cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5777336
title: Campus Pond (Buffalo Creek Watershed) Water Data from 2021-11-03 to 2021-11-23

description: <pre># General Metadata for Campus Pond Sampling Station ## Files Specific metadata for each deployment and sensor can be found as text files with the file format of: LP_CampusPond_DO_YYYY-MM-DD_metadata.txt LP_CampusPond_Depth_YYYY-MM-DD_metadata.txt LP_CampusPond_CT_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab [https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts](https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts). ## File Created * 2021-11-10 by KF - copied and modified from HS_wetland_general_metadata.md ## File Modified ## Description These data are from the sampling station in Campus Pond on the Longwood University Campus. The sensors are along the S shoreline of the pond (37.296813, -78.397702). All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The specific sensors at the site are: * Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland. The sensors are sampled every 15 minutes ## Measurement Parameters, units, and Variable Names * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT, .press, or .BP - the temperature (dC) from the DO, conductivity, water pressure, or barometric pressure sensor. * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2) </pre>
DOI: 10.5281/zenodo.5777335
title: Campus Pond (Buffalo Creek Watershed) Water Data from 2021-11-03 to 2021-11-23

description: <pre># General Metadata for Campus Pond Sampling Station ## Files Specific metadata for each deployment and sensor can be found as text files with the file format of: LP_CampusPond_DO_YYYY-MM-DD_metadata.txt LP_CampusPond_Depth_YYYY-MM-DD_metadata.txt LP_CampusPond_CT_YYYY-MM-DD_metadata.txt Where YYYY-MM-DD is the date that the sampling period ended. NOTE: The metadata in the above file is collected from the data logger and does not have all of fields present in the final data set, because some were created during data cleaning. Details on how the data were cleaned and variables created can be found at in the cleaning scripts on Gitlab [https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts](https://gitlab.com/leo147/leo/-/tree/master/lab_notebook/data_processing/cleaning_scripts). ## File Created * 2021-11-10 by KF - copied and modified from HS_wetland_general_metadata.md ## File Modified ## Description These data are from the sampling station in Campus Pond on the Longwood University Campus. The sensors are along the S shoreline of the pond (37.296813, -78.397702). All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/ ## Station Specifics The specific sensors at the site are: * Water Temperature (dC) and Dissolved Oxygen (mg/l) are collected with a Onset HOBO U26-001 Dissolved Oxygen Logger * Water Temperature (dC) and Water Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger * Water Temperature (dC) and Conductivity are collected with an Onset HOBO U24-001 Conductivity Logger * Air Temperature (dC) and Barometric Pressure (mmHg) are collected with an Onset HOBO U20-001-01 Water Level Logger mounted in the air next to the wetland. The sensors are sampled every 15 minutes ## Measurement Parameters, units, and Variable Names * date.time - the date and time that the record was collected, reported in POSIX standard time (YYYY-MM-DD HH:MM:SS) * observation.DO, .CT, .press, or .BP - the incremental number of each observation from the DO, conductivity, water pressure, or barometric pressure sensor. * timestamp.DO, .CT, .press, or .BP - the data and time that the record was collected, as reported by the data logger (MM/DD/YY HH:MM:SS A/PM) from the DO, conductivity, water pressure, or barometric pressure sensor. * DO - the concentration of dissolved oxygen in the water (mg/L) * Temp.DO, .CT, .press, or .BP - the temperature (dC) from the DO, conductivity, water pressure, or barometric pressure sensor. * Pressure.press or .BP - the pressure recorded by the pressure transducer (kPa) on the water pressure or barometric pressure sensor. * Z - the depth of the water (cm). * Low_Range_CT - the conductivity read from 0 - 2500 uS/cm (uS/cm) * Full_Range_CT - the conductivity read from 0 - 15000 uS/cm (mmHg) * press.g.cm2 - the pressure from the water pressure sensor (g/cm^2) * BP.g.cm2 - the barometric pressure from the barometric pressure sensor (g/cm^2) </pre>
DOI: 10.6084/m9.figshare.16779040.v1
title: Investigation of [MSalen/salophen] (M = Cr<sup>3+</sup>, Fe<sup>3+</sup> or Co<sup>3+</sup>) capped dinuclear complexes of two novel tetraoxocalix[2](m-hydroxymethyl)arene[2]triazine compounds

