cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5885190
title: Qualitative coding of brief videos that teach about the h-index

description: <strong>Dataset of qualitative coding of 31 Youtube videos on the h-index. </strong>The study aimed to characterize educational videos about the h-index to understand available resources and provide recommendations for future educational initiatives. <em>Data.csv</em>: contains the metadata and qualitative coding for 31 videos. <em>ReadMe.csv</em>: contains the codebook including a description of variables. <strong>Abstract. </strong>The authors analyzed videos on the h-index posted to YouTube. Videos were identified by searching YouTube and were screened by two authors. To code the videos the authors created a coding sheet, which assessed content and presentation style with a focus on the videos’ educational quality based on Cognitive Load Theory. Two authors coded each video independently with discrepancies resolved by group consensus. Thirty-one videos met inclusion criteria. Twenty-one videos (68%) were screencasts and seven used a “talking head” approach. Twenty-six videos defined the h-index (83%) and provided examples of how to calculate and find it. The importance of the h-index in high-stakes decisions was raised in 14 (45%) videos. Sixteen videos (52%) described caveats about using the h-index, with potential disadvantages to early researchers the most prevalent (n=7; 23%). All videos incorporated various educational approaches with potential impact on viewer cognitive load. Most videos (n=21; 68%) displayed amateurish production quality. The videos featured content with potential to enhance viewers’ metrics literacies such that many defined the h-index and described its calculation, providing viewers with skills to recognize and interpret the metric. However, less than half described the h-index as an author quality indicator, which has been contested, and caveats about h-index use were inconsistently presented, suggesting room for improvement. While most videos integrated practices to facilitate balancing viewers’ cognitive load, few (32%) were of professional production quality. Some videos missed opportunities to adopt particular practices that could benefit learning.
description: This project is funded by Social Sciences and Humanities Research Council of Canada (SSHRC) Insight Grant #435-2021-0108 "Metrics Literacies: Improving the understanding and appropriate use of scholarly metrics in academia"
DOI: 10.5281/zenodo.6533215
title: Test Test

description: Test Test
DOI: 10.34740/kaggle/dsv/3606676
title: ECB speeches etc. 1997 to 2022-05-09
DOI: 10.13020/54ej-e238
title: R code and data for "Refining the moose serum progesterone threshold to diagnose pregnancy"
DOI: 10.6084/m9.figshare.19735525
title: Cognitive energy budgets

description: <strong>Cognitive energy budgets</strong> <br> Explore cognitive overload in computational and screen time work balanced with the remainder of 'life'.
DOI: 10.6084/m9.figshare.14455911
title: Source Data for Figure 4 and Supplementary Figure 4

description: <br> Source Data for Figure 4 and Supplementary Figure 4 Raw intensity of translation, Ago2 or β-gal, and mRNA for single mRNA tracks for Fig. 4A,B Double-renormalized intensity of translation, Ago2 or β-gal, and mRNA for single mRNA tracks for Fig. 4C-G and Sup. Fig. 4A Translation intensity per mRNA for Harringtonine runoff assay for Fig. H and Sup. Fig. 4C Whole-cell intensity during Harringtonine runoff assay for photobleach correction measurement for Sup. Fig. 4B Translation runoff rate for each cell for Harringtonine runoff assay for Fig. 4I
DOI: 10.6084/m9.figshare.14455911.v2
title: Source Data for Figure 4 and Supplementary Figure 4

description: <br> Source Data for Figure 4 and Supplementary Figure 4 Raw intensity of translation, Ago2 or β-gal, and mRNA for single mRNA tracks for Fig. 4A,B Double-renormalized intensity of translation, Ago2 or β-gal, and mRNA for single mRNA tracks for Fig. 4C-G and Sup. Fig. 4A Translation intensity per mRNA for Harringtonine runoff assay for Fig. H and Sup. Fig. 4C Whole-cell intensity during Harringtonine runoff assay for photobleach correction measurement for Sup. Fig. 4B Translation runoff rate for each cell for Harringtonine runoff assay for Fig. 4I
DOI: 10.6084/m9.figshare.14455911.v1
title: Source Data for Figure 4 and Supplementary Figure 4

