cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13994858.v2
title: Interdisciplinary research integration survey 2021

description: Dataset from the evaluation survey on pilot convergence framework in Biofilm and computational engineering. The evaluation consisted of an 8 question survey (Q1:Who are you? - Q2:What is your primary discipline/background? - Q3:Select all disciplines involved in your current research project. - Q4:How long have you being involved with different labs (disciplines) to solve your research problem? - Q5:Rate how working with different labs (disciplines) help you in your research project and/or your career development so far ("0=not at all" AND "5=Very helpful"). - Q6:How confident do you feel recommending this experience to another student(1=not confident at all, 5=Highly confident)? - Q7: - Q8:How do you think your experience can be improved?). The survey was completed by all 19 participants. Out of 18 participants, 17 find the framework helpful (94.4% rate at 4 or 5 on a scale of 5) toward their research and career development. The same number feel confident recommending the framework to other students. (Google Form: https://docs.google.com/forms/d/e/1FAIpQLScVeYbRkV6csgM8hLmerYfiW8OxEhY-TYU_innUewi7WCfDEQ/viewform?usp=sf_link)
DOI: 10.6084/m9.figshare.13994858.v1
title: Interdisciplinary research integration survey 2021

description: The evaluation consisted of an 8 question survey (Q1:Who are you? - Q2:What is your primary discipline/background? - Q3:Select all disciplines involved in your current research project. - Q4:How long have you being involved with different labs (disciplines) to solve your research problem? - Q5:Rate how working with different labs (disciplines) help you in your research project and/or your career development so far ("0=not at all" AND "5=Very helpful"). - Q6:How confident do you feel recommending this experience to another student(1=not confident at all, 5=Highly confident)? - Q7: - Q8:How do you think your experience can be improved?). The survey was completed by all 19 participants. Figure XXX depicts the outcome of 4 key questions (Q1, Q2, Q6, Q7) and emphasizes the participants’ strong positive opinion about this framework. Out of 18 participants, 17 find the framework helpful (94.4% rate at 4 or 5 on a scale of 5) toward their research and career development. The same number feel confident recommending the framework to other students. (Google Form: https://docs.google.com/forms/d/e/1FAIpQLScVeYbRkV6csgM8hLmerYfiW8OxEhY-TYU_innUewi7WCfDEQ/viewform?usp=sf_link)
DOI: 10.5519/qd.q9g8byd3
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5517/ccdc.csd.cc26j902
title: CCDC 2041443: Experimental Crystal Structure Determination

description: Related Article: Yu-Ting Tian, Fa-Guang Zhang, Jun-An Ma|2021|Tetrahedron|81|131922|doi:10.1016/j.tet.2021.131922
DOI: 10.6084/m9.figshare.13951619
title: EFFECTIVE PRINCIPAL LEADERSHIP BEHAVIORS TO IMPROVE THE TEACHER PERFORMANCE AND THE STUDENT ACHIEVEMENT

description: This is the result of a questionnaire recapitulation of 281 teacher respondents. The X variable represents the principal's effectiveness, the Y1 variable represents the teacher's performance, and Y2 is the achievement which is divided into school exams and report card scores.
DOI: 10.6084/m9.figshare.13951619.v1
title: EFFECTIVE PRINCIPAL LEADERSHIP BEHAVIORS TO IMPROVE THE TEACHER PERFORMANCE AND THE STUDENT ACHIEVEMENT

description: This is the result of a questionnaire recapitulation of 281 teacher respondents. The X variable represents the principal's effectiveness, the Y1 variable represents the teacher's performance, and Y2 is the achievement which is divided into school exams and report card scores.
DOI: 10.5281/zenodo.3995098
title: Artificial Neural Network (ANN) Resampling Results for Gap Filling Eddy Covariance Data

description: These datasets are part of the study described in the manuscript submitted to Biogeosciences: Temporal patterns and biophysical controls on methane emissions from a small eutrophic reservoir: insights from two years of eddy covariance monitoring. The R code used to create these files is also available on Zenodo.
DOI: 10.5281/zenodo.3995097
title: Artificial Neural Network (ANN) Resampling Results for Gap Filling Eddy Covariance Data

description: These datasets are part of the study described in the manuscript submitted to Biogeosciences: Temporal patterns and biophysical controls on methane emissions from a small eutrophic reservoir: insights from two years of eddy covariance monitoring. The R code used to create these files is also available on Zenodo.
DOI: 10.6084/m9.figshare.9994736
title: Supplementary Data S2 - de novo reconstructed transcripts