description: In this work, (3,5-bis((4,6-dichloro-1,3,5-triazine-2-yl)oxy)phenyl)methanol was obtained by reacting cyanuric chloride with 3,5-dihydroxybenzyl alcohol at 0 °C. Then, s-triazine bridged calixarene codded as L1 was obtained by reacting the compounds with 3,5-dihydroxybenzyl alcohol. Targed ligands, which we code as L2 and L3, were obtained from the reaction of the L1 coded compound with 4-aminobenzoic acid or 4-hydroxybenzoic acid, respectively. [MSalen/Saloph] capped s-triazine bridged calixarene complexes were obtained from the reaction of the L2 or L3 codded ligand and the [(MSalen/Saloph)<sub>2</sub>O] (M = Cr<sup>3+</sup>, Fe<sup>3+</sup>, Co<sup>3+</sup>) starting complexes in 1,4-dioxane media. The ligands and complexes were characterized by using <sup>1</sup>H-NMR, FT-IR spectroscopy, UV-Vis spectroscopies, magnetic susceptibility, TGA-DTA and elemental analysis techniques. Metal contents of the complexes were determined by using ICP-AES spectroscopy. Calixarenes can be used in many areas such as chiral phase transfer catalyst, recognition of enantiomers, stationary phase in chromatography, recovery of heavy and precious metals molecules or ions from environmental wastes.
DOI: 10.25345/c5ms2s
title: MassIVE MSV000088555 - Bioplex 3 AP-MS Dataset for Library Construction
title: The BioPlex (biophysical interactions of ORFeome-based complexes) network is the result of creating thousands of cell lines with each expressing a tagged version of a protein from the ORFeome collection. Immunopurification of the tagged protein and detection of associated proteins by mass spectrometry are the building blocks of the network. Source of the data was acquired from the Bioplex project online at http://bioplex.hms.harvard.edu/. Pubmed ID: 26186194. These data were searched using standard pipelines in order to create peptide spectral libraries.
DOI: 10.26300/74bs-dw33
title: Data documentation for "“Now at least we have something to call it”: Pathways to Receiving an Amyloid PET Scan"
DOI: 10.25345/c5gs15
title: MassIVE MSV000088556 - Tgfbr2 in dental pulp cells through semaphorins to guides neurite outgrowth in developing teeth
title: Transforming growth factor beta &#40;TGF&#946;&#41; plays an important role in tooth morphogenesis and mineralization. During postnatal development&#44; the dental pulp &#40;DP&#41; mesenchyme secretes neurotrophic factors that guide trigeminal nerve fibers into and throughout the DP. This process is tightly linked with dentin formation and mineralization. Our laboratory established a mouse model in which Tgfbr2 was conditionally deleted in DP mesenchyme using an Osterix promoter-driven Cre recombinase &#40;Tgfbr2&#94;cko&#41;. These mice survived postnatally with significant defects in bones and teeth&#44; including reduced mineralization and short roots. Hematoxylin and eosin staining revealed reduced axon-like structures in the mutant mice. Reporter imaging demonstrated that Osterix-Cre activity within the tooth was active in DP and derivatives&#44; but not in neurons. Immunofluorescent staining for a neuronal marker&#44; &#946;3 tubulin&#44; was performed on serial cryosections from control and mutant molars at two different postnatal stages. Confocal imaging and pixel quantification for &#946;3 tubulin demonstrated reduced innervation in Tgfbr2&#94;cko first molars at postnatal day 7 and 24 (P7&#44; 24) compared to controls&#44; indicating the signals necessary to promote neurite outgrowth were disrupted with Tgfbr2 deletion. We performed an mRNA-Sequence &#40;RNA-Seq&#41; and gene onotology analysis using RNA from the DP of P7 control and mutant mice to investigate the pathways involved in Tgfbr2-mediated tooth development. These analyses identified downregulation of several mineralization and neuronal genes in the Tgfbr2&#94;cko DP compared to controls. Selected gene expression patterns were confirmed by quantitative real-time PCR and immunofluorescence imaging. Lastly&#44; trigeminal neurons were co-cultured atop Transwell filters overlying primary Tgfbr2&#94;f/f DP cells. Tgfbr2 in the DP was deleted with Adenovirus expressing Cre recombinase. Confocal imaging of axons through the filter pores showed increased axonal sprouting from neurons cultured in the presence of Tgfbr2-positive DP cells when compared to neurons cultured alone. Axon sprouting was reduced when Tgfbr2 was knocked down in the DP cells. Immunofluorescence of DSPP in co-culture DP cells confirmed reduced mineralization potential with Tgfbr2 deletion. Both our proteomics and RNA-Seq indicate interrupted axonal guidance cues&#44; particularly semaphorin signaling&#44; were disrupted with Tgfbr2 deletion. These results indicate that Tgfbr2 in the DP mesenchyme regulates their differentiation and ability to guide neurite outgrowth in the developmental process of tooth mineralization and innervation.
DOI: 10.18112/openneuro.ds003851.v1.0.1
title: Category-biased neural representations form spontaneously during learning that emphasizes memory for specific instances
DOI: 10.5281/zenodo.5777439
title: Hypoxylon rubiginosum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5777438
title: Hypoxylon rubiginosum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5777452
title: Ithomiini grid-cell records for distribution modeling

