cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8035094
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Betty Molesworth Allen, http://www.wikidata.org/entity/Q16537579. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q16537579
DOI: 10.3886/e182564v2
title: Identifying Topics of Privacy Using Posts from Reddit's r/privacy

description: Document-term and document-feature matrices created from titles of posts on r/privacy. Quarto script detailing analyses.
DOI: 10.5281/zenodo.10474721
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Abraham van Luyk, http://www.wikidata.org/entity/Q124217426. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q124217426
DOI: 10.5281/zenodo.10474722
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Abraham van Luyk, http://www.wikidata.org/entity/Q124217426. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q124217426
DOI: 10.5281/zenodo.10515924
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Abraham van Luyk, http://www.wikidata.org/entity/Q124217426. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q124217426
DOI: 10.5281/zenodo.10515718
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Russell Steele Pelly, http://www.wikidata.org/entity/Q75914991. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q75914991
DOI: 10.5281/zenodo.10515719
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Russell Steele Pelly, http://www.wikidata.org/entity/Q75914991. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q75914991
DOI: 10.5281/zenodo.10515925
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Russell Steele Pelly, http://www.wikidata.org/entity/Q75914991. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q75914991
DOI: 10.5281/zenodo.10000165
title: Unveiling Hurdles in Software Engineering Education: The Role of Learning Management Systems

description: Learning management systems (LMSs) are established tools in higher education, especially in the field of software engineering (SE). The onset of the COVID-19 pandemic further amplified the utilization of these systems, which necessitated their integration into educational curricula for both lecturers and students. However, adopting LMSs within SE education has presented distinctive challenges impeding their seamless incorporation into the courses. This paper aims to scrutinize the challenges and requirements encountered by professors, lecturers, and students in the domain of SE education when using LMSs. We conducted an empirical study that included (i) a survey with 47 professors/lecturers and 133 students, (ii) an analysis of the ensuing data, and (iii) 18 additional interviews conducted with professors and lecturers to delve into nuanced variations in viewpoints. The findings derived from our study reveal that the challenges and requirements pertaining to LMSs are rather specific depending on the scope and size of the respective courses. Nevertheless, many participants have a consensus on numerous challenges and requirements for improving certain features of LMSs in order to improve their usage in SE education. The findings are valuable for advancing research and development in the field of LMSs and provide guidance for lecturers in SE education.
DOI: 10.5281/zenodo.8420389
title: Unveiling Hurdles in Software Engineering Education: The Role of Learning Management Systems

description: Learning management systems (LMSs) are established tools in higher education, especially in the field of software engineering (SE). The onset of the COVID-19 pandemic further amplified the utilization of these systems, which necessitated their integration into educational curricula for both lecturers and students. However, adopting LMSs within SE education has presented distinctive challenges impeding their seamless incorporation into the courses. This paper aims to scrutinize the challenges and requirements encountered by professors, lecturers, and students in the domain of SE education when using LMSs. We conducted an empirical study that includes (i) a survey with 47 professors/lecturers and 133 students, (ii) an analysis of the ensuing data, and (iii) 18 additional interviews conducted with professors and lecturers to delve into nuanced variations in viewpoints. The findings derived from our study reveal that the challenges and requirements pertaining to LMSs are rather specific depending on the scope and size of the respective courses. Nevertheless, many participants have a consensus on numerous challenges and requirements for improving certain features of LMSs in order to improve their usage in SE education. The findings are valuable for advancing research and development in the field of LMSs and provide guidance for lecturers in SE education.
DOI: 10.5281/zenodo.8434501
title: Unveiling Hurdles in Software Engineering Education: The Role of Learning Management Systems

description: Learning management systems (LMSs) are established tools in higher education, especially in the field of software engineering (SE). The onset of the COVID-19 pandemic further amplified the utilization of these systems, which necessitated their integration into educational curricula for both lecturers and students. However, adopting LMSs within SE education has presented distinctive challenges impeding their seamless incorporation into the courses. This paper aims to scrutinize the challenges and requirements encountered by professors, lecturers, and students in the domain of SE education when using LMSs. We conducted an empirical study that included (i) a survey with 47 professors/lecturers and 133 students, (ii) an analysis of the ensuing data, and (iii) 18 additional interviews conducted with professors and lecturers to delve into nuanced variations in viewpoints. The findings derived from our study reveal that the challenges and requirements pertaining to LMSs are rather specific depending on the scope and size of the respective courses. Nevertheless, many participants have a consensus on numerous challenges and requirements for improving certain features of LMSs in order to improve their usage in SE education. The findings are valuable for advancing research and development in the field of LMSs and provide guidance for lecturers in SE education.
DOI: 10.5281/zenodo.10515856
title: Unveiling Hurdles in Software Engineering Education: The Role of Learning Management Systems

