cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5484322
title: Dataset - University Social Responsibility in the context of Social Appropriation of Knowledge: A Systematic Literature Mapping Approach (2015-2020)

description: From abstract: University Social Responsibility (USR) should be seen beyond a yearly report and be understood as a process of Social Appropriation of Knowledge (SAK) though different stakeholders around higher education. The object of this literature mapping is to analyze the recent developments regarding USR regarding SAK in a higher education context where different stakeholders are involved. Following a PRISMA flow mapping process, six general-designed questions of 286 articles are revised for this paper as to discover how SAK happens within universities. All articles were retrieved from Scopus and Web of Sciences databases. The findings include a USR field divided between models´ approach and experience such as service-learning. Furthermore, sustainability and the environment take a special role within USR in the context of SAK that must be analyzed by itself as there is a wide interest in the topic.
DOI: 10.5281/zenodo.5484321
title: Dataset - University Social Responsibility in the context of Social Appropriation of Knowledge: A Systematic Literature Mapping Approach (2015-2020)

description: From abstract: University Social Responsibility (USR) should be seen beyond a yearly report and be understood as a process of Social Appropriation of Knowledge (SAK) though different stakeholders around higher education. The object of this literature mapping is to analyze the recent developments regarding USR regarding SAK in a higher education context where different stakeholders are involved. Following a PRISMA flow mapping process, six general-designed questions of 286 articles are revised for this paper as to discover how SAK happens within universities. All articles were retrieved from Scopus and Web of Sciences databases. The findings include a USR field divided between models´ approach and experience such as service-learning. Furthermore, sustainability and the environment take a special role within USR in the context of SAK that must be analyzed by itself as there is a wide interest in the topic.
DOI: 10.5281/zenodo.4664177
title: Cultures of healthy and ALS iPSCs-derived motor neurons immunolabeled for Beta Tubulin III and TDP-43 and counterstained with DAPI

description: The archive (<strong>fluoMNs_subset.zip</strong>) contains five folders corresponding to five screens: E, F, G, H and I. Each screen has one or several folders corresponding to one or several plates. The content of the archive is annotated in <strong>annotations_fluoMNs_subset.csv.</strong> Each row corresponds to one field of fiew in the 96-well plates. Each well in the plate has 10 to 12 fields of view, each field of view has 4 to 5 planes and each plane was stained with 3 to 4 channels. Each image is encoded as follows: <em>00<strong>R</strong>00<strong>C</strong>-<strong>F</strong>-00100<strong>P</strong>00<strong>H</strong></em>, where <strong><em>R</em></strong> is the row of the well in the plate, <strong><em>C</em></strong> is the column of the well in the plate, <strong><em>F</em></strong> is the field of view, <strong><em>P</em></strong> is the plane and <strong><em>H</em></strong> is the channel. Here are two Python functions to get the complete path to one image: <pre><em>def </em>get_filename(source_directory, <em>experiment</em>: str, <em>plate</em>: str, <em>image_id</em>: str) -&gt; str: """Return pathname of an image""" <em>return </em>f'{source_directory}/{<em>experiment</em>}/{<em>plate</em>}/{<em>image_id</em>}.tif' <em>def </em>get_image_id(<em>row</em>: int, <em>col</em>: int, <em>fov</em>: int, <em>plane</em>: int, <em>channel</em>: int) -&gt; str: """Return id of an image""" <em>return </em>f'{<em>row</em>:03}{<em>col</em>:03}-{<em>fov</em>}-001{<em>plane</em>:03}{<em>channel</em>:03}'</pre> This is a subset of a larger dataset of cultures of motor neurons under different celullar stress and immunolabeled for ALS-linked RNA-binding proteins. The whole dataset (~1TB) is available on IDR. Some code using this data subset for classification of ALS vs healthy motor neurons can be accessed on Github. This data has been used in a manuscript posted on BioRxiv.
DOI: 10.5281/zenodo.4664176
title: Cultures of healthy and ALS iPSCs-derived motor neurons immunolabeled for Beta Tubulin III and TDP-43 and counterstained with DAPI

