scene. Instead of capturing the visual scene by a single snapshot, the spike camera records the dynamic changing process of light intensity continuously. Each pixel on spike camera sensor accumulates the incoming photons independently and persistently, and fires spikes once the dispatch threshold is reached, producing a continuous stream of spikes recorded at very high temporal resolution. To recover the dynamic scene from the captured spike data, we present an image reconstruction approach for spike camera. In order to generate high-quality reconstruction, we investigate the temporal correlation along motion trajectories and exploit it via an adaptive temporal auto-regressive model. In particular, we present a hierarchical motion-aligned temporal filtering scheme, combining short-term filtering with long-term filtering to take advantage of long-term temporal correlation with much lower model complexity. Experimental results demonstrate that the proposed method produces high-quality images for high-speed motion scenes, improving the reconstruction images in terms of both objective and subjective qualities. This dataset provides a set of results and demos for our work on spike camera image reconstruction. The work is submitted to IEEE Transactions on Image Processing.
DOI: 10.18112/openneuro.ds002761.v1.1.0
title: memoryreplay
DOI: 10.5281/zenodo.3882293
title: Leaf traits

description: Stomatal anatomy, leaf structure and nutrients of tropical rainforest tree species
DOI: 10.5519/qd.axo0g3mm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (933 records)
DOI: 10.21227/ertk-na07
title: Brazilian Flights Dataset

description: An integrated dataset containing data on departure and arrival data for Brazilian domestic flights comprising the period from the years 2000 to 2018.Composed of 13,823,630 records of flight data, each containing 50 attributes. The attributes include data regarding the airline, flight, airports, meteorological conditions, and scheduled and real times of departure and arrival.
DOI: 10.21227/50td-fy30
title: Brazilian Flights Dataset

description: An integrated dataset containing data on departure and arrival data for Brazilian domestic flights comprising the period from the years 2000 to 2018.Composed of 13,823,630 records of flight data, each containing 50 attributes. The attributes include data regarding the airline, flight, airports, meteorological conditions, and scheduled and real times of departure and arrival.
DOI: 10.5281/zenodo.3820752
title: Fernerkundungssurvey

description: Fernerkundungssurvey im Rahmen des Forschungsprojektes Siedlungskammer Kilikien
DOI: 10.25346/s6/ujojhe
title: direct upload test DEM

description: test upload of dem file
DOI: 10.5281/zenodo.3883480
title: Ultraviolet and darkness regulation of medicinal metabolites in Mahonia bealei revealed by proteomics and metabolomics

description: We initially developed targeted methods for PRM to validate the observed differences (using Tier 3 analyses) on a Q-Exactive plus Orbitrap mass spectrometer (Thermo Scientific, San Jose, CA, USA). Fifth-four proteins from differentially abundance proteins were selected for targeted analyses because of their biological activities and concentration levels of interest. Up to three target peptides with at least one unique peptide per protein were included by matching to the spectral library. These are skyline format file for all the PRM analyses and the raw data from MS. And the pdresult files were used for generation of spectral library.
DOI: 10.5281/zenodo.3883481
title: Ultraviolet and darkness regulation of medicinal metabolites in Mahonia bealei revealed by proteomics and metabolomics

description: We initially developed targeted methods for PRM to validate the observed differences (using Tier 3 analyses) on a Q-Exactive plus Orbitrap mass spectrometer (Thermo Scientific, San Jose, CA, USA). Fifth-four proteins from differentially abundance proteins were selected for targeted analyses because of their biological activities and concentration levels of interest. Up to three target peptides with at least one unique peptide per protein were included by matching to the spectral library. These are skyline format file for all the PRM analyses and the raw data from MS. And the pdresult files were used for generation of spectral library.
DOI: 10.21227/x73k-pz69
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”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". 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: 10.21227/1f4w-mn30
title: Brazilian Flights Dataset

description: An integrated dataset containing data on departure and arrival data for Brazilian domestic flights comprising the period from the years 2000 to 2018.Composed of 13,823,630 records of flight data, each containing 50 attributes. The attributes include data regarding the airline, flight, airports, meteorological conditions, and scheduled and real times of departure and arrival.
DOI: 10.5281/zenodo.3883433
title: TanSat Solar-Induced Chlorophyll Fluorescence Product

