cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20277804
title: Raw data: Respirometry, 2019, MMR at Pcrit

description: Raw respirometry data exported from NeoFox Viewer version 2.4 in .csv format <br> Treatment: MMR at Pcrit
DOI: 10.6084/m9.figshare.20277804.v1
title: Raw data: Respirometry, 2019, MMR at Pcrit

description: Raw respirometry data exported from NeoFox Viewer version 2.4 in .csv format <br> Treatment: MMR at Pcrit
DOI: 10.6084/m9.figshare.20277801
title: Raw data: Respirometry, 2019, MMR at 0.65 x Pcrit

description: Raw respirometry data exported from NeoFox Viewer version 2.4 in .csv format <br> Treatment: MMR at 0.65 x Pcrit
DOI: 10.6084/m9.figshare.20277801.v1
title: Raw data: Respirometry, 2019, MMR at 0.65 x Pcrit

description: Raw respirometry data exported from NeoFox Viewer version 2.4 in .csv format <br> Treatment: MMR at 0.65 x Pcrit
DOI: 10.6084/m9.figshare.20277468
title: Raw data: Respirometry, 2019, background respiration

description: Raw data from analysis of background respiration following MMR-SMR-Pcrit trials in 2019 study, .csv format exported from Neofox Viewer version 2.4
DOI: 10.6084/m9.figshare.20277468.v1
title: Raw data: Respirometry, 2019, background respiration

description: Raw data from analysis of background respiration following MMR-SMR-Pcrit trials in 2019 study, .csv format exported from Neofox Viewer version 2.4
DOI: 10.6084/m9.figshare.20368911
title: S_tragus_fastQ131

description: fastQ files for individuals at site 131, 3 fastQ per individual. Larger two are the paired end reads and the smaller one is reads that could not be assigned to an end.
DOI: 10.6084/m9.figshare.20368911.v1
title: S_tragus_fastQ131

description: fastQ files for individuals at site 131, 3 fastQ per individual. Larger two are the paired end reads and the smaller one is reads that could not be assigned to an end.
DOI: 10.6084/m9.figshare.20368917
title: Supplementary_Data_[Emerging_Technologies_in_STEM_Education]

description: Dataset for Silva-Díaz et al. (2022). Emerging Technologies in STEM Education. A Bibliometric Analysis of Publications in Scopus & WoS (2010–2020).
DOI: 10.6084/m9.figshare.20368917.v1
title: Supplementary_Data_[Emerging_Technologies_in_STEM_Education]

description: Dataset for Silva-Díaz et al. (2022). Emerging Technologies in STEM Education. A Bibliometric Analysis of Publications in Scopus & WoS (2010–2020).
DOI: 10.5255/ukda-sn-855867
title: Prisoners on Prisons: Experiences of Peer-Delivered Suicide Prevention Work, 2020-2021

description: This is qualitative data from a focus group conducted in September 2020 within a study that examined how the Prisons and Probation Ombudsman (seek to) effect change in prisons following prisoner suicides and how death investigations could have more impact on prison policy and practice. The study ran from 2019-2021. Within this project, a focus group was conducted with former prisoners in England and Wales (n=5): Across jurisdictions, prisoner suicide rates consistently exceed those amongst comparable groups in the general population (Zhong et al., 2021). Deaths in coercive institutions threaten the right to life, which is ‘the most fundamental of all human rights [and a] condition of the enjoyment of other rights’ (Owen and Macdonald, 2015: 121). The high rates of suicide and self-harm in prison are of international concern (Dear, 2006). Prison suicide reduction has been designated a priority activity by the World Health Organisation (2007) and England and Wales’ Ministry of Justice (2016). generating very significant harm and costs (Roulston et al., 2021; Author, 2021). Peer support is part of the (international) response to suicidal prisoners, but too little is known about the experience of these interventions. For data storage and analysis purposes the transcript has been carefully anonymised with any potentially identifiable details removed. Within the transcript the 5 former prisoners have deliberately not been differentiated between to protect their identity. Because of the sensitivity of this research, transcripts of follow up support and analysis groups have been omitted due to the participants still being identifiable following transcript anonymisation. Further information about the project and links to publications are available on the University of Nottingham SafeSoc project webpage https://www.safesoc.co.uk
DOI: 10.5281/zenodo.6901623
title: THP-1 DNase I cell culture under confocal microscope