description: The archive (<strong>fluoMNs_subset.zip</strong>) contains five folders corresponding to five screens: E, F, G, H and I. Each screen has one or several folders corresponding to one or several plates. The content of the archive is annotated in <strong>annotations_fluoMNs_subset.csv.</strong> Each row corresponds to one field of fiew in the 96-well plates. Each well in the plate has 10 to 12 fields of view, each field of view has 4 to 5 planes and each plane was stained with 3 to 4 channels. Each image is encoded as follows: <em>00<strong>R</strong>00<strong>C</strong>-<strong>F</strong>-00100<strong>P</strong>00<strong>H</strong></em>, where <strong><em>R</em></strong> is the row of the well in the plate, <strong><em>C</em></strong> is the column of the well in the plate, <strong><em>F</em></strong> is the field of view, <strong><em>P</em></strong> is the plane and <strong><em>H</em></strong> is the channel. Here are two Python functions to get the complete path to one image: <pre><em>def </em>get_filename(source_directory, <em>experiment</em>: str, <em>plate</em>: str, <em>image_id</em>: str) -&gt; str: """Return pathname of an image""" <em>return </em>f'{source_directory}/{<em>experiment</em>}/{<em>plate</em>}/{<em>image_id</em>}.tif' <em>def </em>get_image_id(<em>row</em>: int, <em>col</em>: int, <em>fov</em>: int, <em>plane</em>: int, <em>channel</em>: int) -&gt; str: """Return id of an image""" <em>return </em>f'{<em>row</em>:03}{<em>col</em>:03}-{<em>fov</em>}-001{<em>plane</em>:03}{<em>channel</em>:03}'</pre> This is a subset of a larger dataset of cultures of motor neurons under different celullar stress and immunolabeled for ALS-linked RNA-binding proteins. The whole dataset (~1TB) is available on IDR. Some code using this data subset for classification of ALS vs healthy motor neurons can be accessed on Github. This data has been used in a manuscript posted on BioRxiv.
DOI: 10.6073/pasta/ade57bf34ce54016498673e0f1438539
title: Long-term trends in abundance of Lepidoptera larvae at Hubbard Brook Experimental Forest and three additional northern hardwood forest sites, 1986-2018 (Reformatted to the ecocomDP Design Pattern)

description: This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-hbr/82/8. The abstract below was extracted from the Level 0 data package and is included for context: Numbers and lengths of Lepidoptera larvae (caterpillars, all species) were censused on shrub level foliage at biweekly intervals from late May/early June through late July/early August each year. Measurements were conducted on the Main bird plot in the Hubbard Brook Experimental Forest and on three additional plots within the White Mountain National Forest from 1986-1997.. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
DOI: 10.6073/pasta/73c0fb8563bc6446fde7528d27692e3b
title: Long-term trends in abundance of Lepidoptera larvae at Hubbard Brook Experimental Forest and three additional northern hardwood forest sites, 1986-2018 (Reformatted to a Darwin Core Archive)

description: This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/349/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-hbr/82/8. The abstract below was extracted from the Level 0 data package and is included for context: Numbers and lengths of Lepidoptera larvae (caterpillars, all species) were censused on shrub level foliage at biweekly intervals from late May/early June through late July/early August each year. Measurements were conducted on the Main bird plot in the Hubbard Brook Experimental Forest and on three additional plots within the White Mountain National Forest from 1986-1997.. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
DOI: 10.6084/m9.figshare.16586900
title: Development of a laser ablation ICP-MS method for the analysis of fluid inclusions associated with volcanogenic massive sulfide deposits

description: Data from the study
DOI: 10.6084/m9.figshare.16586900.v1
title: Development of a laser ablation ICP-MS method for the analysis of fluid inclusions associated with volcanogenic massive sulfide deposits

description: Data from the study
DOI: 10.6084/m9.figshare.16586909
title: Development of a laser ablation ICP-MS method for the analysis of fluid inclusions associated with volcanogenic massive sulfide deposits

description: Data from the study
DOI: 10.6084/m9.figshare.16586909.v1
title: Development of a laser ablation ICP-MS method for the analysis of fluid inclusions associated with volcanogenic massive sulfide deposits

description: Data from the study
DOI: 10.6084/m9.figshare.16586919
title: Development of a laser ablation ICP-MS method for the analysis of fluid inclusions associated with volcanogenic massive sulfide deposits

description: Data from the study
DOI: 10.6084/m9.figshare.16586919.v1
title: Development of a laser ablation ICP-MS method for the analysis of fluid inclusions associated with volcanogenic massive sulfide deposits

