cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13061605.v1
title: SM_Row data_dailyVOA&growth from Growth and behaviour of blue mussels, a re-emerging polar resident, follow a strong annual rhythm shaped by the extreme high Arctic light regime

description: Polar regions are currently warming at a rate above the global average. One issue of concern is the consequences on biodiversity in relation to the Northward latitudinal shift in distribution of temperate species. In the present study, lasting almost two years, we examined two phenological traits, i.e. the shell growth and behavioural rhythm of a recently re-established species in the high Arctic, the blue mussel <i>Mytilus sp</i>. We compared this with a native species, the Islandic scallop <i>Chlamys islandica</i>. We show marked differences in the examined traits between the two species. In <i>Mytilus sp.</i>, a clear annual pattern of shell growth strongly correlated to the valve behaviour rhythmicity, whereas <i>C. islandica</i> exhibited a shell growth pattern with a total absence of annual rhythmicity of behaviour. The shell growth was highly correlated to the photoperiod for the mussels but weaker for the scallops. The water temperature cycle was a very weak parameter to anticipate the phenology traits of both species. This study shows that the new resident in the high Arctic, <i>Mytilus sp.</i>, is a highly adaptive species, and therefore a promising bioindicator to study the consequences of biodiversity changes due to global warming.
DOI: 10.6084/m9.figshare.13061605
title: SM_Row data_dailyVOA&growth from Growth and behaviour of blue mussels, a re-emerging polar resident, follow a strong annual rhythm shaped by the extreme high Arctic light regime

description: Polar regions are currently warming at a rate above the global average. One issue of concern is the consequences on biodiversity in relation to the Northward latitudinal shift in distribution of temperate species. In the present study, lasting almost two years, we examined two phenological traits, i.e. the shell growth and behavioural rhythm of a recently re-established species in the high Arctic, the blue mussel <i>Mytilus sp</i>. We compared this with a native species, the Islandic scallop <i>Chlamys islandica</i>. We show marked differences in the examined traits between the two species. In <i>Mytilus sp.</i>, a clear annual pattern of shell growth strongly correlated to the valve behaviour rhythmicity, whereas <i>C. islandica</i> exhibited a shell growth pattern with a total absence of annual rhythmicity of behaviour. The shell growth was highly correlated to the photoperiod for the mussels but weaker for the scallops. The water temperature cycle was a very weak parameter to anticipate the phenology traits of both species. This study shows that the new resident in the high Arctic, <i>Mytilus sp.</i>, is a highly adaptive species, and therefore a promising bioindicator to study the consequences of biodiversity changes due to global warming.
DOI: 10.25345/c5vx90
title: MassIVE MSV000086244 - Test*Characterization of Lyase Isomerase MpeV in Synechoccus sp. RS-9916
title: This is only a silly test to see if I can get a data set to upload before I turn 50. Do I have 50 characters yet?
DOI: 10.5281/zenodo.4071923
title: Southern Ocean Data Base

description: This is the database put together by Orsi and Whitworth III, downloaded originally at this site http://woceatlas.tamu.edu/ It was used to compute the published atlas: Orsi, A. H., and T. Whitworth III, 2005. Hydrographic Atlas of the World Ocean Circulation Experiment (WOCE). Volume 1: Southern Ocean (eds. M. Sparrow, P. Chapman and J. Gould), International WOCE Project Office, Southampton, U.K., ISBN 0-904175-49-9.<br> <br> The DOI (Digital Object Identifier) for the printed WOCE Southern Ocean Atlas is doi:10.21976/C6BC78
DOI: 10.5281/zenodo.4071922
title: Southern Ocean Data Base

