cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7885936
title: Erythropoietin restrains the inhibitory potential of interneurons in the mouse hippocampus: electrophysiology data

description: This publication contains the recording data of the electrophysiology section of the manuscript entitled<strong> Erythropoietin restrains the inhibitory potential of interneurons in the mouse hippocampus</strong>. Files are organized as follows: Raw_LFP.zip contains the raw local field potential (LFP) recordings of mice's dorsal hippocampus (CA1), under head-fix recording set up using Open Ephys GUI. README.txt file contains de information about the codification data of mice. Matlab and R scripts used for data processing are available upon request
DOI: 10.5281/zenodo.7885935
title: Erythropoietin restrains the inhibitory potential of interneurons in the mouse hippocampus: electrophysiology data

description: This publication contains the recording data of the electrophysiology section of the manuscript entitled<strong> Erythropoietin restrains the inhibitory potential of interneurons in the mouse hippocampus</strong>. Files are organized as follows: Raw_LFP.zip contains the raw local field potential (LFP) recordings of mice's dorsal hippocampus (CA1), under head-fix recording set up using Open Ephys GUI. README.txt file contains de information about the codification data of mice. Matlab and R scripts used for data processing are available upon request
DOI: 10.5281/zenodo.8369654
title: Na-hP4 basis-set

description: Basis functions, reported in Crystal17 format, used to study Na-hP4 at 190 GPa.
DOI: 10.5281/zenodo.8369653
title: Na-hP4 basis-set

description: Basis functions, reported in Crystal17 format, used to study Na-hP4 at 190 GPa.
DOI: 10.57745/bn4psl
title: Mjav-G4+telomeres.pdf

description: G4 and Mjav repeat distribution in Mjav contigs
DOI: 10.5255/ukda-sn-856681
title: Centre for Climate Change and Social Transformations: Cardiff Travel Survey, Wave 3, 2023

description: The Cardiff Travel Survey is a longitudinal survey that aims to (a) establish current and previous (before the coronavirus outbreak) travel habits; (b) explore how travel-related attitudes, social norms and perceptions change over time; and (c) examine the interplay between individual (perceptual) and environmental (infrastructural) factors in travel mode choice, in particular in relation to the uptake of active travel such as walking and cycling in the City of Cardiff, Wales. The Cardiff Travel Survey 2023 (Wave 3) is an opportunity sample that was collected in 2023 (n=1,886) by the Centre for Climate Change and Social Transformations (CAST), and is the third of a longitudinal series of surveys to be held annually for the duration of the centre. Data for the Cardiff Travel Survey 2023 were collected between 11 May 2023 and 19 July 2023. Participants of the Cardiff Travel Survey 2022 who consented (n=739) were recontacted via email to invite them to take part in the 2023 survey. Furthermore, participants were recruited through posts on social media, such as Facebook® and Twitter®. Invitations were posted on CAST and investigator accounts. The posts on Facebook were promoted to make them more visible in the area. The survey was hosted on the Qualtrics online survey platform and available in both English and Welsh. Inclusion criteria were that participants had to be at least 18 years of age and live in or travel regularly to Cardiff. The English version of the survey was completed by 2,033 respondents and the Welsh version by 56 respondents. Incomplete responses (n=152), defined as those without any answers beyond socio-demographic, were removed from the dataset. A further 51 respondents did not complete the first section on current travel behaviours and were also removed. This left a final sample of n=1,886 adults. Participants were asked to create a unique code that can be used match this survey to the previous and next surveys without knowing their identity. Main topic areas of the questionnaire are: Demographics, Travel behaviours, Physical activity, Physical health and mental wellbeing, Perceptions of infrastructure and environmental quality, Travel-related social identity, Attitudes to active travel, Active travel related social norms, Support for active travel policies, and Unique ID.
DOI: 10.5281/zenodo.8360991
title: OSValidate