description: Source Data for Figure 4 and Supplementary Figure 4 Raw intensity of translation, Ago2 or β-gal, and mRNA for single mRNA tracks for Fig. 4A,BDouble-renormalized intensity of translation, Ago2 or β-gal, and mRNA for single mRNA tracks for Fig. 4C,D,E,F and Sup. Fig. 4ATranslation intensity per mRNA for Harringtonine runoff assay for Fig. 4G and Sup. Fig. 4B,DWhole-cell intensity during Harringtonine runoff assay for photobleach correction measurement for Sup. Fig. 4CSlow rate, fast rate, and fast fraction data for each cell for Harringtonine runoff assay for Sup. Fig. 4E,F,GTranslation half-times for each cell for Harringtonine runoff assay for Fig. 4H<br>
DOI: 10.6084/m9.figshare.14455905
title: Source Data for Figure 3 and Supplementary Figure 3

description: Source Data for Figure 3 and Supplementary Figure 3 Intensity of mRNA puncta over 12 hours for Fig. 3B Fraction of mRNA in P bodies per cell for Fig. 3D Percent mRNA foci in P-bodies for tethered Ago25XE in Sup. Fig. 3C Fraction of intact mRNA per cell and in/out of P-bodies for Sup. Fig. 3F Intensity ratio of 3’ / 5’ per mRNA in vs out of P-bodies in cells expressing tetherable Ago2 for Sup. Fig. 3G
DOI: 10.6084/m9.figshare.14455905.v2
title: Source Data for Figure 3 and Supplementary Figure 3

description: Source Data for Figure 3 and Supplementary Figure 3 Intensity of mRNA puncta over 12 hours for Fig. 3B Fraction of mRNA in P bodies per cell for Fig. 3D Percent mRNA foci in P-bodies for tethered Ago25XE in Sup. Fig. 3C Fraction of intact mRNA per cell and in/out of P-bodies for Sup. Fig. 3F Intensity ratio of 3’ / 5’ per mRNA in vs out of P-bodies in cells expressing tetherable Ago2 for Sup. Fig. 3G
DOI: 10.6084/m9.figshare.14455905.v1
title: Source Data for Figure 3 and Supplementary Figure 3

description: Source Data for Figure 3 and Supplementary Figure 3 Intensity of mRNA puncta over 12 hours for Fig. 3BFraction of mRNA in P bodies per cell for Fig. 3DFraction of intact mRNA per cell and in/out of P-bodies for Sup. Fig. 3AIntensity ratio of 3’ / 5’ per mRNA in vs out of P-bodies in cells expressing tetherable Ago2 for Sup. Fig. 3B
DOI: 10.6084/m9.figshare.14455923
title: Source Data for Figure 5 and Supplementary Figure 5

description: Source Data for Figure 5 and Supplementary Figure 5 Intensity of FLAG and HA per mRNA for miRNA site targeting translation assay for Fig. 5C and Sup. Fig. 5C sfGFP intensity per nucleus for miRNA site targeting translation assay for Sup. Fig. 5B Co-movement of POLR3G 3'UTR mRNA and P-bodies data for Sup. Fig. 5E
DOI: 10.6084/m9.figshare.14455923.v2
title: Source Data for Figure 5 and Supplementary Figure 5

description: Source Data for Figure 5 and Supplementary Figure 5 Intensity of FLAG and HA per mRNA for miRNA site targeting translation assay for Fig. 5C and Sup. Fig. 5C sfGFP intensity per nucleus for miRNA site targeting translation assay for Sup. Fig. 5B Co-movement of POLR3G 3'UTR mRNA and P-bodies data for Sup. Fig. 5E
DOI: 10.6084/m9.figshare.14455923.v1
title: Source Data for Figure 5 and Supplementary Figure 5

description: Source Data for Figure 5 and Supplementary Figure 5 Intensity of FLAG and HA per mRNA for miRNA site targeting translation assay for Fig. 5C and Sup. Fig. 5CsfGFP intensity per nucleus for miRNA site targeting translation assay for Sup. Fig. 5B
DOI: 10.5281/zenodo.6533266
title: Icequake-magnitude scaling relationship along a rift within the Ross Ice Shelf, Antarctica

