cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/jbjey1
title: Study On Press Mud

description: Nowadays , we use fossil fuels fuels for electricity, heat for our homes and fuel for vehicles. We are using them faster than they are created. Finally, they are going to end. Today, there is an urgent need to find another source to fossil fuels since fossil fuels are vanishing . we need to discover another source of energy. This article leads us to notice that Press Mud, a waste from sugar cane, can be utilized for the production of bio gas. The sugar industry produces a huge amount of waste that is organic and nutrients rich in nature. Sugar cane Press Mud, have a high potential for the production of renewable energy through the anaerobic digestion process. In order to obtain bio gas from the mud, press mud is anaerobically fermented in an anaerobic digester for 3 to 4 hours at 35 to 40 ˚C .As India Stands second largest producer of sugarcane, this can play important role in India
DOI: 10.21223/7uojxp/6zblah
title: 01_Spectra_data_H_4_15_2020.xlsx
DOI: 10.21223/7uojxp/ej659s
title: 02_Spectra_Average_data_H_4_15_2020.xlsx
DOI: 10.21223/7uojxp/4iisgs
title: 07_Data_dictionary_H_4_15_2020.tab
DOI: 10.21223/7uojxp/ifewtq
title: 03_Laboratory_data_H_4_15_2020.tab
DOI: 10.21223/7uojxp/zgnsnr
title: 04_Laboratory_average_data_H_4_15_2020.tab
DOI: 10.21223/7uojxp/bu3m5i
title: 05_Calibration_data_H_4_15_2020.tab
DOI: 10.21223/7uojxp/smh4ap
title: 06_Description_data_H_4_15_2020.tab
DOI: 10.21223/7uojxp
title: Dataset for: RTBfoods H.4.15- NIRS Calibration for Texture Cooking Time on raw fresh sweetpotato

description: NIRS spectra were taken of 61 SP genotypes. 3 fresh roots per genotype were cut hoizontally and scanned by NIRS XDS in the middle of the root. Scans were used for calibration development by single root but also averaged by genotype. Spectra databases and lab values are presented in 2 separated sheets. Reference analysis was done in Uganda directly after scanning by cutting 2 cubes of each root for cooking time and texture parameters. For texture parameters, the method needed to be changed after sample 17, which means that samples with IDs 02 to 17 were not considered for calibration development. Procedures for sampling and sample preparation NIRS measurement on raw FRESH GROUND sweetpotato. SP root samples harvested in 2 locations in Uganda at 11/2019 (61 genotypes). NIR instrument used was Foss XDS, serial number 30101714, and 183 spectra in single roots were taken. In a second approach 3 spectra per genotype were averaged to 61 spectra (1 representing 1 genotype).
DOI: 10.15468/6ce4xv
title: A Taxonomic study of two species of Thiotricha Meyrick (Lepidoptera: Gelechiidae), from southwestern Japan, with notes on the biology of their immature stages

description: This dataset contains the digitized treatments in Plazi based on the original journal article Kyaw, Khine Mon Mon, Ueda, Tatsuya, Yagi, Sadahisa, Okamoto, Tomoko, Wang, Min, Hirowatari, Toshiya (2021): A Taxonomic study of two species of Thiotricha Meyrick (Lepidoptera: Gelechiidae), from southwestern Japan, with notes on the biology of their immature stages. Zootaxa 4980 (2): 331-354, DOI: https://doi.org/10.11646/zootaxa.4980.2.5
DOI: 10.6084/m9.figshare.14807346
title: 图2a-0.1.xlsx

description: raw date<br>
DOI: 10.6084/m9.figshare.14807346.v1
title: 图2a-0.1.xlsx

description: raw date<br>
DOI: 10.25612/837.13ga4vpepl3q
title: Texas Behavioral Risk Factor Surveillance System (BRFSS)

description: Data about Texas residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services.
DOI: 10.25810/y7rc-0571
title: Data sets associated with "Non-Gaussian Detection using Machine Learning with Data Assimilation Applications"

description: Manuscript abstract:
In most data assimilation and numerical weather prediction systems, the Gaussian assumption is prevalent for the behaviour of the random variables/errors that are involved. At the Cooperative Institute for Research in the Atmosphere (CIRA) theory has been developed for different forms of variational data assimilation schemes that enables the Gaussian assumption to be relaxed. For certain variable types, a log normally distributed random variable can be combined in a mixed Gaussian-lognormal distribution to better capture the interactions of the errors of different distributions. However, assuming that a distribution can change in time,  then developing techniques to know when to switch between different versions of the data assimilation schemes becomes very important.  Given this ability to change the formulation of the data assimilation system enable us to select the more optimal scheme for the different distributed situations.