description: Data from the study
DOI: 10.6084/m9.figshare.13317482
title: Comprehensive evolution analysis with genome-scale metabolic models reveals diverse mechanisms in metabolic innovations across 332 yeast species

description: EC number prediction using DeepECDomain annotation for 332 yeast speciesEssential gene prediction using machine learning for yeast species without experimental dataSingle species GEMs in various formatsGeneMining KO query files for tblastn seach for substrate usage and complex IBranch site result for the trait of Crabtree effect and heat-toleranceSite model result for positively selected site analysis across 332 yeast species plus 11 fungal species<br>
DOI: 10.6084/m9.figshare.13317482.v4
title: Comprehensive evolution analysis with genome-scale metabolic models reveals diverse mechanisms in metabolic innovations across 332 yeast species

description: EC number prediction using DeepECDomain annotation for 332 yeast speciesEssential gene prediction using machine learning for yeast species without experimental dataSingle species GEMs in various formatsGeneMining KO query files for tblastn seach for substrate usage and complex IBranch site result for the trait of Crabtree effect and heat-toleranceSite model result for positively selected site analysis across 332 yeast species plus 11 fungal species<br>
DOI: 10.6084/m9.figshare.13317482.v3
title: Comprehensive evolution analysis with genome-scale metabolic models reveals diverse mechanisms in metabolic innovations across 332 yeast species

description: EC number prediction using DeepECDomain annotation for 332 yeast speciesEssential gene prediction using machine learning for yeast species without experimental dataSingle species GEMs in mat format and panmodel in mat formatGeneMining KO query files for tblastn seach for substrate usage and complex IBranch site result for the trait of Crabtree effect and heat-toleranceSite model result for positively selected site analysis across 332 yeast species plus 11 fungal species<br>
DOI: 10.5281/zenodo.5495275
title: Nedystoma dayi RB0472 NOVOGENE SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5495274
title: Nedystoma dayi RB0472 NOVOGENE SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.14473776
title: Comprehensive evolution analysis with genome scale metabolic models reveals diverse mechanisms in metabolic innovations across 332 yeast species

description: EC number prediction using DeepECDomain annotation for 332 yeast speciesEssential gene prediction using machine learning for yeast species without experimental dataSingle species GEMs in various formatsGeneMining KO query files for tblastn seach for substrate usage and complex IBranch site result for the trait of Crabtree effect and heat-toleranceSite model result for positively selected site analysis across 332 yeast species plus 11 fungal species
DOI: 10.6084/m9.figshare.14473776.v1
title: Comprehensive evolution analysis with genome scale metabolic models reveals diverse mechanisms in metabolic innovations across 332 yeast species

description: EC number prediction using DeepECDomain annotation for 332 yeast speciesEssential gene prediction using machine learning for yeast species without experimental dataSingle species GEMs in mat format and panmodel in mat formatGeneMining KO query files for tblastn seach for substrate usage and complex IBranch site result for the trait of Crabtree effect and heat-toleranceSite model result for positively selected site analysis across 332 yeast species plus 11 fungal species
DOI: 10.5257/iea/ri/2021
title: Renewables Information (2021 Edition)

description: Renewables Information provides a comprehensive review of historical and current market trends in OECD countries, including provisional data. It provides an overview of the development of renewables and waste in the world from 1990 onwards. A greater focus is given to the OECD countries with a review of electricity generation and capacity from renewable and waste energy sources, including detailed tables. However, an overview of developments in the world and OECD renewable and waste market is also presented. The publication encompasses energy indicators, generating capacity, electricity and heat production from renewable and waste sources, as well as production and consumption of renewables and waste.
DOI: 10.5066/p9xcp1ae
title: Input data, model output, and R scripts for a machine learning streamflow model on the Wyoming Range, Wyoming, 2012-17