description: OSValidate dataset to validate SUMo's best hyperparameter combinations in both filtering and matching phase. This dataset is composed of multiple pcap files containing the packets exchanged when clients accessed clearweb sites and onion services using the Tor browser, automatized with Selenium.
description: Other funding bodies: NSERC under grant RGPIN-2023-03304, and IAPMEI under grant C6632206063-00466847.
DOI: 10.5281/zenodo.8360990
title: OSValidate

description: OSValidate dataset to validate SUMo's best hyperparameter combinations in both filtering and matching phase. This dataset is composed of multiple pcap files containing the packets exchanged when clients accessed clearweb sites and onion services using the Tor browser, automatized with Selenium.
description: Other funding bodies: NSERC under grant RGPIN-2023-03304, and IAPMEI under grant C6632206063-00466847.
DOI: 10.21227/dhxt-5g91
title: ISSLIDE: A new InSAR dataset for Slow SLIding area DEtection with machine learning

description: Remote sensing images have been leveraged in multiple fields of applications: land-cover mapping, boat detections, flood and landslides delineations or post-disaster building damage assessment. Nevertheless, their wide spatial coverage has a cost in terms of resolution so that slow ground displacements and deformations are not visible. This issue is tackled by using the phase information of Synthetic Aperture Radar (SAR) images through Interferometric SAR (InSAR) approaches. Therefore, studying ground displacements requires both geomorphological and SAR interferometry knowledge. This dual-expertise requirement may explain the lack of InSAR datasets in the literature. The few available datasets based on InSAR are targeting volcanoes unresting period detection. But to the best of our knowledge, there is no dataset adapted to machine learning approaches and targeting slow gravitary movement detections. With this dataset, we propose a new InSAR dataset  for Slow SLIding areas DEtections (ISSLIDE) with machine learning. The dataset is composed of standardly processed interferograms and manual annotations created following geomorphologist strategies. A total of 200 independent moves are delineated on the each of the 54 interferograms. Extracting patches surrounding these moves ends up with 13,230 samples for training purpose. The dataset is provided for future user to develop new machine and deep learning methodologies in detecting and localizing slow moving areas with standard, multi-temporal or multi-interferogram studies.
DOI: 10.57745/v89lvs
title: Mare-G4+telomeres.pdf

description: G4 and Mare repeat distribution in Mare contigs
DOI: 10.34740/kaggle/dsv/6518431
title: Daikin Employee Reviews Dataset
DOI: 10.6084/m9.figshare.24182664
title: Processed overexpression RNA-seq (salmon)

description: Processed RNA-seq data from a gene overexpression experiment (done with salmon).There are three control and three overexpression replicates.For students to perform differential expression analysis with sleuth.
DOI: 10.6084/m9.figshare.24182664.v1
title: Processed overexpression RNA-seq (salmon)

description: Processed RNA-seq data from a gene overexpression experiment (done with salmon).There are three control and three overexpression replicates.For students to perform differential expression analysis with sleuth.
DOI: 10.12770/7fe77c80-798a-42d4-a69c-2b5f0ba81a43
title: WOC Geostrophic Surface Current Estimated by the BFN-QG over Agulhas Region