description: THP-1 DNase I cell imaged in suspension using a confocal microscope LSM780 Zeiss The cells have their nucleus, membrane, and mitochondria stained respectively using DAPI, Phalloidin, and TOMM20. The mitochondria signal is deconvoluted using Huygens SVI software. This is a high magnification cell culture data example for cell segmentation and mitochondria segmentation in 3D.
DOI: 10.5281/zenodo.6901622
title: THP-1 DNase I cell culture under confocal microscope

description: THP-1 DNase I cell imaged in suspension using a confocal microscope LSM780 Zeiss The cells have their nucleus, membrane, and mitochondria stained respectively using DAPI, Phalloidin, and TOMM20. The mitochondria signal is deconvoluted using Huygens SVI software. This is a high magnification cell culture data example for cell segmentation and mitochondria segmentation in 3D.
DOI: 10.24378/exe.3904
title: Differentiated Integration - expert survey 1

description: This is an expert survey on different normative questions relating to differentiated integration.
description: The article associated with this dataset is located in ORE at: http://hdl.handle.net/10871/127933
DOI: 10.6073/pasta/787e578bf75bf9caab7574c8c5024ec7
title: Microclimate at Harvard Forest HEM, LPH and EMS Towers since 2005

description: Microclimatic data are collected continuously within footprint areas of the flux towers in order to (1) understand relationships between climatic and microclimatic conditions and carbon dioxide and water vapor exchange between forest and atmosphere, (2) to develop predictive models for the exchange rates of these gases, based on microclimate data, (3) detect long-term trends in climate and microclimate at the sites and (4) determine whether the response of carbon dioxide and water vapor exchange processes to climate or microclimate is changing over time. The primary climatic and microclimatic variables measured at these sites include: above-canopy photosynthetically active radiation (PAR), forest understory PAR at 1 m height, total net radiation exchange measured above the forest canopy, air temperature and relative humidity above the forest canopy, forest understory air temperature at 20 cm and 1 m above ground level (shielded but not ventilated thermocouple sensors, used only to estimate nighttime understory air temperature), soil temperature at 10 cm depth, and soil moisture.
DOI: 10.6084/m9.figshare.20368932
title: Towards process-oriented management of tropical reefs in the Anthropocene

description: Dataset of standing biomass, biomass production and biomass turnover used in the "<strong>Towards process-oriented management of tropical reefs in the Anthropocene" </strong>publication <br> RLS_Management.Rdata is the final dataset divided into the different management classes and used for all subsequent analyses. <br> raw_data.Rdata is the raw Reef Life Survey dataset used for this study before all filters and data cleaning operations <br> All figures are supplied as resolution png.
DOI: 10.6084/m9.figshare.20368932.v1
title: Towards process-oriented management of tropical reefs in the Anthropocene

description: Dataset of standing biomass, biomass production and biomass turnover used in the "<strong>Towards process-oriented management of tropical reefs in the Anthropocene" </strong>publication <br> RLS_Management.Rdata is the final dataset divided into the different management classes and used for all subsequent analyses. <br> raw_data.Rdata is the raw Reef Life Survey dataset used for this study before all filters and data cleaning operations <br> All figures are supplied as resolution png.
DOI: 10.6084/m9.figshare.19904407
title: Neoherpotrichiella juglandicola phylogenetic data

description: The combined ITS, LSU and <em>tub2</em> sequence data set.
DOI: 10.6084/m9.figshare.19904407.v1
title: Neoherpotrichiella juglandicola phylogenetic data

description: The combined ITS, LSU and <em>tub2</em> sequence data set.
DOI: 10.5287/bodleian:jbj1zzjbg
title: Hernia GWAS summary statistics
DOI: 10.6073/pasta/42efb36a8771e7af03445fe5d45f633e
title: Microclimate at Harvard Forest HDW Tower since 2014