description: A machine learning streamflow (MLFLOW) model was developed in R (model is in the Rscripts folder) for modeling monthly streamflow from 2012 to 2017 in three watersheds on the Wyoming Range in the upper Green River basin. Geospatial information for 125 site features (vector data are in the Sites.shp file) and discrete streamflow observation data and environmental predictor data were used in fitting the MLFLOW model and predicting with the fitted model. Tabular calibration and validation data are in the Model_Fitting_Site_Data.csv file, totaling 971 discrete observations and predictions of monthly streamflow. Geospatial information for 17,518 stream grid cells (raster data are in the Streams.tif file) and environmental predictor data were used for continuous streamflow predictions with the MLFLOW model. Tabular prediction data for all the study area (17,518 stream grid cells) and study period (72 months; 2012-17) are in the Model_Prediction_Stream_Data.csv file, totaling 1,261,296 predictions of spatially and temporally continuous monthly streamflow. Additional information about the datasets is in the metadata included in the four zipped dataset files and about the MLFLOW model is in the readme included in the zipped model archive folder.
DOI: 10.6084/m9.figshare.14681454.v1
title: Supplementary material for Palaeoenvironmental controls on deep-water syn-Rift sediment flux (Cullen et al, in review))

description: Supplementary material for EarthArxiv pre-print and Frontiers in Earth Science article;<br><br>"Deep-water syn-rift stratigraphy as archives of Early-Mid Pleistocenepalaeoenvironmental signals and controls on sediment delivery"<br>by <br>Timothy M. Cullen<sup>1,2</sup>, Richard E. Ll. Collier<sup>1</sup>, David M. Hodgson<sup>1</sup>, Robert L. Gawthorpe<sup>2</sup>, Katerina Kouli<sup>3</sup>,Marco Maffione<sup>4</sup>, Haralambos Kranis<sup> 3</sup>, Gauti T. Eliassen<sup>2</sup>.<br>1. School of Earth and Environment, University of Leeds, Leeds, United Kingdom2. Department of Earth Sciences, University of Bergen, Bergen, Norway3. Department of Geology and Geoenvironment, National and Kapodistrian University of Athens,Athens, Greece4. Department of Earth and Environmental Sciences, University of Birmingham, Birmingham,United Kingdom
DOI: 10.6084/m9.figshare.14681454
title: Supplementary material for Palaeoenvironmental controls on deep-water syn-Rift sediment flux (Cullen et al, in review))

description: Supplementary material for EarthArxiv pre-print and Frontiers in Earth Science article;<br><br>"Deep-water syn-rift stratigraphy as archives of Early-Mid Pleistocenepalaeoenvironmental signals and controls on sediment delivery"<br>by <br>Timothy M. Cullen<sup>1,2</sup>, Richard E. Ll. Collier<sup>1</sup>, David M. Hodgson<sup>1</sup>, Robert L. Gawthorpe<sup>2</sup>, Katerina Kouli<sup>3</sup>,Marco Maffione<sup>4</sup>, Haralambos Kranis<sup> 3</sup>, Gauti T. Eliassen<sup>2</sup>.<br>1. School of Earth and Environment, University of Leeds, Leeds, United Kingdom2. Department of Earth Sciences, University of Bergen, Bergen, Norway3. Department of Geology and Geoenvironment, National and Kapodistrian University of Athens,Athens, Greece4. Department of Earth and Environmental Sciences, University of Birmingham, Birmingham,United Kingdom
DOI: 10.5066/p9zcnr6x
title: Humpback chub (Gila cypha) capture history data (2009-2020), Grand Canyon, Arizona

description: These tabular data were compiled to estimate abundance from paired physical capture and antenna detection data in closed, open, and robust design mark-recapture models. These data represent humpback chub capture histories in the Little Colorado River (LCR) and in the juvenile chub monitoring (JCM) reach of the Colorado River in the Grand Canyon, Arizona. The dataset includes multistate capture histories that describe both physical captures and antenna detections. The 'LCR chub' dataset was collected from long-term monitoring efforts by the US Fish and Wildlife Service in the lower 13.56 river km of the LCR (2009-2020), as well as a summer sampling trip conducted by US Geological Survey (2013-2020). Also, the LCR chub dataset includes data from the juvenile chub monitoring (JCM) reach, which was located 63.4-65.0 river miles downstream of Lees Ferry (2009-2016) and expanded from river mile 62.8-66.0 from 2017-2020. Data from the JCM reach was collected as part of the Near Shore Ecology (NSE) project by the University of Florida (2009-2011) and by US Geological Survey (2012-2020). These data can be used to help inform more complex models, such as models that use robust design to estimate temporary emigration from the study area.
DOI: 10.5281/zenodo.5495236
title: Greek Elementary Sign Language Dataset