description: The TanSat Solar Induce chlorophyll fluorescence (SIF) retrievals acquired from the AGCS spectroradiometer onboard TanSat make use of O<sub>2</sub>-A band L1 data with 0.044 nm spectral resolution and wavelengths between 757.4 and 778.1 nm. The TanSat SIF products contain effective SIF values (including SIF_758nm and SIF_771nm) and other geometric information (Latitude, longitude, angles and so forth). There is one file per day if the corresponding ACGS L1B data is available.
description: {"references": ["Du S, Liu L, Liu X, et al. Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite [J]. Science Bulletin: 2018, 63: 1502-1512."]}
DOI: 10.5281/zenodo.3883434
title: TanSat Solar-Induced Chlorophyll Fluorescence Product

description: The TanSat Solar Induce chlorophyll fluorescence (SIF) retrievals acquired from the AGCS spectroradiometer onboard TanSat make use of O<sub>2</sub>-A band L1 data with 0.044 nm spectral resolution and wavelengths between 757.4 and 778.1 nm. The TanSat SIF products contain effective SIF values (including SIF_758nm and SIF_771nm) and other geometric information (Latitude, longitude, angles and so forth). There is one file per day if the corresponding ACGS L1B data is available.
description: {"references": ["Du S, Liu L, Liu X, et al. Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite [J]. Science Bulletin: 2018, 63: 1502-1512."]}
DOI: 10.5281/zenodo.3883491
title: abschneider/Paper_HBV_Greenland: Supplementary data

description: Supplementary data of paper "Analysis of Hepatitis B virus genotype D in Greenland suggests presence of novel subtype".
DOI: 10.5281/zenodo.3883492
title: abschneider/Paper_HBV_Greenland: Supplementary data

description: Supplementary data of paper "Analysis of Hepatitis B virus genotype D in Greenland suggests presence of novel subtype".
DOI: 10.21227/v2nd-yk98
title: Reconstruct Clear Image for High-Speed Motion Scene with Retina-Inspired Spike Camera - Supplementary Material

description: Supplementary material for the paper: 'Reconstruct Clear Image for High-Speed Motion Scene with Retina-Inspired Spike Camera' PAPER ABSTRACT: Conventional digital cameras typically accumulate all the photoelectric information within an exposure window to form a snapshot image. It requires the scene to be quite still during the exposure interval, otherwise it would result in blurry image for the moving objects. Recently, a retina-inspired spike camera has been proposed and shown great potential for recording high-speed motion scene. Instead of capturing the visual scene by a single snapshot, the spike camera records the dynamic changing process of light intensity continuously. Each pixel on spike camera sensor accumulates the incoming photons independently and persistently, and fires spikes once the dispatch threshold is reached, producing a continuous stream of spikes recorded at very high temporal resolution. To recover the dynamic scene from the captured spike data, we present an image reconstruction approach for spike camera. In order to generate high-quality reconstruction, we investigate the temporal correlation along motion trajectories and exploit it via an adaptive temporal auto-regressive model. In particular, we present a hierarchical motion-aligned temporal filtering scheme, combining short-term filtering with long-term filtering to take advantage of long-term temporal correlation with much lower model complexity. Experimental results demonstrate that the proposed method produces high-quality images for high-speed motion scenes, improving the reconstruction images in terms of both objective and subjective qualities. This dataset provides a set of results and demos for our work on spike camera image reconstruction. The work is submitted to IEEE Transactions on Image Processing.
DOI: 10.5281/zenodo.3884374
title: Fast Bit-Vector Satisfiability

description: SMT-LIB2 queries from two symbolic analysis platforms and twenty programs pinpoint qsym
DOI: 10.5281/zenodo.3884375
title: Fast Bit-Vector Satisfiability

description: SMT-LIB2 queries
DOI: 10.5519/qd.5wn43a85
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4559609 records)
DOI: 10.5281/zenodo.3275879
title: Visually Inferring Elasticity from the Motion Trajectory of Bouncing Cubes

description: Dataset relative to the following publication: Paulun, V.C., & Fleming, R. W. (2020). Visually Inferring Elasticity from the Motion Trajectory of Bouncing Cubes. <em>Journal of Vision, </em>20(6):6, 1–14, https://doi.org/10.1167/jov.20.6.6 One folder contains the stimuli, another folder contains data from the main and control experiment.
DOI: 10.5281/zenodo.3275880
title: Visually Inferring Elasticity from the Motion Trajectory of Bouncing Cubes