description: This is the database put together by Orsi and Whitworth III, downloaded originally at this site http://woceatlas.tamu.edu/ It was used to compute the published atlas: Orsi, A. H., and T. Whitworth III, 2005. Hydrographic Atlas of the World Ocean Circulation Experiment (WOCE). Volume 1: Southern Ocean (eds. M. Sparrow, P. Chapman and J. Gould), International WOCE Project Office, Southampton, U.K., ISBN 0-904175-49-9.<br> <br> The DOI (Digital Object Identifier) for the printed WOCE Southern Ocean Atlas is doi:10.21976/C6BC78
DOI: 10.7284/130598
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AR30-03 using a Hawaii UHDAS instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130604
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AR30-03 using a Sea-Bird SBE-911+ instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130591
title: Fluorometer data collected during research cruise AR30-03 using a WET Labs WETStar instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130597
title: GNSS (Global Navigation Satellite System) data collected during research cruise AR30-03 using a C&C C-Nav 3050 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130592
title: Gyrocompass data collected during research cruise AR30-03 using a Sperry NAVIGAT X MK 1 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130605
title: Meteorological Station data collected during research cruise AR30-03 using a Vaisala WXT520 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130607
title: Multibeam Echosounder data collected during research cruise AR30-03 using a Kongsberg EM122 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130595
title: Radiometer data collected during research cruise AR30-03 using a Biospherical QSR-2150 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130596
title: Radiometer data collected during research cruise AR30-03 using a Eppley RAD instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130594
title: Singlebeam Echosounder data collected during research cruise AR30-03 using a Knudsen 3260 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130590
title: Speedlog data collected during research cruise AR30-03 using a JRC JLN-550 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130593
title: SSV (Sound Speed Velocimeter) data collected during research cruise AR30-03 using a AML SmartX P1S2 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130589
title: Water Temperature data collected during research cruise AR30-03 using a Sea-Bird SBE-48 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/130588
title: TSG (Thermosalinograph) data collected during research cruise AR30-03 using a Sea-Bird SBE-45 instrument system onboard the platform RV Neil Armstrong

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5517/ccdc.csd.cc25j8ss
title: CCDC 2011645: Experimental Crystal Structure Determination

description: Related Article: Pierre Baillargeon, Tarik Rahem, Rémi Charest, Krystyna Lyubchynska|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25j8zz
title: CCDC 2011651: Experimental Crystal Structure Determination

description: Related Article: Pierre Baillargeon, Tarik Rahem, Julien Villeneuve, Alexandra Côté|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25jg17
title: CCDC 2011808: Experimental Crystal Structure Determination

description: Related Article: Pierre Baillargeon, Tarik Rahem, Stéphanie Lepage, Charles Boily|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25k2sm
title: CCDC 2012420: Experimental Crystal Structure Determination

description: Related Article: Mikhail E. Minyaev, Sergey M. Ivanov|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25k777
title: CCDC 2012558: Experimental Crystal Structure Determination

description: Related Article: Steven Mansfield, Edward Anderson|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25kc26
title: CCDC 2012677: Experimental Crystal Structure Determination

description: Related Article: Milan Gembicky|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25kc37
title: CCDC 2012678: Experimental Crystal Structure Determination

description: Related Article: Milan Gembicky|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25kc48
title: CCDC 2012679: Experimental Crystal Structure Determination

description: Related Article: Milan Gembicky|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25kg07
title: CCDC 2012768: Experimental Crystal Structure Determination

description: Related Article: Peter Amseis, Armin Berndt, Gertraud Geiseler, Werner Massa|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25kx1q
title: CCDC 2013203: Experimental Crystal Structure Determination

description: Related Article: Kedar U. Narvekar, Vishal M. Pawar, Vishnu S. Nadkarni, Bikshandarkoil R Srinivasan|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25kx2r
title: CCDC 2013204: Experimental Crystal Structure Determination

description: Related Article: Kedar U. Narvekar, Vishal M. Pawar, Vishnu S. Nadkarni, Bikshandarkoil R Srinivasan|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25kx3s
title: CCDC 2013205: Experimental Crystal Structure Determination

description: Related Article: Kedar U. Narvekar, Vishal M. Pawar, Vishnu S. Nadkarni, Bikshandarkoil R Srinivasan|2020|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc25nzkd
title: CCDC 2016164: Experimental Crystal Structure Determination

description: Related Article: Simone Bertini, Martin Albrecht|2020|Organometallics|39|3413|doi:10.1021/acs.organomet.0c00495
DOI: 10.5517/ccdc.csd.cc25nzlf
title: CCDC 2016165: Experimental Crystal Structure Determination

description: Related Article: Simone Bertini, Martin Albrecht|2020|Organometallics|39|3413|doi:10.1021/acs.organomet.0c00495
DOI: 10.17909/t9-rnk7-gr88
title: Pan-STARRS1 Source Types and Redshifts with Machine Learning ("PS1-STRM")