description: To assemble de novo transcripts from RNA-seq data, Cufflinks v2.2.1 (Roberts et al., 2011) was used with default parameters, with the TAIR10 general feature format (GFF) annotation provided as a guide (option -g). Cuffcompare was applied to compare the set of de novo transcripts across genotypes. De novo transcripts overlapping a reference exon or intron but on the opposite strand (codes s and x, respectively), and associated with an RNA-seq up-regulated gene on the reverse strand were selected for assessing the length and splicing of exosome-sensitive transcripts. <br>
DOI: 10.6084/m9.figshare.9994736.v1
title: Supplementary Data S2 - de novo reconstructed transcripts

description: To assemble de novo transcripts from RNA-seq data, Cufflinks v2.2.1 (Roberts et al., 2011) was used with default parameters, with the TAIR10 general feature format (GFF) annotation provided as a guide (option -g). Cuffcompare was applied to compare the set of de novo transcripts across genotypes. De novo transcripts overlapping a reference exon or intron but on the opposite strand (codes s and x, respectively), and associated with an RNA-seq up-regulated gene on the reverse strand were selected for assessing the length and splicing of exosome-sensitive transcripts. <br>
DOI: 10.25919/fxm8-ej89
title: Parkes observations for project P885 semester 2020OCTS_12

description: We propose to continue observing the magnetars 1E 1547.0?5408 and PSR J1622?4950 weekly, to track their rotation, flux density, and polarisation. The revolutionary UWL capabilities promise new opportunities to disentangle still mysterious radio emission mechanisms in magnetars. This promise is supported by the recent follow up of XTE J1810?197, again a radio magnetar after 10 years dormant, and of the hot-off-the-press magnetar Swift J1818.0?1607, for which our observations with the UWL receiver are providing unique and tantalizing measurements of radio spectrum and polarisation.
DOI: 10.5281/zenodo.4540302
title: Archive of aggregate data on work and parenthood

description: These are aggregate files of labor force participation for mothers, fathers, and women not living with own children. Read ReadMe.txt file first for definitions and codes. Generated using the Current Population Survey monthly files from ipums.org. These files are meant to encourage discussion and analysis of labor force participation of mothers in comparison to fathers and women without own children in their home.
DOI: 10.6084/m9.figshare.14005130
title: Supplementary Files

description: Supplementary Files 1,2,3,4,8,9,10
DOI: 10.6084/m9.figshare.14005127
title: summary_enrich_pathways.orig.hess.tsv

description: summary_enrich_pathways.orig.hess.tsv
DOI: 10.6084/m9.figshare.14005274
title: all_hits_msiggenes.bed

description: all_hits_msiggenes.bed
DOI: 10.6084/m9.figshare.14005274.v1
title: all_hits_msiggenes.bed

description: all_hits_msiggenes.bed
DOI: 10.6084/m9.figshare.14005127.v1
title: summary_enrich_pathways.orig.hess.tsv

description: summary_enrich_pathways.orig.hess.tsv
DOI: 10.3886/e112618v1-79200
title: Replication data for: Place Based Policies with Unemployment

description: We develop a stylized model of frictional local labor markets with the goal of studying the efficiency of unemployment differences across areas. The model adapts the widely used Diamond-Mortensen-Pissarides framework to a local labor market setting with a competitive housing market. The result is a simple search analogue of the classic Roback model that provides a tractable environment for studying the effects of local job creation efforts.
DOI: 10.3886/e132341
title: Analysis of Social Media Discourse and Life Cycle Evolution

description: When international events happen, related discourse is abundant on local social media, less academic attention has been paid to comparisons between topical issues and user interactions in different platforms. Taking the US-China trade war as a case, this paper provides a discourse analysis framework to discuss the topical issues and temporal evolution trends of social media reflecting local users' opinions. This study uses text mining, topical analysis, and temporal trend analysis to examine how social media interactions between Twitter and Weibo users’ response to the discourse of the US-China trade war. We saved the complete datasets for both Twitter and Weibo platforms to the Microsoft Excel formatted file.
DOI: 10.3886/e132341v1
title: Analysis of Social Media Discourse and Life Cycle Evolution