description: Dataset relative to the following publication: Paulun, V.C., & Fleming, R. W. (2020). Visually Inferring Elasticity from the Motion Trajectory of Bouncing Cubes. <em>Journal of Vision, </em>20(6):6, 1–14, https://doi.org/10.1167/jov.20.6.6 One folder contains the stimuli, another folder contains data from the main and control experiment.
DOI: 10.15472/wyykeq
title: Algas perifíticas asociadas a fuentes hídricas presentes en áreas degradadas por minería en jigualito, Condoto-Chocó, 2015

description: La actividad minera en Condoto - Chocó es realizada a cielo abierto empleando maquinaria para remover el material del suelo, desviar cauces hídricos y utilizarlos como fuente de abastecimiento de agua y sitio de disposición final de los residuos del proceso, los cuales incluyen grandes cantidades de sedimentos, contaminantes químicos, metales, grasas y aceites; que son vertidos directamente, ocasionando la alteración de las características físicoquímicas del agua y afectando a su vez el funcionamiento ecológico de estas como lo han afirmado algunos autores como Martínez y Donato (2003), Díaz y Rivera (2004), Hernández et al. (2005), Zapata y Donato (2005), Ramírez y Plata (2008), Rivera y Donato (2008) yAguirre et al. (2008) entre otroa, que han utilizado grupos biológicos para evaluar el nivel de afectación y la respuesta de los ecosistemas hídricos frente a los disturbios antrópicos. Teniendo en cuenta esta problemática, la presente investigación busca evaluar la estructura comunitaria de las algas perifíticas en relación con las características fisicoquímicas del agua, en ecosistemas hídricos disturbados por minería con diferente exposición histórica al impacto, como herramienta para diagnosticar las afectaciones ecológicas producidas por la actividad. Partiendo de la evaluación de 4 fuentes hídricas con diferente tiempo de cese de la intervención minera; donde se llevó a cabo una caracterización ambiental, mediante determinación de la concentración de variables físicoquímicas del agua y de la composición, densidad y abundancia de las algas perifíticas, para su posterior correlación. Con el estudio, se espera generar información que dé cuenta del grado de afectación de las fuentes hídricas ante el impacto, así como el comportamiento de la fracción perifítica a estas intervenciones, permitiendo evaluar de manera dinámica la perturbación minera a nivel ecológico y la potencial recuperación en el tiempo de los cuerpos de agua intervenidos, información que servirá como base para la toma de decisiones encaminadas a restauración y bioindicación en áreas que presenten este tipo de disturbio considerando la relevancia del tipo de ecosistemas para el desarrollo económico y sociocultural de las comunidades del departamento del Chocó y la predominancia de la minería a cielo abierto e ilegal como una de las principales actividades productivas en el territorio.
DOI: 10.5281/zenodo.3818182
title: Dataset for Ray et al. "Giving and Getting Credit for Authorship of Research Publications"

description: Survey questionnaire and dataset for Ray et al. "Giving and Getting Credit for Authorship of Research Publications"
DOI: 10.5281/zenodo.3818183
title: Dataset for Ray et al. "Giving and Getting Credit for Authorship of Research Publications"

description: Survey questionnaire and dataset for Ray et al. "Giving and Getting Credit for Authorship of Research Publications"
DOI: 10.5281/zenodo.3884392
title: Fast Bit-Vector Satisfiability

description: SMT-LIB2 queries from two symbolic analysis platforms and twenty programs pinpoint qsym
DOI: 10.7910/dvn/9argzn/0x9vv3
title: Model_oc_88_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/d1rmew
title: Model_oc_65_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/g9kxdb
title: Model_ic_47_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/goioq0
title: Model_oc_22_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/kzdewi
title: Model_oc_94_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/rjnzxx
title: Model_ic_43_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/sxzozh
title: Model_oc_23_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/zlf6z7
title: Model_oc_47_M&A (v0=1e12).tab

description: :unav
DOI: 10.5281/zenodo.3884397
title: GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.

description: Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set. Included in zenodo: - dataset of water quality data - additional material and descriptions provided for the competition The competition was organized by: F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln) The dataset was provided by: Thüringer Fernwasserversorgung and IMProvT research project GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' Description: For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation! Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH) Official webpage: http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/