description: PS1-STRM is a neural network source classification and photometric redshift catalog created from the PanSTARRS1 (PS1) 3π DR1. Neural networks have been trained on a compilation of spectroscopic measurements, cross-matched with PS1. Based on PS1 forced mean photometry, a source is classified as galaxy, star, quasar, or unsure. For galaxies, photometric redshift estimation is performed, also yielding an estimate of redshift error via Monte-Carlo sampling. Sources lying outside the parameter coverage of the training set (i.e. extrapolated sources) are identified using self-organizing maps. Classification and photo-z results are provided for every source in the PS1 3π DR1 ForcedMeanObject table, a total of 2,902,054,648 objects. See the README file and the primary reference paper for a detailed description of the catalog metadata.
DOI: 10.5281/zenodo.3560890
title: Genome-wide association summary statistics for sex- and age-specific analysis of chronic back pain

description: The dataset comprises summary-level statistics for age- and sex-specific genome-wide association study of chronic back pain (cBP) in individuals of European descent from UK Biobank (https://www.ukbiobank.ac.uk/). The study was carried out under UK Biobank approved project #18219. <strong>The dataset accompanies the paper (please cite if using the dataset):</strong> Freidin, Maxim B.; Tsepilov, Yakov A.; Stanaway, Ian B.; Meng, Weihua; Hayward, Caroline; Smith, Blair H.; Khoury, Samar; Parisien, Marc; Bortsov, Andrey; Diatchenko, Luda; Børte, Sigrid; Winsvold, Bendik S.; Brumpton, Ben M.; Zwart, John-Anker; HUNT All-In Pain; Aulchenko, Yurii S.; Suri, Pradeep; Williams, Frances M.K. Sex- and age-specific genetic analysis of chronic back pain. Pain. 2020. doi:10.1097/j.pain.0000000000002100. The phenotype of cBP was defined as back pain for 3+ months. Linear mixed-effects additive model was fitted adjusting for age, genotyping array type, and 10 genetic PCs provided by UK Biobank. The following filters were applied: minor allele frequency &gt;0.001, genotyping and individual call rates &gt;0.98%, imputation quality score (INFO) &gt;0.7. GWAS were carried out in males and females separately in the whole sample (<strong>allages</strong>) as well as in groups of younger than 65 years (<strong>under65</strong>) and 65+ years old (<strong>65plus</strong>) as detailed in the paper. Accordingly, 6 files are deposited here, corresponding to each group. <strong>Column headers:</strong> SNP, SNP rsID CHR, chromosome BP, genomic position (GRCh37 build) EA, effect allele (coded as "1") OTHER, other allele (coded as "0") A1FREQ, frequency of effect allele INFO, imputation quality BETA, effect size (for effect allele) SE, standard error of effect size PVAL, p-value for association
DOI: 10.5281/zenodo.3560889
title: Genome-wide association summary statistics for sex- and age-specific analysis of chronic back pain

description: The dataset comprises summary-level statistics for age- and sex-specific genome-wide association study of chronic back pain (cBP) in individuals of European descent from UK Biobank (https://www.ukbiobank.ac.uk/). The study was carried out under UK Biobank approved project #18219. <strong>The dataset accompanies the paper (please cite if using the dataset):</strong> Freidin, Maxim B.; Tsepilov, Yakov A.; Stanaway, Ian B.; Meng, Weihua; Hayward, Caroline; Smith, Blair H.; Khoury, Samar; Parisien, Marc; Bortsov, Andrey; Diatchenko, Luda; Børte, Sigrid; Winsvold, Bendik S.; Brumpton, Ben M.; Zwart, John-Anker; HUNT All-In Pain; Aulchenko, Yurii S.; Suri, Pradeep; Williams, Frances M.K. Sex- and age-specific genetic analysis of chronic back pain. Pain. 2020. doi:10.1097/j.pain.0000000000002100. The phenotype of cBP was defined as back pain for 3+ months. Linear mixed-effects additive model was fitted adjusting for age, genotyping array type, and 10 genetic PCs provided by UK Biobank. The following filters were applied: minor allele frequency &gt;0.001, genotyping and individual call rates &gt;0.98%, imputation quality score (INFO) &gt;0.7. GWAS were carried out in males and females separately in the whole sample (<strong>allages</strong>) as well as in groups of younger than 65 years (<strong>under65</strong>) and 65+ years old (<strong>65plus</strong>) as detailed in the paper. Accordingly, 6 files are deposited here, corresponding to each group. <strong>Column headers:</strong> SNP, SNP rsID CHR, chromosome BP, genomic position (GRCh37 build) EA, effect allele (coded as "1") OTHER, other allele (coded as "0") A1FREQ, frequency of effect allele INFO, imputation quality BETA, effect size (for effect allele) SE, standard error of effect size PVAL, p-value for association
DOI: 10.6084/m9.figshare.12988313.v5
title: Continuation and user satisfaction of the levonorgestrel intrauterine system (LNG IUS) contraceptive in Nigeria