description: Learning management systems (LMSs) are established tools in higher education, especially in the field of software engineering (SE). The onset of the COVID-19 pandemic further amplified the utilization of these systems, which necessitated their integration into educational curricula for both lecturers and students. However, adopting LMSs within SE education has presented distinctive challenges impeding their seamless incorporation into the courses. This paper aims to scrutinize the challenges and requirements encountered by professors, lecturers, and students in the domain of SE education when using LMSs. We conducted an empirical study that included (i) a survey with 47 professors/lecturers and 133 students, (ii) an analysis of the ensuing data, and (iii) 18 additional interviews conducted with professors and lecturers to delve into nuanced variations in viewpoints. The findings derived from our study reveal that the challenges and requirements pertaining to LMSs are rather specific depending on the scope and size of the respective courses. Nevertheless, many participants have a consensus on numerous challenges and requirements for improving certain features of LMSs in order to improve their usage in SE education. The findings are valuable for advancing research and development in the field of LMSs and provide guidance for lecturers in SE education.
DOI: 10.20383/102.0546
title: SalishSeaCast hourly surface along-axis wind velocity, temperature and nitrate summary 2015-2019

description: Aggregated hourly surface along-axis wind velocity, temperature and nitrate results for 2015-2019 from the SalishSeaCast hourly hindcast archive (https://salishsea.eos.ubc.ca/erddap/). This file is a companion asset to the following publication: B. Moore-Maley and S. E. Allen: Wind-driven upwelling and surface nutrient delivery in a semi-enclosed coastal sea, Ocean Sci., 2022. SalishSeaCast is a NEMO 3.6 configuration for the Salish Sea (https://salishsea-meopar-docs.readthedocs.io/). Along-axis wind velocity and wind stress are calculated from the Environment and Climate Change Canada HRDPS model (https://weather.gc.ca/grib/grib2_HRDPS_HR_e.html). This file has a companion software repository, which includes scripts to recreate the file (https://github.com/SalishSeaCast/SoG_upwelling_EOF_paper).
DOI: 10.5281/zenodo.8420388
title: Unveiling Hurdles in Software Engineering Education: The Role of Learning Management Systems

description: Learning management systems (LMSs) are established tools in higher education, especially in the field of software engineering (SE). The onset of the COVID-19 pandemic further amplified the utilization of these systems, which necessitated their integration into educational curricula for both lecturers and students. However, adopting LMSs within SE education has presented distinctive challenges impeding their seamless incorporation into the courses. This paper aims to scrutinize the challenges and requirements encountered by professors, lecturers, and students in the domain of SE education when using LMSs. We conducted an empirical study that included (i) a survey with 47 professors/lecturers and 133 students, (ii) an analysis of the ensuing data, and (iii) 18 additional interviews conducted with professors and lecturers to delve into nuanced variations in viewpoints. The findings derived from our study reveal that the challenges and requirements pertaining to LMSs are rather specific depending on the scope and size of the respective courses. Nevertheless, many participants have a consensus on numerous challenges and requirements for improving certain features of LMSs in order to improve their usage in SE education. The findings are valuable for advancing research and development in the field of LMSs and provide guidance for lecturers in SE education.
DOI: 10.6069/mazf-2w73
title: Targeted Proteomics Reveals Quantitative Differences in Low Abundance Glycosyltransferases of Patients with Congenital Disorders of Glycosylation