description: When international events happen, related discourse is abundant on local social media, less academic attention has been paid to comparisons between topical issues and user interactions in different platforms. Taking the US-China trade war as a case, this paper provides a discourse analysis framework to discuss the topical issues and temporal evolution trends of social media reflecting local users' opinions. This study uses text mining, topical analysis, and temporal trend analysis to examine how social media interactions between Twitter and Weibo users’ response to the discourse of the US-China trade war. We saved the complete datasets for both Twitter and Weibo platforms to the Microsoft Excel formatted file.
DOI: 10.5281/zenodo.4540312
title: CoVaxxy Tweet IDs data set

description: A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see https://arxiv.org/abs/2101.07694 for more information.
DOI: 10.6084/m9.figshare.14005982
title: dataset of Modeling 5-km running performance on level and hilly terrains in recreational runners

description: Dataset of Modeling 5-km running performance on level and hilly terrains in recreational runners
DOI: 10.6084/m9.figshare.14006054
title: Non-core vocabulary words data

description: Data associated with 288 non-core vocabulary words from Merrian-Webster's Advanced Learner's English Dictionary.<br>
DOI: 10.6084/m9.figshare.14006054.v1
title: Non-core vocabulary words data

description: Data associated with 288 non-core vocabulary words from Merrian-Webster's Advanced Learner's English Dictionary.<br>
DOI: 10.6084/m9.figshare.14005973
title: Core vocabulary words data

description: Data associated with 288 core vocabulary words from Merriam-Webster's Advanced Learner's English Dictionary<br>
DOI: 10.6084/m9.figshare.12273128
title: Temporo-Parietal Cortex Involved in Modeling One’s Own and Others’ Attention

description: Dataset for the publication "Cortical Networks Involved in Modeling One’s Own and Others’ Attention" by Guterstam, Bio, Wilterson, and Graziano.<br><br>Average_t1_scan.nii - Average anatomical (t1) scan across all 32 subjects.<br>Source_data_Table2_and_Fig3.mat - Data for the main analyses (Table 2 and Figure 3). The order of the analyses in the data file is: row1=endo vs exo, row2=self vs other, row3=selfendo vs selfexo, row4=otherendo vs otherexo, row5=(endo vs exo) vs (self vs other)<br><br>Source_data_Table3_and_Fig4.mat - Data for the Social vs Nonsocial analyses (Table 3 and Figure 4).<br>
DOI: 10.6084/m9.figshare.12273128.v1
title: Temporo-Parietal Cortex Involved in Modeling One’s Own and Others’ Attention

description: Dataset for the publication "Cortical Networks Involved in Modeling One’s Own and Others’ Attention" by Guterstam, Bio, Wilterson, and Graziano.<br><br>Average_t1_scan.nii - Average anatomical (t1) scan across all 32 subjects.<br>Source_data_Table2_and_Fig3.mat - Data for the main analyses (Table 2 and Figure 3). The order of the analyses in the data file is: row1=endo vs exo, row2=self vs other, row3=selfendo vs selfexo, row4=otherendo vs otherexo, row5=(endo vs exo) vs (self vs other)<br><br>Source_data_Table3_and_Fig4.mat - Data for the Social vs Nonsocial analyses (Table 3 and Figure 4).<br>
DOI: 10.6084/m9.figshare.14005304
title: Summary statistics for secondary analyses of testosterone-related phenotypes

description: Summary statistics for secondary analyses of testosterone-related phenotypes
DOI: 10.6084/m9.figshare.14005304.v1
title: Summary statistics for secondary analyses of testosterone-related phenotypes

description: Summary statistics for secondary analyses of testosterone-related phenotypes
DOI: 10.6084/m9.figshare.14006141.v1
title: Ribosomal protein database of Listeria seeligeri strain 7KSM

description: This work presents the ribosomal protein database of <i>Listeria seeligeri strain </i>7KSM<i>. </i>Original data for the work came from the annotated proteome data of the bacterium downloaded from UniProt. Using an in-house MATLAB ribosomal protein database analysis software, the original proteome data file was parsed to extract protein name and amino acid sequence of all ribosomal proteins in the species. The database also includes calculated variables such as number of residues, molecular weight, and nucleotide sequence. Overall, the presented database could serve as a ribosomal protein mass fingerprint for use in microbial identification, or it could be used in fundamental studies seeking to uncover new insights into ribosomal protein biology.
DOI: 10.6084/m9.figshare.14006141
title: Ribosomal protein database of Listeria seeligeri strain 7KSM