description: Dataset was from a prospective longitudinal phone survey made up of baseline survey of users at 2-weeks post-insertion of LNG IUS, recruited from 40 clinics across 18 states in Nigeria, with follow-up survey at the third and twelfth-month. A total 209 users were interviewed at baseline, 98 at three-months and 73 at twelve-month. User family planning and LNG IUS use experiences were elicited, and the continuation and satisfaction with the method at three and twelve months were also assessed. Average age of users of LNG IUS in the study at baseline is 33.7±6, and majority are aged 35 years and older (44%), followed by those aged 30-34 years and 25-29 years old which are approximately 30% and 20%. At the 3 and 12-months post-insertion, 97% (95% CI: 91.2, 98.8) and 92% (95% CI: 83.2, 96.2), respectively, reported still using the LNG IUS, with none out of the few users who discontinued the method reporting a method failure. Discontinuation was mainly as a result of desire to get pregnant (25.0%), experiences of bleeding disturbances (18.8%) and weight gain (18.8%). High satisfaction with the LNG IUS, 77.1% at 3-months and 86.3% at 12-month post-insertion was reported. Satisfaction with LNG IUS was associated with not having headache/migraine (88.4%) and no pelvic discomfort (90.8%) at 12-month than experiencing headache (50.0%) and pelvic discomfort (50.0%) (p&lt;0.05).
DOI: 10.6084/m9.figshare.11888187
title: ms_manuscript publication.dta

description: This is a study conducted in 2016 to evaluate the use of information and communications technologies (TICs) among medical students of 40 local scientific societies of medical students affiliated to the Latin American Federation of Medical Students Scientific Societies (FELSOCEM, in Spanish). Local teams in each local scientific society surveyed self-reported scientific publications and explored its association with socioeconomic, academic, and research training conditions
DOI: 10.6084/m9.figshare.11888187.v1
title: ms_manuscript publication.dta

description: This is a study conducted in 2016 to evaluate the use of information and communications technologies (TICs) among medical students of 40 local scientific societies of medical students affiliated to the Latin American Federation of Medical Students Scientific Societies (FELSOCEM, in Spanish). Local teams in each local scientific society surveyed self-reported scientific publications and explored its association with socioeconomic, academic, and research training conditions
DOI: 10.6084/m9.figshare.13061693
title: data Silene gene flow for repository.xlsx

description: Data of the effects of different gene flow treatments on germination time, flowering time and seedling survival of Silene ciliata.
DOI: 10.6084/m9.figshare.13061696
title: JVASP-13509 to JVASP-13597(Negative bias)

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.13061696.v1
title: JVASP-13509 to JVASP-13597(Negative bias)

description: JARVIS-DFT
DOI: 10.5519/qd.ac3fh5ww
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4662251 records)
DOI: 10.5281/zenodo.4071944
title: Song Preference CLassification Dataset for Gen Z