description: For over 25 years, satellite altimetry has provided invaluable information about the ocean dynamics at many scales. Gridded Sea Surface Height (SSH) maps allow to estimate the mesoscale geostrophic circulation in the ocean. However, conventional interpolation techniques rely on static optimal interpolation schemes, hence limiting the estimation of non-linear dynamics at scales not well sampled by altimetry (i.e., below 150-200km at mid latitudes). To overcome this limitation in the resolution of small-scale SSH structures (and thus small-scale geostrophic currents), a Back-and-Forth Nudging algorithm combined with a Quasi-Geostrophic model, a technique called BFN-QG, has been implemented within the theme 1 of the ESA World Ocean Circulation (WOC) project. The performances have been evaluated within Observing System Experiments (OSEs) that use independent data (such as independent SSH, Sea Surface Temperature and drifter data) as ground-truth. By comparing the mapping performances to the ones obtained by operational products, the BFN-QG improves the mapping of short, energetic mesoscale structures and associated geostrophic currents both in space and time. In particular, the BFN-QG improves (i) the spatial effective resolution of the SSH maps by a factor of 20%, (ii) the zonal and (especially) the meridional geostrophic currents and (iii) the prediction of Lagrangian transport for lead times up to 10 days. The data are available through HTTP and FTP; access to the data is free and open. In order to be informed about changes and to help us keep track of data usage, we encourage users to register at: https://forms.ifremer.fr/lops-siam/access-to-esa-world-ocean-circulation-project-data/ This dataset was generated by Datlas and is distributed by Ifremer / CERSAT in the frame of the World Ocean Circulation (WOC) project funded by the European Space Agency (ESA).
DOI: 10.34740/kaggle/ds/3768191
title: Daikin Employee Reviews Dataset
DOI: 10.6084/m9.figshare.24182721
title: <b>Data from: </b><b>Expansion limits of the Yellow-legged hornet-mediated beekeeping risk in Western Europe</b>

description: This dataset result from the large-scale citizen science program aiming at accurately mapping the distribution of V. velutina in apiaries across France and Germany. Overall, a total of 1697 responses from beekeepers on the presence or absence of V. velutina predating in the apiaries across France (n=1368 responses) and Germany (n=329 responses). The responses were widely distributed throughout France and Germany. The hornet was established throughout the French territory as expected, and mostly detected near the French border in Germany. We found that HBR was substantially lower in Germany than in France. Temperature had a positive effect on both the hornet presence and HBR whereas the distance to the introduction point had a negative effect, both in France and Germany. These results suggest that the expansion of V. velutina in Western Europe could be reduced on the eastern front due to continental climate. The spatial variability in HBR suggests that the impact of V. velutina on beekeeping is not homogenous throughout the invasion range, in particular in Germany. Considering HBR spatial variability could help formulating regionally adapted management plans to limit V. velutina impacts on biodiversity, human health and economic sectors. The data are related to the scientific paper "Requier, F., et al. (under review) Expansion limits of the Yellow-legged hornet-mediated beekeeping risk in Western Europe. NeoBiota ».Data are available as a csv file titled "Requier et al._data Yellow-legged hornet in France and Germany.csv". To keep the data anonymous, we removes the Latitude and Longitude informations. # METADATA<br>#'data.frame': 1697 obs. of 8 variables:<br># $ ID : Factor variable ; a unique identity for the reponse to the survey<br># $ Year : Factor variable ; two factors are available, representing the year when the response was collected, with "2018" for the first year of the survey and "2019" for the second year of the survey <br># $ Country : Factor variable ; two factors are available (France and Germany), representing the country name were the response was collected<br># $ Presence : Numeric variable; the absence (0) or the presence (1) of V. velutina at the observation point<br># $ Number : Numeric variable; the maximum number of hornet individuals observed at the same time predating in front of a beehive<br># $ Distance : Numeric variable; the distance (in km) to the introduction site as an euclidean distance in kilometers considering the location of accidental introduction in 2004 in the south-western France in the Lot et Garonne department <br># $ Temperature : Numeric variable; the annual ambient mean temperature (in °C) at the observation point<br># $ Urban_habitat : Numeric variable; the proportion of urbanized areas around the observation point in an area of 10 km2 around the GPS coordinates
DOI: 10.34740/kaggle/dsv/6518462
title: Blue Star Employee Reviews Dataset
DOI: 10.25345/c5hh6cg9s
title: MassIVE MSV000092937 - Characterization of molecular partners and physiological functions of the conserved RNA binding protein Rbp3
title: The RNA recognition motif (RRM) domain proteins are crucial RB (Rbps) in all domains of life. In cyanobacteria, proteins with a single RRM domain were suggested to be involved in mRNA targeting to the thylakoid membrane and the acclimation to certain stress conditions. The model cyanobacterium Synechocystis sp. PCC 6803 contains a small family of 3 genes encoding RRM-domain containing Rbps, Rbp1, Rbp2 and Rbp3. With a focus on Rbp3, here, we addressed the molecular details of its functions and the associated physiological consequences. To elucidate the set of interacting proteins, co-immunoprecipitation experiments were performed with a strain expressing an Rbp3 with a triple FLAG tag added to its C-terminus.
DOI: 10.13120/df09d7cc-fda3-4e23-8e0d-aea6add88805
title: Seafloor litter - Spatial distribution of plastic bags density normalized per km² 2011/2022 v2023
title: sl_spatialdistribution_plasticbagabundance_30km