description: Protein glycosylation is an essential posttranslational modification in all domains of life. Its impairment in human can result in severe diseases named Congenital Disorders of Glycosylation (CDGs).  Most of the glycosyltransferases (GTs) responsible for proper glycosylation are polytopic membrane proteins that represent challenging targets in proteomics. We established a multiple reaction monitoring (MRM) assay to comprehensively quantify GTs involved in the processes of N-glycosylation, O- and C-mannosylation in the endoplasmic reticulum. High robustness was achieved by using an enriched membrane protein fraction of isotopically labeled HEK 293T cells as an internal protein standard. The analysis of primary skin fibroblasts from eight CDG type I patients with impaired ALG1, ALG2, or ALG11 genes revealed substantial reduction in the levels of corresponding proteins. The abundance of the other GTs, however, remained unchanged on transcript as well as on protein level, indicating that there is no fail-safe mechanism for this essential pathway. The established MRM assay is easily shared with the scientific community via the open source Skyline software environment, including Skyline Batch for automated data analysis. We demonstrate that another research group could easily reproduce all analysis steps even while using different LC-MS hardware.
DOI: 10.5281/zenodo.10494771
title: Regional model (ACM) output used to investigate the uncertainty in regional future projections of the northwest North Atlantic (Rutherford et al., 2024; BG)

description: Key variables from the regional Atlantic Canada model (ACM) used to investigate the uncertainty in projections of the northwest North Atlantic. The dataset includes all model variables required to reproduce the key results in Rutherford et al. (2024, BG). See Rutherford_etal_BG_ACM_data_README.txt for more details.
DOI: 10.5281/zenodo.10494772
title: Regional model (ACM) output used to investigate the uncertainty in regional future projections of the northwest North Atlantic (Rutherford et al., 2024; BG)

description: Key variables from the regional Atlantic Canada model (ACM) used to investigate the uncertainty in projections of the northwest North Atlantic. The dataset includes all model variables required to reproduce the key results in Rutherford et al. (2024, BG). See Rutherford_etal_BG_ACM_data_README.txt for more details.
DOI: 10.34740/kaggle/dsv/7409887
title: crypto-data-daily-updates
DOI: 10.6084/m9.figshare.25000589
title: Specimen Records <i>Philonthus splendens</i><i> </i>+ <i>Philonthus sideropterus.</i><i> </i>From Geisler et al. 2024

description: Dataset on the distribution of P. splendens and P. sideropterus
DOI: 10.6084/m9.figshare.25000589.v1
title: Specimen Records <i>Philonthus splendens</i><i> </i>+ <i>Philonthus sideropterus.</i><i> </i>From Geisler et al. 2024

description: Dataset on the distribution of P. splendens and P. sideropterus
DOI: 10.5517/ccdc.csd.cc2fmslg
title: CCDC 2253346: Experimental Crystal Structure Determination

description: Related Article: Jyotirmayee Dash|2023|CSD Communication|||
description: Related Article: Soumya Jyoti Basak, Jyotirmayee Dash|2023|J.Org.Chem.|89|233|doi:10.1021/acs.joc.3c01996
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.25000601
title: T1w.nii.gz

description: t1w image<br>
DOI: 10.6084/m9.figshare.25000601.v1
title: T1w.nii.gz

description: t1w image<br>
DOI: 10.6084/m9.figshare.25000592
title: Water balance of Zé Pereira CZO

description: Water balance of the José Pereira river basin Critical Zone Observatory.
DOI: 10.34740/kaggle/dsv/7410012
title: BBC News
DOI: 10.5281/zenodo.10515968
title: CryptoSignalMonitor

description: This dataset encompasses a diverse collection of crypto signals derived from popular tweets by various Twitter influencers, providing insights into their impact on user behavior within the cryptocurrency space.Primary Tweets List:



Tweet Text: The textual content of the tweet.

Link: URL link to the tweet.

Author: Username of the tweet author.

Category: Categorization of the tweet content i-e general or Specific.

Date: Date of the tweet.

Number of Likes: Count of likes received by the tweet.

Value in U.S Dollar Before the Tweet: Initial value of a specified cryptocurrency before the tweet.

Value in U.S Dollar After the Tweet (Date, Open, High, Low, Close): Cryptocurrency value parameters after the tweet, including date, open, high, low, and close.

Change Status: Status indicating the change in cryptocurrency value.

% Value Before Tweet: Percentage value of the cryptocurrency before the tweet.

Change in % Value Before Tweet: Percentage change in cryptocurrency value before the tweet.

% Value After Tweet: Percentage value of the cryptocurrency after the tweet.

Trading Volume Before Tweet: Trading volume of the cryptocurrency before the tweet.

Trading Volume After Tweet: Trading volume of the cryptocurrency after the tweet.

Change Status: Status indicating the change in trading volume.


Tweet Replies List:



File #: Unique identifier for the file containing tweet replies.

Tweet Link: URL link to the original tweet.

Author: Username of the reply author.

Category: Categorization of the reply content.

IDs: Unique identifiers for the replies.