In this paper, we present results with a machine learning technique (the support vector machine) to switch between data assimilation methods based on the detection of a change in the Lorenz 1963 model's z component's probability distribution. Given the machine learning technique's detection/prediction of a change in the distribution, then either a Gaussian or a mixed Gaussian-lognormal 3DVar based cost function is used to minimise the errors in this period of time. It is shown that switching from a Gaussian 3DVar to a lognormal 3DVar at lognormally-distributed parts of the attractor improves the data assimilation analysis error compared to using one distribution type for the entire attractor.
DOI: 10.6084/m9.figshare.13090367
title: Integration of biplanar X-ray, 3-D animation, and particle simulation reveals details of human ‘track ontogeny’ - Data

description: Data includes:Data for:<br>Hatala, K., Gatesy, S. M., and Falkingham, P.L. 2021. Integration of biplanar X-ray, 3-D animation, and particle simulation reveals details of human ‘track ontogeny’. Interface Focus<br><br>Scans:3D models of tracks3D model of foot<br>Biplanar X-ray:Videos + calibration images of the trial used.<br>Simulation:Input files and final frame of the simulation.
DOI: 10.15468/h2f7ud
title: Review of the status of the genus Phelene stat. resurr. (Orthoptera: Tetrigidae Lophotettiginae) and description of a new species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Cadena-Castañeda, Oscar J., Díaz, Carlos Julio Arango, Silva, Daniela Santos Martins, Buitrago, Oscar, García, Alexander García, Tumbrinck, Josef (2021): Review of the status of the genus Phelene stat. resurr. (Orthoptera: Tetrigidae Lophotettiginae) and description of a new species. Zootaxa 4980 (2): 355-365, DOI: https://doi.org/10.11646/zootaxa.4980.2.6
DOI: 10.6084/m9.figshare.14807315.v1
title: Phylogeny and evolutionary history of polychelidan lobsters

description: Polychelidan lobsters are unique decapod crustaceans characterized by having four or five pairs of claws. We still do not have a clear overview of the 69 described species of fossil polychelidans and, until now, little was known of their phylogenetic relationships. Our phylogenetic analysis includes 35 fossil polychelidans and offers the first high-resolution study of their relationships, uniting fossil and extant polychelidans. It shows that polychelidans are divided into five main lineages – two monospecific (<i>Palaeopentacheles roettenbacheri</i> and <i>Rogeryon oppeli</i>) and three clades (Eryonidae, Coleiidae and Polychelidae). The recent inclusion of Tetrachelidae within Coleiidae is confirmed. The analysis allowed the identification of diagnostic characters for Eryonidae and demonstrates that <i>Eryon siburicus</i> from the Toarcian is the oldest occurrence of this family. The diverse <i>Coleia</i> is polyphyletic. We were able to identify several trends in polychelidan evolution: (1) diversification of the main lineages probably predates the Late Triassic; (2) Eryonidae probably evolved fossorial adaptations independently; (3) the general trend to live in deeper water disappears when accounting for the bathyal distribution of each monophyletic group across time; (4) several lineages conquered shallow-water palaeoenvironments; and (5) fossil polychelids inhabited deep water as early as the Early–Middle Jurassic. Extant polychelidans are not refugees that adapted to deep water, but survivors of a group that was once more diverse both morphologically and ecologically. http://zoobank.org/urn:lsid:zoobank.org:pub:A0E01F06-083B-426F-A5C3-6D6A8218A1A8
DOI: 10.6084/m9.figshare.14807315
title: Phylogeny and evolutionary history of polychelidan lobsters