description: This repository contains georeferenced <strong>occurrence data</strong> for ithomiine butterflies (tribe Ithomiini) needed to reproduce the analyses presented in the research paper <strong>"Doré et al., 2021 - Anthropogenic pressures coincide with Neotropical biodiversity hotspots in a flagship butterfly group"</strong>: https://doi.org/10.1111/ddi.13455. <strong>Distribution maps</strong> generated from distribution models runned on these occurrences data are available in an other associated archive: https://doi.org/10.5281/zenodo.4673446. <strong>Mimicry classification</strong> used for theses analyses is available in this associated archive: https://doi.org/10.5281/zenodo.5497876. <strong>Scripts</strong> to reproduce these maps are available on Github at https://github.com/MaelDore/ithomiini_diversity. This repository contains an Excel file with the occurrence records and associated metadata in separated tabs.
DOI: 10.5281/zenodo.5777472
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Sarah Williams, https://orcid.org/0000-0002-2644-9952. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5281/zenodo.5777471
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Sarah Williams, https://orcid.org/0000-0002-2644-9952. Claims were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
DOI: 10.5517/ccdc.csd.cc28kk4k
title: CCDC 2102238: Experimental Crystal Structure Determination

description: Related Article: Gong-Qing Liu, Chen-Fan Zhou, Yun-Qian Zhang, Wei Yi, Peng-Fei Wang, Ji Liu, Yong Ling|2021|Green Chemistry|23|9968|doi:10.1039/D1GC03195C
DOI: 10.5517/ccdc.csd.cc28kk5l
title: CCDC 2102239: Experimental Crystal Structure Determination

description: Related Article: Gong-Qing Liu, Chen-Fan Zhou, Yun-Qian Zhang, Wei Yi, Peng-Fei Wang, Ji Liu, Yong Ling|2021|Green Chemistry|23|9968|doi:10.1039/D1GC03195C
DOI: 10.5517/ccdc.csd.cc28kk6m
title: CCDC 2102240: Experimental Crystal Structure Determination

description: Related Article: Gong-Qing Liu, Chen-Fan Zhou, Yun-Qian Zhang, Wei Yi, Peng-Fei Wang, Ji Liu, Yong Ling|2021|Green Chemistry|23|9968|doi:10.1039/D1GC03195C
DOI: 10.5517/ccdc.csd.cc28kx0s
title: CCDC 2102575: Experimental Crystal Structure Determination

description: Related Article: Marvin Nathusius, Daniel Sleeman, Junyou Pan, Frank Rominger, Jan Freudenberg, Uwe H. F. Bunz, Klaus Müllen|2021|Chem.-Eur.J.|27|16606|doi:10.1002/chem.202103285
DOI: 10.5517/ccdc.csd.cc28kx1t
title: CCDC 2102576: Experimental Crystal Structure Determination