description: This dataset contains audio recordings of 12 different accents across the UK: Northern Ireland, Scotland, Wales, North East England, North West England, Yorkshire and Humber, East Midlands, West Midlands, East of England, Greater London, South East England, South West England. We split the data into a Male: Female ratio of 1:1. The audio dataset was compiled using opensource YouTube videos and it a collation of different accents, the audio files were trimmed for uniformity. The Audio files are of length 30 seconds, with the first 5 seconds and last 5 seconds of the signal being blank. We also resample the audio signals at 8 kHz, again for uniformity and to remove any noise present in the audio signals whilst retaining the underlying characteristics. The intended application of this dataset was to be used in conjunction with a deep neural network for accent and gender classification tasks. This dataset was recorded for an experimentation looking into applying machine learning techniques for the task of classifying song preference amongst generation Z (18 to 24 years) participants. We define a labelling system corresponding to specific songs with 5 ratings: hate, dislike, neutral, like and love. The songs used for this experiment were chosen due their success for various awards, such as the BRIT awards (BRIT), Mercury Prize (MERC), Rolling Stone most influential albums (ROLS). They are as shown: S1: One Kiss by Calvin Harris and Dua Lipa (BRIT) S2: Don't Delete the Kisses by Wolf Alice MERC) S3: Money by Pink Floyd (ROLS) S4: Shotgun by George Ezra (BRIT) S5: Location by Dave (MERC) S6: Smells Like Teen Spirit by Nirvana (ROLS) S7: God's Plan by Drake (BRIT) S8: Breezeblocks by alt-J (MERC) S9: Lucy In The Sky With Diamonds by The Beatles (ROLS) S10: Thank U, Next by Ariana Grande (BRIT) S11: Shutdown by Skepta (MERC) S12: Billie Jean by Micheal Jackson (ROLS) A Unicorn Hybrid Black was used for recording the EEG data from the participants whilst they were played the control songs listed above. For each of the 12 total song played to a participant during the experiment, there were 8 EEG lead recordings measured of length 20 seconds, with the first 5 seconds and the last 5 seconds being blank for control purposes. The EEG signals were sampled at 250 Hz by the Unicorn Hybrid Black devices, which also filtered the signals to be between 2Hz to 30 Hz in order to remove any noise recorded during the experimentation. There are approximately 5000 data points per reading of a given song, with there being 12 songs played to a total of 10 participants.
DOI: 10.5281/zenodo.4071943
title: Song Preference CLassification Dataset for Gen Z

description: This dataset contains audio recordings of 12 different accents across the UK: Northern Ireland, Scotland, Wales, North East England, North West England, Yorkshire and Humber, East Midlands, West Midlands, East of England, Greater London, South East England, South West England. We split the data into a Male: Female ratio of 1:1. The audio dataset was compiled using opensource YouTube videos and it a collation of different accents, the audio files were trimmed for uniformity. The Audio files are of length 30 seconds, with the first 5 seconds and last 5 seconds of the signal being blank. We also resample the audio signals at 8 kHz, again for uniformity and to remove any noise present in the audio signals whilst retaining the underlying characteristics. The intended application of this dataset was to be used in conjunction with a deep neural network for accent and gender classification tasks. This dataset was recorded for an experimentation looking into applying machine learning techniques for the task of classifying song preference amongst generation Z (18 to 24 years) participants. We define a labelling system corresponding to specific songs with 5 ratings: hate, dislike, neutral, like and love. The songs used for this experiment were chosen due their success for various awards, such as the BRIT awards (BRIT), Mercury Prize (MERC), Rolling Stone most influential albums (ROLS). They are as shown: S1: One Kiss by Calvin Harris and Dua Lipa (BRIT) S2: Don't Delete the Kisses by Wolf Alice MERC) S3: Money by Pink Floyd (ROLS) S4: Shotgun by George Ezra (BRIT) S5: Location by Dave (MERC) S6: Smells Like Teen Spirit by Nirvana (ROLS) S7: God's Plan by Drake (BRIT) S8: Breezeblocks by alt-J (MERC) S9: Lucy In The Sky With Diamonds by The Beatles (ROLS) S10: Thank U, Next by Ariana Grande (BRIT) S11: Shutdown by Skepta (MERC) S12: Billie Jean by Micheal Jackson (ROLS) A Unicorn Hybrid Black was used for recording the EEG data from the participants whilst they were played the control songs listed above. For each of the 12 total song played to a participant during the experiment, there were 8 EEG lead recordings measured of length 20 seconds, with the first 5 seconds and the last 5 seconds being blank for control purposes. The EEG signals were sampled at 250 Hz by the Unicorn Hybrid Black devices, which also filtered the signals to be between 2Hz to 30 Hz in order to remove any noise recorded during the experimentation. There are approximately 5000 data points per reading of a given song, with there being 12 songs played to a total of 10 participants.