description: Polychelidan lobsters are unique decapod crustaceans characterized by having four or five pairs of claws. We still do not have a clear overview of the 69 described species of fossil polychelidans and, until now, little was known of their phylogenetic relationships. Our phylogenetic analysis includes 35 fossil polychelidans and offers the first high-resolution study of their relationships, uniting fossil and extant polychelidans. It shows that polychelidans are divided into five main lineages – two monospecific (<i>Palaeopentacheles roettenbacheri</i> and <i>Rogeryon oppeli</i>) and three clades (Eryonidae, Coleiidae and Polychelidae). The recent inclusion of Tetrachelidae within Coleiidae is confirmed. The analysis allowed the identification of diagnostic characters for Eryonidae and demonstrates that <i>Eryon siburicus</i> from the Toarcian is the oldest occurrence of this family. The diverse <i>Coleia</i> is polyphyletic. We were able to identify several trends in polychelidan evolution: (1) diversification of the main lineages probably predates the Late Triassic; (2) Eryonidae probably evolved fossorial adaptations independently; (3) the general trend to live in deeper water disappears when accounting for the bathyal distribution of each monophyletic group across time; (4) several lineages conquered shallow-water palaeoenvironments; and (5) fossil polychelids inhabited deep water as early as the Early–Middle Jurassic. Extant polychelidans are not refugees that adapted to deep water, but survivors of a group that was once more diverse both morphologically and ecologically. http://zoobank.org/urn:lsid:zoobank.org:pub:A0E01F06-083B-426F-A5C3-6D6A8218A1A8
DOI: 10.6084/m9.figshare.4487321
title: Seattle_Street_Clock_Database.xlsx

description: This spreadsheet contains three tables: Clocks, Owners, and Evidence- Clocks tab's name column and Owners tab's corporation column keys in Evidence.- Evidence is a list of every unique fact I have via newspaper, photograph, document, or otherwise about when a clock was in a particular location, and who owned it at that time. v1 has 1033 pieces of evidence.Please contact me if you have additions or find errors in my data. I will be refreshing this periodically. I constantly find new resources. Along with my friend Paul Middents we have proven the connection between many owners, and I know that there will be more discoveries to show which clocks were later owned by another jeweler.<br><br>A few notes on fields:<br>- In Evidence, "Var_Year" indicates a +/- value on the Source_Year. Empty means exact. 0 means it was the first year that the clock was at that location. - If the Source_Type is Photograph, Source_ID has a value, and there is no URL, then I have the photo on my harddrive but can't post it.- In Clocks, if the Unique field is Yes, then I'm sure that this clock cannot be matched with any other clocks in the list. There are 114 clocks in the list which is excessive. It should be down closer to 60.- There are a growing number of sign and facade clocks in the database. On the linked page I will maintain a list of open questions, and areas I know I need to work on.
DOI: 10.6084/m9.figshare.4487321.v3
title: Seattle_Street_Clock_Database.xlsx

description: This spreadsheet contains three tables: Clocks, Owners, and Evidence- Clocks tab's name column and Owners tab's corporation column keys in Evidence.- Evidence is a list of every unique fact I have via newspaper, photograph, document, or otherwise about when a clock was in a particular location, and who owned it at that time. v1 has 1033 pieces of evidence.Please contact me if you have additions or find errors in my data. I will be refreshing this periodically. I constantly find new resources. Along with my friend Paul Middents we have proven the connection between many owners, and I know that there will be more discoveries to show which clocks were later owned by another jeweler.<br><br>A few notes on fields:<br>- In Evidence, "Var_Year" indicates a +/- value on the Source_Year. Empty means exact. 0 means it was the first year that the clock was at that location. - If the Source_Type is Photograph, Source_ID has a value, and there is no URL, then I have the photo on my harddrive but can't post it.- In Clocks, if the Unique field is Yes, then I'm sure that this clock cannot be matched with any other clocks in the list. There are 114 clocks in the list which is excessive. It should be down closer to 60.- There are a growing number of sign and facade clocks in the database. On the linked page I will maintain a list of open questions, and areas I know I need to work on.
DOI: 10.6084/m9.figshare.4487321.v2
title: Seattle_Street_Clock_Database.xlsx