description: This visualization product displays the spatial distribution of plastic bags density per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during fishing bottom trawl surveys. In cases where the wingspread and/or number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities have been calculated on each trawl and year using the following computation: Density of plastic bags (number of items per km²) = ∑Number of plastic bags related items / Swept area (km²) Then a grid with 30km x 30km cells is used to calculate the weighted mean of densities in each cell from the formula : Weighted mean (number of items per km²) = ∑ (Distance (km) * Density (number of items per km²)) / ∑ Distance (km) Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. More information on data processing and calculation are detailed in the document attached. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.
DOI: 10.34740/kaggle/dsv/6518518
title: Voltas Employee Reviews Dataset
DOI: 10.6084/m9.figshare.23791512.v1
title: Thermal requirements for embryo growth and seed germination of <i>Fraxinus excelsior</i> L. (Oleaceae)

description: We have examined thermal control for embryo growth and seed germination in European ash. Using data historical data (span of 36 years) of embryo index in matured seeds we showed that maturation is not influenced by the annual average temperature. While embryo growth and germination are controlled by similar temperature range. Our results shows thermal windows for development and germination of ash seeds.
DOI: 10.13120/e78db7b8-b01b-4a5d-bde9-1237eb0bcfd7
title: Seafloor litter - Spatial distribution of fishing related items density normalized per km² 2011/2022 v2023
title: sl_spatialdistribution_fishingrelatedabundance_30km

description: This visualization product displays the spatial distribution of fishing related items density per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during fishing bottom trawl surveys. In cases where the wingspread and/or number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities have been calculated on each trawl and year using the following computation: Density of fishing related items (number of items per km²) = ∑Number of fishing related items / Swept area (km²) Then a grid with 30km x 30km cells is used to calculate the weighted mean of densities in each cell from the formula : Weighted mean (number of items per km²) = ∑ (Distance (km) * Density (number of items per km²)) / ∑ Distance (km) Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. More information on data processing and calculation are detailed in the document attached. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.
DOI: 10.13120/9a9bf6cb-6701-4074-b922-64a230044161
title: Seafloor litter - Spatial distribution of litter density normalized per km² 2011/2022 v2023
title: sl_spatialdistribution_totalabundance_30km

description: This visualization product displays the spatial distribution of seafloor litter density per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during fishing bottom trawl surveys. In cases where the wingspread and/or number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities have been calculated on each trawl and year using the following computation: Density (number of items per km²) = ∑Number of items / Swept area (km²) Then a grid with 30km x 30km cells is used to calculate the weighted mean of densities in each cell from the formula : Weighted mean (number of items per km²) = ∑ (Distance (km) * Density (number of items per km²)) / ∑ Distance (km) Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. More information on data processing and calculation are detailed in the document attached. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.
DOI: 10.13120/41953811-7541-483e-b243-256d23b01055
title: Seafloor litter - Plastic bags density normalized per km² 2011/2022 v2023
title: sl_plasticbags

description: This visualization product displays plastic bags density per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during fishing bottom trawl surveys. In cases where the wingspread and/or number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities have been calculated on each trawl and year using the following computation: Density of plastic bags (number of items per km²) = ∑Number of plastic bags related items / Swept area (km²) Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. The list of selected items for this product is attached to this metadata. Information on data processing and calculation is detailed in the attached methodology document. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.