description: This work presents the ribosomal protein database of <i>Listeria seeligeri strain </i>7KSM<i>. </i>Original data for the work came from the annotated proteome data of the bacterium downloaded from UniProt. Using an in-house MATLAB ribosomal protein database analysis software, the original proteome data file was parsed to extract protein name and amino acid sequence of all ribosomal proteins in the species. The database also includes calculated variables such as number of residues, molecular weight, and nucleotide sequence. Overall, the presented database could serve as a ribosomal protein mass fingerprint for use in microbial identification, or it could be used in fundamental studies seeking to uncover new insights into ribosomal protein biology.
DOI: 10.5281/zenodo.4540316
title: eQuilibrator_Group_Data

description: Group Definitions for eQuliibrator for use in https://gitlab.com/KShebek/equilibrator-assets Data were take from the quilt package on February 13, 2021. The following parameters were used to load the quilt data into python. equilibrator/component_contribution version 0.3.0
DOI: 10.5281/zenodo.4540315
title: eQuilibrator_Group_Data

description: Group Definitions for eQuliibrator for use in https://gitlab.com/KShebek/equilibrator-assets Data were take from the quilt package on February 13, 2021. The following parameters were used to load the quilt data into python. equilibrator/component_contribution version 0.3.0
DOI: 10.25919/tcb9-pp03
title: Parkes observations for project P1038 semester 2020OCTS_10

description: PSR~J1759-2402 is one of a small handful of pulsars which are in orbit about high mass stars. Its orbital period is 8.3 years and the orbit is highly eccentric with periastron expected in 2021 March. Although discovered at Parkes, the Lovell telescope has been monitoring the pulsar over many years. However, it’s restricted bandwidth, maintenance schedule and poorer polarization capability make it unsuitable to follow the pulsar as it enters and leaves the eclipse by the companion. In contrast, the UWL on Parkes with its wide- band and excellent polarization characteristics means that we can determine scattering, polarization, dispersion measure and rotation measure changes as the pulsar enters its companion wind. We request a continuation of our weekly observations of the pulsar to track its progress around the orbit.
DOI: 10.25919/fdbd-r018
title: Parkes observations for project P456 semester 2020OCTS_07

description: The Parkes Pulsar Timing Array (PPTA) project has three primary goals: (a) detection of gravitational waves from astronomical sources, (b) establishment of a pulsar timescale, and (c) improvement of our understanding of Solar-system dynamics. The PPTA is the oldest of three international pulsar timing groups. We have the smallest telescope and the smallest group, but the best measurements and the best results. Our bound on the strength of the incoherent gravitational wave background is the only bound which significantly constrains theoretical models. We have maintained our leading position because: we have the southern sky; we have a small but well-focussed group; we have the best-calibrated receivers; and we have been able to observe with an almost regular cadence of 15 to 20 days over a wide bandwidth. With this proposal we aim to maintain our pre-eminent position in the field. Unlike most observing proposals, this is a continuing proposal for which the observations will continue to improve bounds on ultra-low-frequency gravitational waves until they are finally detected. Continued Parkes observations will remain valuable at least until the first stage of the SKA is able to improve on our sensitivity and observing cadence. Even after the gravitational wave background is detected we will want to continue observations in support of the nascent field of gravitational wave astronomy!
DOI: 10.6084/m9.figshare.13116740
title: CCN Flows and Finances

description: Community Cellular Data Files<br>=============================<br><br>Anonymized data files for open &lt;3 science<br><br>Generated with pandas 1.1.3 and dask 2.30.0<br><br>pandas parquet files (engine=fastparquet)<br>-----------------------------------------<br>* log_gaps.parquet - Times when no data was logged at all (indicates network likely down)<br>* transactions_TM.parquet - Timezone aligned and trimmed transaction file<br>* user_active_deltas.parquet - Computed metadata for each user about their length of activity in the network.<br><br>dask parquet files (engine=fastparquet, compresson=snappy)<br>----------------------------------------------------------<br>* flows/typical_fqdn_org_category_local_TM_DIV_none_INDEX_start<br> - Flow logs with computed metadata per flow, between one user and a<br> non-user server. Timezone aligned and trimmed. Index pre-built for<br> the start column.<br><br>* flows/p2p_TM_DIV_none_INDEX_start<br> - Flow logs between two users, timezone aligned and trimmed. Index<br> pre-built for the start column.
DOI: 10.6084/m9.figshare.13116740.v1
title: CCN Flows and Finances