description: This spreadsheet contains three tables: Clocks, Owners, and Evidence- Clocks tab's name column and Owners tab's corporation column keys in Evidence.- Evidence is a list of every unique fact I have via newspaper, photograph, document, or otherwise about when a clock was in a particular location, and who owned it at that time. v1 has 1033 pieces of evidence.Please contact me if you have additions or find errors in my data. I will be refreshing this periodically. I constantly find new resources. Along with my friend Paul Middents we have proven the connection between many owners, and I know that there will be more discoveries to show which clocks were later owned by another jeweler.<br><br>A few notes on fields:<br>- In Evidence, "Var_Year" indicates a +/- value on the Source_Year. Empty means exact. 0 means it was the first year that the clock was at that location. - If the Source_Type is Photograph, Source_ID has a value, and there is no URL, then I have the photo on my harddrive but can't post it.- In Clocks, if the Unique field is Yes, then I'm sure that this clock cannot be matched with any other clocks in the list. There are 114 clocks in the list which is excessive. It should be down closer to 60.- There are a growing number of sign and facade clocks in the database. On the linked page I will maintain a list of open questions, and areas I know I need to work on.
DOI: 10.25326/229
title: Photographs taken from the HALO aircraft during the EUREC4A field campaign

description: The dataset contains photographs taken from the HALO aircraft during the EUREC4A field campaign. Meta data are added to each picture file describing the object of the photo and giving additional information.
DOI: 10.5281/zenodo.4728229
title: Data and scripts related to: Rapid coordination of effective learning by the human hippocampus

description: This data set contains intracranial EEG data (ASCII format), eye-tracking data from an EyeLink 1000 remote system (edf format), behavioral data, and MATLAB code to reproduce the analyses reported in the manuscript, “Rapid coordination of effective learning by the human hippocampus” published in <em>Science Advances.</em> The file <strong>KragelEtal21_SciAdv.zip</strong> contains the raw data divided into folders according to content type, for each of the six participants in the study, and the MATLAB code necessary to reproduce all analyses. MATLAB live scripts provide examples of how to reproduce the main analyses reported in the manuscript. External datasets: In addition to the dataset provided here, three open-access datasets are analyzed in the manuscript. - The FIGRIM Dataset contains eye-tracking data during a continuous recognition task. - Two additional eye-tracking datasets during free viewing of repeated scenes are provided in “An extensive dataset of eye movements during viewing of complex images,” namely the Memory I and Memory II datasets. To reproduce region of interest analyses outside of the hippocampus, both the seven-network cortical parcellation developed by Yeo, Krienen et al., and the Harvard-Oxford cortical atlas are required. Stimuli: The scenes used in this study are part of Microsoft COCO. Scenes were selected from the 2017 Train images. Image identifiers are maintained. Salience model: To reproduce analyses that consider the visual salience of each scene, DeepGaze II model predictions for each stimulus are required. Tensorflow models and a Jupyter notebook demonstrating their use are available for download. Software dependencies: The code in this project was developed using MATLAB r2017b. The following external packages are required for code execution. Some external packages are included in the repository. - fieldtrip (https://github.com/fieldtrip/fieldtrip)<br> - spm12 (https://github.com/spm/spm12)<br> - BOSC (https://doi.org/10.1016/j.neuroimage.2010.08.064)<br> - Edf2Mat (https://github.com/uzh/edf-converter)<br> - boundedline (https://github.com/kakearney/boundedline-pkg)<br> - export_fig (https://github.com/altmany/export_fig) License: The included code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version. See the file COPYING for more details. The release of this software includes functions from other toolboxes that are covered under their respective licenses.
DOI: 10.5281/zenodo.4728228
title: Data and scripts related to: Rapid coordination of effective learning by the human hippocampus