description: The HDW (Hardwood) microclimate and walkup tower at Harvard Forest became operational in April 2014. The site was chosen to represent the mixed hardwood forest around the EMS and NEON towers and to provide a platform for research projects without access to those towers. Microclimate data are archived annually and recent data are available upon request. Measured values include air temperature, relative humidity, PAR, net radiation, albedo, wind speed and direction, soil temperature and soil moisture.
DOI: 10.6084/m9.figshare.20263820
title: Predictive Subdata Selection for Computer Models

description: An explosion in the availability of rich data from the technological advances is hindering efforts at statistical analysis due to constraints on time and memory storage, regardless of whether researchers employ simple methods (e.g., linear regression) or complex models (e.g., Gaussian processes). A recent approach to overcoming these limits involves information-based optimal subdata selection and Latin hypercube subagging. In the current study, we develop a novel subdata selection method for large-scale computer models based on expected improvement optimization. Numerical and empirical analysis using real-world data are used to select subdata by which to derive accurate predictions. During the optimization procedure, the proposed scheme employs the geometry of the input feature region as well as information related to output values. The data points associated with the largest improvement in prediction accuracy are combined in the construction of a subdataset that can be used to formulate predictions with affordable computing time. Supplementary materials for this article, including proofs of theorems and additional numerical results, are available online.
DOI: 10.6084/m9.figshare.20263820.v1
title: Predictive Subdata Selection for Computer Models

description: An explosion in the availability of rich data from the technological advances is hindering efforts at statistical analysis due to constraints on time and memory storage, regardless of whether researchers employ simple methods (e.g., linear regression) or complex models (e.g., Gaussian processes). A recent approach to overcoming these limits involves information-based optimal subdata selection and Latin hypercube subagging. In the current study, we develop a novel subdata selection method for large-scale computer models based on expected improvement optimization. Numerical and empirical analysis using real-world data are used to select subdata by which to derive accurate predictions. During the optimization procedure, the proposed scheme employs the geometry of the input feature region as well as information related to output values. The data points associated with the largest improvement in prediction accuracy are combined in the construction of a subdataset that can be used to formulate predictions with affordable computing time. Supplementary materials for this article, including proofs of theorems and additional numerical results, are available online.
DOI: 10.34740/kaggle/dsv/3984985
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.6901525
title: Nanopore Translocation Signal

description: This dataset contains a set of nanopore translocation current traces. It is divided in two parts. <strong>Part I:</strong> This part contains artificially generated traces with different levels of background noise (SNR = 4, 2, 1, 0.5, and 0.25) For each noise level, three parameters are varied in data generation: a. Twenty different concentrations of nanoparticles as the analytes (Cnp): 0.013, 0.016, 0.020, 0.025, 0.032, 0.040, 0.050, 0.063, 0.080, 0.1, 0.13, 0.16, 0.20, 0.25, 0.32, 0.40, 0.50, 0.63, 0.80, and 1, with the unit of nano-molar, [nM]. b. Fifteen different diameters of the nanoparticles (Dnp): 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17, with the unit of nanometer, [nm]. c. Five different translocation durations (Duration): 0.5, 1.0, 1.5, 3.0, and 5.0, with the unit of millisecond, [ms]. In total we have 20*15*5=1500 current traces for each SNR. There are three datasets: training, validation and test. Traces in training datasets are of 20 seconds, traces in validation and test datasets are 10 seconds long. For SNR = 4, training, validation and test datasets are provided. For other SNRs, only test traces are provided. <strong>Pert II</strong>: This part contains real experimental datasets, the translocation of Lambda DNA and Streptavidin with 6 current traces each at different bias voltages. Each Lambda DNA trace has 71 seconds, while each Streptavidin trace has 126 seconds. Two truncated pyramid shape nanopores were used in our experiments, one with a side length of 7.5 nm and another 16 nm, both in a 55 nm-thick silicon layer, for DNA and protein streptavidin translocation, respectively. The DNA and streptavidin were dispersed in 500 mM KCl electrolyte with a concentration of 78 pM and 84 nM, respectively. <br> Details of artificially generated data and experimental data can be found in our paper: Dario Dematties, Chenyu Wen, Mauricio David Pérez, Dian Zhou, Shi-Li Zhang. Deep learning of nanopore sensing signals using a bi-path network. arXiv:2105.03660. <strong>Trained and Validated Models</strong> In this data set we also include all the trained and best validated models evaluated in our paper. <strong>Nanopore Translocation Detector Trained and Validated Models </strong> In this data set we also include all the trained and best validated models evaluated in our newer paper: <em>A Generalized Transformer-Based Pulse Detection Algorithm</em>
description: The Artificial data set is compressed and split into "n" .zip files. Once you downloaded such files you can use the following sequence of commands for joining and unzipping them: zip -F Artificial_Data.zip --out single-archive.zip unzip single-archive.zip
DOI: 10.5281/zenodo.5013855
title: Nanopore Translocation Signal