description: Community Cellular Data Files<br>=============================<br><br>Anonymized data files for open &lt;3 science<br><br>Generated with pandas 1.1.3 and dask 2.30.0<br><br>pandas parquet files (engine=fastparquet)<br>-----------------------------------------<br>* log_gaps.parquet - Times when no data was logged at all (indicates network likely down)<br>* transactions_TM.parquet - Timezone aligned and trimmed transaction file<br>* user_active_deltas.parquet - Computed metadata for each user about their length of activity in the network.<br><br>dask parquet files (engine=fastparquet, compresson=snappy)<br>----------------------------------------------------------<br>* flows/typical_fqdn_org_category_local_TM_DIV_none_INDEX_start<br> - Flow logs with computed metadata per flow, between one user and a<br> non-user server. Timezone aligned and trimmed. Index pre-built for<br> the start column.<br><br>* flows/p2p_TM_DIV_none_INDEX_start<br> - Flow logs between two users, timezone aligned and trimmed. Index<br> pre-built for the start column.
DOI: 10.6084/m9.figshare.14006138.v1
title: Speedtest (Download Performance) vs NBN (Technology Maps)

description: This dataset merges two public datasets:- Speedtest network performance data for Australia (Q3, 2020), as loaded to AURIN geo-analysis platform (88,000 approx locations)- NBN mapping of Technology data (inc FTTN, FTTP, FTTC, FTTB, HFC, Wireless, Satellite); complete map of Australia, colour-coded by technology, in WMS or KML format. KML format used in this instance.<br>to produce an intersection datasets (Result: 319337 rows × 26 columns), including: <br>- data includes LocID, download speed, nbn tech, lat, Lon, SA2, SA3, SA4 (see ABS link below).But many techs in one Speedtest block (600m^2) so have to untangle.- a ten line sample (CSV) included.- see image as provided by AURIN - QGIS.png.<br>** METHOD: Advice from AURIN:<br>In order to join these two maps, you will need to perform a spatial join based on the two layers. It is possible to do this with geopandas.sjoin(), which by default performs an intersection join - that is, any portion of a matching polygon from the second layer is considered a match to join on. More information about spatial predicates is here in case you're looking for a different spatial relationship.<br>In this link, I've supplied a notebook (OoklaNBN-AURIN.ipynb) that collects the datasets from the AURIN API and data.gov.au, combines the several KML NBN layers into one, and joins them with the Ookla 2020 Q3 dataset. In order to use it, you will first need to input your AURIN API credentials into the first cell.<br>The spatial join occurs in the final notebook cell and writes its output to a geopackage (OoklaNBN.gpkg) which I've included also, as the script can take some time to run. You will notice that each Ookla cell now may be represented by many records, this is due to there being more than one overlapping NBN technology polygon. As one Ookla grid can cover many technology zones, aggregating these may be useful depending on how you approach your analysis.Regards AURIN.<br>The authors acknowledge the facilities and scientific and technical assistance of the NCRIS-enabled Australian Urban Research Infrastructure Network (AURIN)”Thanks Evan Thomas, AURIN - ORCiD: https://orcid.org/0000-0001-7564-4116<br>*** Preliminary Analysis1. Count of Tech TypeTech countFibre to the Basement (vectored or non-vectored) 14147Satellite 15946Fixed Wireless 23663Fibre to the Curb 36843Hybrid Fibre Coaxial (HFC) 41175Fibre to the Premises 93732Fibre to the Node 93831<br><br>2. Mean of Tech TypeNBN Tech Type Aus mean(mbps)Satellite 48.1674 Fixed Wireless 49.1477 Fibre to the Node 75.1639 Fibre to the Premises 83.71 Fibre to the Curb 86.8433 Hybrid Fibre Coaxial (HFC) 87.1248 Fibre to the Basement (vectored or non-vectored) 116.324<br>*** Licence;Speedtest licence (at AWS data licence) is "CC BY-NC-SA 4.0", so use of this data must be:- non-commercial (NC)- reuse must be share-alike (SA)(add same licence).This restricts the standard CC-BY Figshare licence.<br>
DOI: 10.5281/zenodo.4432958
title: CORD-19 Diachronic Concept Embeddings

description: Medical concept embeddings trained on monthly releases of the CORD-19 dataset, for analysis of diachronic change. This resource includes: Concept embeddings, trained using the JET toolkit on the set of articles added to CORD-19 in each month of March 2020 - October 2020. Concepts are SNOMED CT codes. Nearest neighborhood-based analysis results of the concept embeddings, stored as a SQLite database. Database content excludes terms and definitions for SNOMED CT codes, which are licensed content owned by SNOMED International. Information on accessing SNOMED licensed content may be found here.