description: This data set contains intracranial EEG data (ASCII format), eye-tracking data from an EyeLink 1000 remote system (edf format), behavioral data, and MATLAB code to reproduce the analyses reported in the manuscript, “Rapid coordination of effective learning by the human hippocampus” published in <em>Science Advances.</em> The file <strong>KragelEtal21_SciAdv.zip</strong> contains the raw data divided into folders according to content type, for each of the six participants in the study, and the MATLAB code necessary to reproduce all analyses. MATLAB live scripts provide examples of how to reproduce the main analyses reported in the manuscript. External datasets: In addition to the dataset provided here, three open-access datasets are analyzed in the manuscript. - The FIGRIM Dataset contains eye-tracking data during a continuous recognition task. - Two additional eye-tracking datasets during free viewing of repeated scenes are provided in “An extensive dataset of eye movements during viewing of complex images,” namely the Memory I and Memory II datasets. To reproduce region of interest analyses outside of the hippocampus, both the seven-network cortical parcellation developed by Yeo, Krienen et al., and the Harvard-Oxford cortical atlas are required. Stimuli: The scenes used in this study are part of Microsoft COCO. Scenes were selected from the 2017 Train images. Image identifiers are maintained. Salience model: To reproduce analyses that consider the visual salience of each scene, DeepGaze II model predictions for each stimulus are required. Tensorflow models and a Jupyter notebook demonstrating their use are available for download. Software dependencies: The code in this project was developed using MATLAB r2017b. The following external packages are required for code execution. Some external packages are included in the repository. - fieldtrip (https://github.com/fieldtrip/fieldtrip)<br> - spm12 (https://github.com/spm/spm12)<br> - BOSC (https://doi.org/10.1016/j.neuroimage.2010.08.064)<br> - Edf2Mat (https://github.com/uzh/edf-converter)<br> - boundedline (https://github.com/kakearney/boundedline-pkg)<br> - export_fig (https://github.com/altmany/export_fig) License: The included code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version. See the file COPYING for more details. The release of this software includes functions from other toolboxes that are covered under their respective licenses.
DOI: 10.6084/m9.figshare.14807565
title: Urban OMI NOx 2005 - 2019

description: OMI NOx data
DOI: 10.6084/m9.figshare.14807565.v1
title: Urban OMI NOx 2005 - 2019

description: OMI NOx data
DOI: 10.15468/8rpwv5
title: Electrocambalidae fam. nov., a new family of Cambalidea from Cretaceous Burmese amber (Diplopoda, Spirostreptida)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Moritz, Leif, Wesener, Thomas (2021): Electrocambalidae fam. nov., a new family of Cambalidea from Cretaceous Burmese amber (Diplopoda, Spirostreptida). European Journal of Taxonomy 755 (1): 22-46, DOI: https://doi.org/10.5852/ejt.2021.755.1397, URL: http://dx.doi.org/10.5852/ejt.2021.755.1397
DOI: 10.25345/c5mc18
title: MassIVE MSV000087656 - Hela and LacZ Run Data, plus blanks
title: Run containing two samples (Hela and Hela+LacZ), plus blanks
DOI: 10.5281/zenodo.4463683
title: Collision Avoidance Challenge dataset

description: The Collision Avoidance Challenge dataset is the official dataset used during the <strong>ESA's Kelvins</strong> <strong>competition for "Collision Avoidance Challenge"</strong>. The dataset is a collection of Conjunction Data Messages (CDMs) received by ESA from 2015 to 2019. The CDMs have been anonymised for distribution. The initial raw data, as well as the labels that were kept private during the competition, are also released.<br> ESA thanks the US Space Surveillance Network for the provision of surveillance data supporting safe operations of ESA’s spacecraft. In addition, we are grateful to the agreement which allows to publicly release the current dataset. The dataset is represented as a table, where each row corresponds to a single CDM, and each CDM contains 103 recorded characteristics/features. There are thus 103 columns, which are described in the competition pages. The dataset is made of several unique collision/close approach events, which are identified in the <code>event_id</code> column. In turn, each collision event is made of several CDMs recorded over time. Therefore, a single collision event can be thought of as a times series of CDMs. From these CDMs, for every collision event, we are interested in predicting the final risk which is computed in the last CDM of the time series (i.e. the risk value in the last row of each collision event). For a detailed description on the challenge and this dataset, visit kelvins.esa.int/collision-avoidance-challenge/data/. The paper describing the competition setup and result can be found at arxiv.org/pdf/2008.03069.pdf.