description: This data set contains the icequake catalog used for Huang et al. (2022).
DOI: 10.5281/zenodo.6533265
title: Icequake-magnitude scaling relationship along a rift within the Ross Ice Shelf, Antarctica

description: This data set contains the icequake catalog used for Huang et al. (2022).
DOI: 10.5517/ccdc.csd.cc27k8x0
title: CCDC 2072192: Experimental Crystal Structure Determination

description: Related Article: Zohreh Razmara, Vaclav Eigner, Michal Dusek|2022|J.Solid State Chem.|311|123123|doi:10.1016/j.jssc.2022.123123
DOI: 10.5517/ccdc.csd.cc296zl3
title: CCDC 2121875: Experimental Crystal Structure Determination

description: Related Article: Sayak Das Gupta, Annaliese E. Thuijs, Ethan G. Fisher, Khalil A. Abboud, George Christou|2022|Inorg.Chem.|61|6392|doi:10.1021/acs.inorgchem.1c03719
DOI: 10.5517/ccdc.csd.cc296zm4
title: CCDC 2121876: Experimental Crystal Structure Determination

description: Related Article: Sayak Das Gupta, Annaliese E. Thuijs, Ethan G. Fisher, Khalil A. Abboud, George Christou|2022|Inorg.Chem.|61|6392|doi:10.1021/acs.inorgchem.1c03719
DOI: 10.5517/ccdc.csd.cc296zn5
title: CCDC 2121877: Experimental Crystal Structure Determination

description: Related Article: Sayak Das Gupta, Annaliese E. Thuijs, Ethan G. Fisher, Khalil A. Abboud, George Christou|2022|Inorg.Chem.|61|6392|doi:10.1021/acs.inorgchem.1c03719
DOI: 10.5281/zenodo.6533327
title: Collection of pXRF Data Corresponding to Study Spectral Samples | Southwest Australia Acid Saline Sediments | Radwin 2022

description: This file contains pXRF data from a portable Bruker XRF instrument using mudrock calibration settings, where each sample was scanned for 90 seconds to alternate through energy spectrums. This data has at tab displaying associated file numbers that correspond to sample ID's. These sample ID's all correspond to spectra of the same sample location used for the pXRF measurements. This data was collected for the completion of a masters thesis at the University of Utah Department of Geology & Geophysics.
DOI: 10.5281/zenodo.6533326
title: Collection of pXRF Data Corresponding to Study Spectral Samples | Southwest Australia Acid Saline Sediments | Radwin 2022

description: This file contains pXRF data from a portable Bruker XRF instrument using mudrock calibration settings, where each sample was scanned for 90 seconds to alternate through energy spectrums. This data has at tab displaying associated file numbers that correspond to sample ID's. These sample ID's all correspond to spectra of the same sample location used for the pXRF measurements. This data was collected for the completion of a masters thesis at the University of Utah Department of Geology & Geophysics.
DOI: 10.3886/e113988v1-117750
title: Replication data for: From "Made in China" to "Innovated in China": Necessity, Prospect, and Challenges

description: After more than three decades of high growth based on its low-wage advantage and relatively favorable demographics--in combination with market-oriented reforms and openness to the world economy--China is at a crossroads with a much higher wage and a shrinking workforce. Future growth will depend, by necessity, more on the generation of increased productivity, and domestic innovation will play an important part in this. In this paper, we assess the likelihood that China can make the necessary transition. Using data on expenditure on research and development, and patent applications, receipts, and citations, we show that the Chinese economy has become increasingly innovative. We will argue that rising wages and expanding markets are among the important drivers of China's growth in innovation. On the other hand, we find evidence of resource misallocation in the innovation area: while state-owned firms receive more subsidies, private firms exhibit more innovation results. Innovation can presumably progress even faster if resource misallocation can be tackled.
DOI: 10.6084/m9.figshare.19735618.v1
title: knitrepository.dat

description: A sample of knitters and a comparison sample of undergraduates.
DOI: 10.6084/m9.figshare.19735618
title: knitrepository.dat

description: A sample of knitters and a comparison sample of undergraduates.
DOI: 10.6084/m9.figshare.19735564.v1
title: Lundell et al 2022 Ecology soil biota and drought response data