description: Related Article: Marvin Nathusius, Daniel Sleeman, Junyou Pan, Frank Rominger, Jan Freudenberg, Uwe H. F. Bunz, Klaus Müllen|2021|Chem.-Eur.J.|27|16606|doi:10.1002/chem.202103285
DOI: 10.5517/ccdc.csd.cc28ndkx
title: CCDC 2104979: Experimental Crystal Structure Determination

description: Related Article: Madushani Dharmarwardana, Bhargav S. Arimilli, Gregory T. McCandless, Jeremiah J. Gassensmith|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28ndly
title: CCDC 2104980: Experimental Crystal Structure Determination

description: Related Article: Madushani Dharmarwardana, Bhargav S. Arimilli, Gregory T. McCandless, Jeremiah J. Gassensmith|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28ndmz
title: CCDC 2104981: Experimental Crystal Structure Determination

description: Related Article: Madushani Dharmarwardana, Bhargav S. Arimilli, Gregory T. McCandless, Jeremiah J. Gassensmith|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28ndp1
title: CCDC 2104983: Experimental Crystal Structure Determination

description: Related Article: Madushani Dharmarwardana, Bhargav S. Arimilli, Gregory T. McCandless, Jeremiah J. Gassensmith|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28ndy9
title: CCDC 2104991: Experimental Crystal Structure Determination

description: Related Article: Madushani Dharmarwardana, Bhargav S. Arimilli, Gregory T. McCandless, Jeremiah J. Gassensmith|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28ndzb
title: CCDC 2104992: Experimental Crystal Structure Determination

description: Related Article: Madushani Dharmarwardana, Bhargav S. Arimilli, Gregory T. McCandless, Jeremiah J. Gassensmith|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28nf0d
title: CCDC 2104993: Experimental Crystal Structure Determination

description: Related Article: Madushani Dharmarwardana, Bhargav S. Arimilli, Gregory T. McCandless, Jeremiah J. Gassensmith|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28ngl0
title: CCDC 2105042: Experimental Crystal Structure Determination

description: Related Article: Danielle L. Gray, Toby J. Woods|2021|CSD Communication|||
description: Related Article: Travis Menard, Aragorn Laverny, Scott E. Denmark|2021|J.Org.Chem.|86|14290|doi:10.1021/acs.joc.1c02290
DOI: 10.5517/ccdc.csd.cc28ngm1
title: CCDC 2105043: Experimental Crystal Structure Determination

description: Related Article: Danielle L. Gray, Toby J. Woods|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28nnk5
title: CCDC 2105227: Experimental Crystal Structure Determination

description: Related Article: Kai Gong, Yilin Zhou, Xuefeng Jiang|2021|Green Chemistry|23|9865|doi:10.1039/D1GC03242A
DOI: 10.5281/zenodo.5777468
title: Supporting data for the AI education publication statistics in "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates"

description: Supporting data for the AI education publication statistics presented in the paper "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates" to be published at the Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-22). The data was used to plot the figure showing the cumulative number of publications from 1976 to 2020 relating to AI education.
description: The data was collected by searching for publications relating to AI education (i.e. teaching and learning in relation to the the subject of AI) using Scopus, Web of Science, Educational Resources Information Center (ERIC), ACM Digital Library, and IEEE Xplore. The search was conducted on 6th September 2021.
DOI: 10.5281/zenodo.5777467
title: Supporting data for the AI education publication statistics in "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates"

description: Supporting data for the AI education publication statistics presented in the paper "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates" to be published at the Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-22). The data was used to plot the figure showing the cumulative number of publications from 1976 to 2020 relating to AI education.
description: The data was collected by searching for publications relating to AI education (i.e. teaching and learning in relation to the the subject of AI) using Scopus, Web of Science, Educational Resources Information Center (ERIC), ACM Digital Library, and IEEE Xplore. The search was conducted on 6th September 2021.
DOI: 10.6084/m9.figshare.14101359
title: How educational are “educational” apps for young children? App store content analysis using the Four Pillars of Learning framework