Raw Data: Original raw data of the reply.

Sentiment Analysis Results: Results of sentiment analysis for the reply.

Date: Date of the reply.

Total - Comments: Total number of negative comments.

Total + Comments: Total number of positive comments.

Total Neutral Comments: Total number of neutral comments.

Total Comments/Replies: Overall count of comments and replies to the original tweet.


This metadata provided ensures a comprehensive understanding of the dataset structure and variables, fostering transparency, and facilitating its re-use for research purposes. Researchers are encouraged to explore the dataset for insights into cryptocurrency discussions on social media.
DOI: 10.5281/zenodo.10515969
title: CryptoSignalMonitor

description: This dataset encompasses a diverse collection of crypto signals derived from popular tweets by various Twitter influencers, providing insights into their impact on user behavior within the cryptocurrency space.Primary Tweets List:



Tweet Text: The textual content of the tweet.

Link: URL link to the tweet.

Author: Username of the tweet author.

Category: Categorization of the tweet content i-e general or Specific.

Date: Date of the tweet.

Number of Likes: Count of likes received by the tweet.

Value in U.S Dollar Before the Tweet: Initial value of a specified cryptocurrency before the tweet.

Value in U.S Dollar After the Tweet (Date, Open, High, Low, Close): Cryptocurrency value parameters after the tweet, including date, open, high, low, and close.

Change Status: Status indicating the change in cryptocurrency value.

% Value Before Tweet: Percentage value of the cryptocurrency before the tweet.

Change in % Value Before Tweet: Percentage change in cryptocurrency value before the tweet.

% Value After Tweet: Percentage value of the cryptocurrency after the tweet.

Trading Volume Before Tweet: Trading volume of the cryptocurrency before the tweet.

Trading Volume After Tweet: Trading volume of the cryptocurrency after the tweet.

Change Status: Status indicating the change in trading volume.


Tweet Replies List:



File #: Unique identifier for the file containing tweet replies.

Tweet Link: URL link to the original tweet.

Author: Username of the reply author.

Category: Categorization of the reply content.

IDs: Unique identifiers for the replies.

Raw Data: Original raw data of the reply.

Sentiment Analysis Results: Results of sentiment analysis for the reply.

Date: Date of the reply.

Total - Comments: Total number of negative comments.

Total + Comments: Total number of positive comments.

Total Neutral Comments: Total number of neutral comments.

Total Comments/Replies: Overall count of comments and replies to the original tweet.


This metadata provided ensures a comprehensive understanding of the dataset structure and variables, fostering transparency, and facilitating its re-use for research purposes. Researchers are encouraged to explore the dataset for insights into cryptocurrency discussions on social media.
DOI: 10.5281/zenodo.10499867
title: Prefrontal Manifold Geometry Contributes to Reaction Time Variability

description: Spike counts from cells recorded from the Frontal Eye Fields (FEF) and dorso-lateral prefrontal cortex (DLPFC) of 2 non-human primates preforming a delayed memory saccade task. Each file contains spike counts for all cells aligned to either target onset, go-cue onset or movement (saccade) onset. The suffix 'raw' indicates whether the file contains raw spike counts, or spike counts normalized to the pre-fixation baseline. 

The data are stored in an HD5-based format for objects created with the Julia progamming language. The following code snippet shows how to load the data

 

```julia

using JLD2

ppsth,labels, trialidx, rtimes = JLD2.load("ppsth_fef_mov.jld2","ppsth", "labels","trialidx","rtimes")

```

Here, the variable `ppsth` contains the spike counts in `ppsth.counts`, the bins in `ppsth.bins`. The variable `labels` contains the label of the target shown for each trial and for each cell. Note that, `length(labels)==size(ppsth.counts,3)` is the number of cells and `length(labels[1])` is the number of correct trails for cell `. The variable `rtimes` contains the reaction time for each session used. The session name for each cell can be found by examining the variable `ppsth.cellnames`, where the name of each cell has the format "Animal/date/session/array/channel/cellid/", e.g. "J/20140904/session01/array01/channel001/cell01" denotes the first cell on the first channel of the first array recorded in the first session on 4th September 2014 from animal J.

In addition to spike counts, this dataset also contains processed data for producing the main figures in an upcoming manuscript. To reproduce the figures, first go to the paper's repository and follow the installation instructions. Then, download the data files to a 'data' sub-folder, and run the codes as instructed in the repository's README file.