description: Data from Lundell et al. 2022 Ecology. This includes plant biomass data from five species used in a growth chember experiment. These species were seeded together into pots inoculated with live and sterile soils collected from 12 grassland sites that had been subjected to drought treatments. The pots were also exposed to another drought treatment over the course of the study.
DOI: 10.6084/m9.figshare.19735549
title: Assessing behavioral sex differences to chemical cues of predation risk while provisioning nestlings in a hole-nesting bird

description: Birds can assess nest predation risk and adjust their parental activity accordingly. Risk taking behavior should be related to investment in reproduction as well as to confidence in parenthood that often differ between sexes. In those cases, sexual differences in risk taking behavior may be expected. For example, in blue tits, females invest more time and energy than males in nest-building, egg laying and incubation. Furthermore, confidence in parenthood is supposed to be higher for females, as extrapair paternity is common in this species. Therefore, the reproductive value of nestlings may be higher for females than for males and the former may assume greater risks to ensure nestling growth and maximize their reproductive success. We examined potential sexual differences in the risk assumed by parents in relation to perceived risk of predation inside the nest cavity, where predation risk perception may be higher. We increased perceived predation risk by adding predator chemical cues inside blue tit (<em>Cyanistes caeruleus</em>) nest-boxes, and we tested whether female and male parents differed in the risk assumed when taking care of nestlings. Females and males did not differ in the risk assumed in response to perceived predation risk. However, females reduced time devoted to nest sanitation activities when predator chemical cues were detected inside the nest-box, likely as an anti-predatory strategy to minimize their own risk of predation. Therefore, these results add to the evidence that birds can detect chemical cues of predators inside the nest cavity and suggest that the behavioral response to an increase in risk of predation perceived through olfactory cues is not sex-dependent in blue tit.
DOI: 10.6084/m9.figshare.19735549.v1
title: Assessing behavioral sex differences to chemical cues of predation risk while provisioning nestlings in a hole-nesting bird

description: Birds can assess nest predation risk and adjust their parental activity accordingly. Risk taking behavior should be related to investment in reproduction as well as to confidence in parenthood that often differ between sexes. In those cases, sexual differences in risk taking behavior may be expected. For example, in blue tits, females invest more time and energy than males in nest-building, egg laying and incubation. Furthermore, confidence in parenthood is supposed to be higher for females, as extrapair paternity is common in this species. Therefore, the reproductive value of nestlings may be higher for females than for males and the former may assume greater risks to ensure nestling growth and maximize their reproductive success. We examined potential sexual differences in the risk assumed by parents in relation to perceived risk of predation inside the nest cavity, where predation risk perception may be higher. We increased perceived predation risk by adding predator chemical cues inside blue tit (<em>Cyanistes caeruleus</em>) nest-boxes, and we tested whether female and male parents differed in the risk assumed when taking care of nestlings. Females and males did not differ in the risk assumed in response to perceived predation risk. However, females reduced time devoted to nest sanitation activities when predator chemical cues were detected inside the nest-box, likely as an anti-predatory strategy to minimize their own risk of predation. Therefore, these results add to the evidence that birds can detect chemical cues of predators inside the nest cavity and suggest that the behavioral response to an increase in risk of predation perceived through olfactory cues is not sex-dependent in blue tit.
DOI: 10.25739/1ttq-2q60
title: MArVD2 data

description: Training, benchmarking, reference data, and a random forest model for novel archaeal virus identification from metagenomic data. This model can be implemented with MArVD2 located at https://bitbucket.org/MAVERICLab/marvd2/
DOI: 10.7910/dvn/xfqla6
title: Teamsters 2021 Election Tweets