description: This dataset contains a set of nanopore translocation current traces. It is divided in two parts. <strong>Part I:</strong> This part contains artificially generated traces with different levels of background noise (SNR = 4, 2, 1, 0.5, and 0.25) For each noise level, three parameters are varied in data generation: a. Twenty different concentrations of nanoparticles as the analytes (Cnp): 0.013, 0.016, 0.020, 0.025, 0.032, 0.040, 0.050, 0.063, 0.080, 0.1, 0.13, 0.16, 0.20, 0.25, 0.32, 0.40, 0.50, 0.63, 0.80, and 1, with the unit of nano-molar, [nM]. b. Fifteen different diameters of the nanoparticles (Dnp): 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and 17, with the unit of nanometer, [nm]. c. Five different translocation durations (Duration): 0.5, 1.0, 1.5, 3.0, and 5.0, with the unit of millisecond, [ms]. In total we have 20*15*5=1500 current traces for each SNR. There are three datasets: training, validation and test. Traces in training datasets are of 20 seconds, traces in validation and test datasets are 10 seconds long. For SNR = 4, training, validation and test datasets are provided. For other SNRs, only test traces are provided. <strong>Pert II</strong>: This part contains real experimental datasets, the translocation of Lambda DNA and Streptavidin with 6 current traces each at different bias voltages. Each Lambda DNA trace has 71 seconds, while each Streptavidin trace has 126 seconds. Two truncated pyramid shape nanopores were used in our experiments, one with a side length of 7.5 nm and another 16 nm, both in a 55 nm-thick silicon layer, for DNA and protein streptavidin translocation, respectively. The DNA and streptavidin were dispersed in 500 mM KCl electrolyte with a concentration of 78 pM and 84 nM, respectively. <br> Details of artificially generated data and experimental data can be found in our paper: Dario Dematties, Chenyu Wen, Mauricio David Pérez, Dian Zhou, Shi-Li Zhang. Deep learning of nanopore sensing signals using a bi-path network. arXiv:2105.03660. <strong>Trained and Validated Models</strong> In this data set we also include all the trained and best validated models evaluated in our paper. <strong>Nanopore Translocation Detector Trained and Validated Models </strong> In this data set we also include all the trained and best validated models evaluated in our newer paper: <em>A Generalized Transformer-Based Pulse Detection Algorithm</em>
description: The Artificial data set is compressed and split into "n" .zip files. Once you downloaded such files you can use the following sequence of commands for joining and unzipping them: zip -F Artificial_Data.zip --out single-archive.zip unzip single-archive.zip
DOI: 10.5281/zenodo.6902482
title: Difference between finger prints of type 2 diabetics and healthy individuals

description: This dataset provides the fingerprint patterns of type 2 diabetic and non-diabetic patients. Careful inclusion and exclusion criteria were used to select participants so that their fingerprints could be compared for research purposes.
DOI: 10.5281/zenodo.6902481
title: Difference between finger prints of type 2 diabetics and healthy individuals

description: This dataset provides the fingerprint patterns of type 2 diabetic and non-diabetic patients. Careful inclusion and exclusion criteria were used to select participants so that their fingerprints could be compared for research purposes.
DOI: 10.6073/pasta/0cd5f1d466c665ca80f516e2bd90d0ea
title: LAGOS - Predicted and observed maximum depth values for lakes in a 17-state region of the U.S.