description: Entails the course material of the first years of elementary school in Greece. It includes 28.357 signed phrases that are present in the 33 issues of 13 distinct textbooks of the A, B and C years of Primary school. The Elementary Dataset consists of the following courses: Greek Language (1st, 2nd and 3rd year) Mathematics (1st, 2nd and 3rd year) Anthology of Greek Literacy (1st, 2nd, 3rd and 4th year) Environmental Studies (1st, 2nd and 3rd year) History (3rd year) Religious Study (3rd year)
DOI: 10.5281/zenodo.5495517
title: Summary of bee and plant abundance 2020-part of 2021 NutriB2

description: Summary requested for literature search for a phd proposal. Please do not distribute.
DOI: 10.5281/zenodo.5495516
title: Summary of bee and plant abundance 2020-part of 2021 NutriB2

description: Summary requested for literature search for a phd proposal. Please do not distribute.
DOI: 10.25919/0d98-9v49
title: Parkes observations for project P1032 semester 2021APRS_15

description: This project aims to continue to perform rapid measurements of relativistic parameters of southern binary pulsar systems using data from the Parkes Ultra Wideband Low (UWL) receiver and the MeerKAT telescope thereby enabling measurement of their component masses and in some cases, test theories of gravity. We will use the UWL data to obtain significant orbital coverage and timing baseline on these pulsars and obtain important constraints on orbital and temporal DM variations that will help in identifying the nature of the companion star. We will then use MeerKAT data at crucial orbital phases to improve our chances of measuring the relativistic parameters. Increase in the number of measured NS masses has profound implications for NS internal structure and for stellar and binary evolution physics. From this semester, we are converting this proposal into a ``Large project" proposal.
DOI: 10.6084/m9.figshare.16586897
title: Sea level components and contribution

description: Sea level components and contribution
DOI: 10.6084/m9.figshare.16586897.v1
title: Sea level components and contribution

description: Sea level components and contribution
DOI: 10.6084/m9.figshare.16586957
title: EEG driver drowsiness dataset (unbalanced)

description: The dataset contains EEG signals from 11 subjects with labels of alert and drowsy. It can be opened with Matlab. We extracted the data for our own research purpose from another public dataset:<br>Cao, Z., et al., Multi-channel EEG recordings during a sustained-attention driving task. Scientific data, 2019. 6(1): p. 1-8.<br>If you find the dataset useful, please give credits to their works.<br>The full version of the pre-processed dataset from the original author is accessible from:<br>https://figshare.com/articles/dataset/Multi-channel_EEG_recordings_during_a_sustained-attention_driving_task_preprocessed_dataset_/7666055<br><br>This dataset is the unbalanced version of our previous version of dataset:<br>https://figshare.com/articles/dataset/EEG_driver_drowsiness_dataset/14273687<br><br>The difference from the previous version is that the samples have not been balanced for each subject. The details on how the data were extracted are described in our paper (except performing step 3 in page 3):<br>"Jian Cui, Zirui Lan, Yisi Liu, Ruilin Li, Fan Li, Olga Sourina, Wolfgang Müller-Wittig, A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG, Methods, 2021, ISSN 1046-2023, https://doi.org/10.1016/j.ymeth.2021.04.017."<br>The data file contains 3 variables and they are EEGsample, substate and subindex.<br><br>"EEGsample" contains 2952 EEG samples of size 20x384 from 11 subjects. Each sample is a 3s EEG data with 128Hz from 30 EEG channels."subindex" is an array of 2952x1. It contains the subject indexes from 1-11 corresponding to each EEG sample."substate" is an array of 2952x1. It contains the labels of the samples. 0 corresponds to the alert state and 1 correspond to the drowsy state.<br>
DOI: 10.5281/zenodo.5495226
title: Spatio-Temporal Clustering Benchmark for Collective Animal Behavior

description: Synthetic datasets for the paper "Spatio-Temporal Clustering Benchmark for Collective Animal Behavior". The full_data.zip contains all 3600 datasets, and the Excel files contain information about the generated large datasets from which we sampled the respective 3600 datasets.
DOI: 10.5281/zenodo.5495225
title: Spatio-Temporal Clustering Benchmark for Collective Animal Behavior

description: Synthetic datasets for the paper "Spatio-Temporal Clustering Benchmark for Collective Animal Behavior". The full_data.zip contains all 3600 datasets, and the Excel files contain information about the generated large datasets from which we sampled the respective 3600 datasets.