description: This dataset contains the tweet identifiers of 976 tweets posted by accounts associated with the two Twitter handles active during an important campaign to elect a new General President of the International Brotherhood of Teamsters following the retirement from the position of James P. Hoffa after 24 years. Sean O'Brien was the eventual winner of the election; during the campaign, he used the Twitter handle @OBrien_OZ2021. Also included are the tweets of UnitedTeamsters, a group campaigning for the union to take a new direction. No Twitter handle associated with O'Brien's primary opponent, Steve Vairma, was identified. Tweets were collected between July 2, 2021 and October 27, 2021, from the GET statuses/user_timeline method of the Twitter v1 REST API using Social Feed Manager. The teamsters_election_ids.txt file contains the IDs of the tweets collected. The list of screen names included in the collection can be found in the teamsters_election_README.txt file. The teamsters_election_README.txt file also contains additional documentation on how the tweets were collected, including the dates and times that screen names were added to collecting, and when collections occurred. The GET statuses/lookup method supports retrieving the complete tweet for a tweet ID (known as hydrating). Tools such as Twarc or Hydrator can be used to hydrate tweets. Per Twitter’s Developer Policy, tweet ids may be publicly shared for academic purposes; tweets may not. Questions about this dataset can be sent to sfm@gwu.edu. George Washington University researchers should contact us for access to the tweets.
DOI: 10.25919/ehr4-aq77
title: Parkes observations for project P455 semester 2022APRS_01

description: This project is to continue timing and profile studies of the first double-pulsar system, a unique laboratory for gravitational physics. Recent results, including the first measurements of higher-order light-propagation effects and of the relativistic deformation of the orbit, highlight the importance of a long term observational campaign for this remarkable system. The main aims of this proposal are to exploit the high sensitivity of the UWL receiver to provide the strongest tests to date for general relativity and to measure for the first time the moment-of-inertia of a neutron star. Additionally, we will search for the reappearance of the B pulsar.
DOI: 10.6084/m9.figshare.19735564
title: Plant responses to soil biota depend on precipitation history, plant diversity, and productivity

description: Data from Lundell et al. 2022 Ecology. This includes plant biomass data from five species used in a growth chember experiment. These species were seeded together into pots inoculated with live and sterile soils collected from 12 grassland sites that had been subjected to drought treatments. The pots were also exposed to another drought treatment over the course of the study.
DOI: 10.6084/m9.figshare.19735564.v2
title: Lundell et al 2022 Ecology soil biota and drought response data

description: Data from Lundell et al. 2022 Ecology. This includes plant biomass data from five species used in a growth chember experiment. These species were seeded together into pots inoculated with live and sterile soils collected from 12 grassland sites that had been subjected to drought treatments. The pots were also exposed to another drought treatment over the course of the study.
DOI: 10.3886/e167021
title: Code for: Parental Deportation, Safe Zone Schools, and the Socio-Emotional and Behavioral Health of Children Left Behind

description: These files reproduce the results in "Parental Deportation, Safe Zone Schools, and the Socio-Emotional and Behavioral Health of Children Left Behind" published in the AEA Papers and Proceedings. Access to the individual-level data is restricted and must be requested.
DOI: 10.3886/e167021v1
title: Code for: Parental Deportation, Safe Zone Schools, and the Socio-Emotional and Behavioral Health of Children Left Behind

description: These files reproduce the results in "Parental Deportation, Safe Zone Schools, and the Socio-Emotional and Behavioral Health of Children Left Behind" published in the AEA Papers and Proceedings. Access to the individual-level data is restricted and must be requested.
DOI: 10.5281/zenodo.6533452
title: Probing TDP-43 Condensation using an In Silico Designed Aptamer

description: Aptamer super-resolution and SAVE imaging data for Figure 4 and Figure 5 in publication.
DOI: 10.5281/zenodo.6533761
title: Campus Pond (Buffalo Creek Watershed) Data from 2021-11-23 to 2022-02-16

description: 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: <pre><code>LP_CampusPond_DO_YYYY-MM-DD_metadata.txt LP_CampusPond_Depth_YYYY-MM-DD_metadata.txt LP_CampusPond_CT_YYYY-MM-DD_metadata.txt</code></pre> 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. 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: <pre><code>* 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.</code></pre> The sensors are sampled every 15 minutes Measurement Parameters, units, and Variable Names <pre><code>* 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 - the temperature (dC) from the DO, conductivity, * WaterTemp - The water temperature from the pressure transducer under water (dC) * AirTemp - the air temperature from the pressure transducer (BP sensor) in the air (dC) * 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)</code></pre>