description: Experts have expressed concerns about the lack of evidence demonstrating that children’s “educational” applications (apps) have educational value. This study aimed to operationalize Hirsh-Pasek, Zosh, and colleagues' Four Pillars of Learning into a reliable coding scheme (Pillar 1: Active Learning, Pillar 2: Engagement in the Learning Process, Pillar 3: Meaningful Learning, Pillar 4: Social Interaction), describe the educational quality of commercially available apps, and examine differences in educational quality between free and paid apps. We analyzed 100 children’s educational apps with the highest downloads from Google Play and Apple app stores, as well as 24 apps most frequently played by preschool-age children in a longitudinal cohort study. We developed a coding scheme in which each app earned a value of 0–3 for each Pillar, defining lower-quality apps as those scoring ≤4, summed across the Four Pillars. Overall scores were low across all Pillars. Free apps had significantly lower Pillar 2 (Engagement in Learning Process) scores (t-test, <i>p</i> p
DOI: 10.6084/m9.figshare.14101359.v2
title: How educational are “educational” apps for young children? App store content analysis using the Four Pillars of Learning framework

description: Experts have expressed concerns about the lack of evidence demonstrating that children’s “educational” applications (apps) have educational value. This study aimed to operationalize Hirsh-Pasek, Zosh, and colleagues' Four Pillars of Learning into a reliable coding scheme (Pillar 1: Active Learning, Pillar 2: Engagement in the Learning Process, Pillar 3: Meaningful Learning, Pillar 4: Social Interaction), describe the educational quality of commercially available apps, and examine differences in educational quality between free and paid apps. We analyzed 100 children’s educational apps with the highest downloads from Google Play and Apple app stores, as well as 24 apps most frequently played by preschool-age children in a longitudinal cohort study. We developed a coding scheme in which each app earned a value of 0–3 for each Pillar, defining lower-quality apps as those scoring ≤4, summed across the Four Pillars. Overall scores were low across all Pillars. Free apps had significantly lower Pillar 2 (Engagement in Learning Process) scores (t-test, <i>p</i> p
DOI: 10.6084/m9.figshare.14101359.v1
title: How educational are “educational” apps for young children? App store content analysis using the Four Pillars of Learning framework

description: Experts have expressed concerns about the lack of evidence demonstrating that children’s “educational” applications (apps) have educational value. This study aimed to operationalize Hirsh-Pasek, Zosh, and colleagues' Four Pillars of Learning into a reliable coding scheme (Pillar 1: Active Learning, Pillar 2: Engagement in the Learning Process, Pillar 3: Meaningful Learning, Pillar 4: Social Interaction), describe the educational quality of commercially available apps, and examine differences in educational quality between free and paid apps. We analyzed 100 children’s educational apps with the highest downloads from Google Play and Apple app stores, as well as 24 apps most frequently played by preschool-age children in a longitudinal cohort study. We developed a coding scheme in which each app earned a value of 0–3 for each Pillar, defining lower-quality apps as those scoring ≤4, summed across the Four Pillars. Overall scores were low across all Pillars. Free apps had significantly lower Pillar 2 (Engagement in Learning Process) scores (t-test, <i>p</i> p
DOI: 10.18112/openneuro.ds002814.v1.3.0
title: A Multimodal Neuroimaging Dataset to Study Spatiotemporal Dynamics of Visual Processing in Humans
DOI: 10.6084/m9.figshare.14766102.v25
title: antspyt1w helper data

description: downloadable cloud data for testing and processing
DOI: 10.5281/zenodo.5777470
title: Prueba depósito términos más usados durante la Pandemia Covid-19 en España y Francia

description: Búsqueda y comparación entre Francia y España de los términos más usados sobre el Covid-19.
DOI: 10.5281/zenodo.5777469
title: Prueba depósito términos más usados durante la Pandemia Covid-19 en España y Francia

description: Búsqueda y comparación entre Francia y España de los términos más usados sobre el Covid-19.
DOI: 10.6084/m9.figshare.14766102.v26
title: antspyt1w helper data

description: downloadable cloud data for testing and processing