description: This dataset includes predicted and observed values of maximum depth for lakes in the upper Midwest and northeast United States. All observed values came from LAGOS ver 1.040.0 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 40 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, observed maximum depth values (n = 8164) were used to train and validate a predictive mixed effects model for lake depth using terrestrial and lake morphology as predictors (Oliver et al., submitted). Predicted values (n = 50 607) generated by the model had a root mean squared error of 7.1 m. This research was supported by the NSF Macrosystem Biology awards 1065786, 1065818, and 1065649.
DOI: 10.6084/m9.figshare.20369097
title: Prevalence of common risk factors of major non-communicable diseases among sexual and gender minorities in Kathmandu valley of Nepal

description: Data of the research project entitled: Prevalence of common risk factors of major non-communicable diseases among sexual and gender minorities in Kathmandu valley of Nepal
DOI: 10.6084/m9.figshare.20369097.v1
title: Prevalence of common risk factors of major non-communicable diseases among sexual and gender minorities in Kathmandu valley of Nepal

description: Data of the research project entitled: Prevalence of common risk factors of major non-communicable diseases among sexual and gender minorities in Kathmandu valley of Nepal
DOI: 10.6073/pasta/1201e43cf21718c300db9777f89e09f8
title: Chlorophyll determined by extraction of samples taken approximately weekly from seawater intake starting at Palmer Station by station personnel including during winter-over period, 1991 – Mar. 2019.

description: Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Chlorophyll a is determined weekly year-round at the laboratory seawater intake (SWI), from a depth of 6 meters. Concentrations are typically very low (&lt; 1 µg Chl a per liter) in winter (April-October), and higher (1-30 µg/L) following the initiation of the annual spring-summer phytoplankton bloom in November - January.
DOI: 10.6073/pasta/d63bbcd641494e6f89143d011ea9cc2a
title: Average monthly sea ice coverage for the PAL LTER region West of the Antarctic Peninsula derived from passive microwave, 1978 - Apr. 2019

description: Monthly sea ice coverage derived from passive microwave satellite measurements. Ice extent is defined as the area bounded by the 15% ice concentration contour which is determined by summing up all the pixel areas (nominally 25km by 25km) which have concentrations greater than 15%. Sea-ice extent refers to the total surface area inside the ice-edge. This area inside the ice-edge is not necessarily covered by 100%.
DOI: 10.6084/m9.figshare.20369112
title: Watson et al. Raw data for peer review.xlsx

description: The file contains the four excel sheets, which were collected from pollinator gardens: 1) Sampling collection details (i.e. sampling time, numbers of samplings per garden, day of sampling, total sampling time). 2) Plant composition in each pollinator garden (i.e. number of flowers classified at the plant family level) used for NMDS analyses and PERMANOVA. 3) Insect visitation in each pollinator garden (i.e. the rate of visits per hour for each pollinator group) used for NMDS analyses and PERMANOVA. 4) Insect visitation data in relationship with garden characteristics (i.e. explanatory variables) for GLM analyses.
DOI: 10.6084/m9.figshare.20369112.v1
title: Watson et al. Raw data for peer review.xlsx

description: The file contains the four excel sheets, which were collected from pollinator gardens: 1) Sampling collection details (i.e. sampling time, numbers of samplings per garden, day of sampling, total sampling time). 2) Plant composition in each pollinator garden (i.e. number of flowers classified at the plant family level) used for NMDS analyses and PERMANOVA. 3) Insect visitation in each pollinator garden (i.e. the rate of visits per hour for each pollinator group) used for NMDS analyses and PERMANOVA. 4) Insect visitation data in relationship with garden characteristics (i.e. explanatory variables) for GLM analyses.
DOI: 10.6084/m9.figshare.20369136
title: Datasheet PlosOne.xlsx

description: Dataset for Manuscript
DOI: 10.6084/m9.figshare.20369136.v1
title: Datasheet PlosOne.xlsx

description: Dataset for Manuscript
DOI: 10.5255/ukda-sn-855831
title: Investigating Institutional and Societal Logics Around Female Career Progression in the United Kingdom and Germany, 2019-2021

description: Using a qualitative case study approach, this project explores two institutional settings: the UK and Germany – with qualitative data collected from sites within a devolved British nation (DBN) and a German Federal State (GFS). Both countries show similarities concerning gender norms and work, e.g., traditionally fostering a predominantly male breadwinner model. The analysis incorporates data from 41 semi-structured interviews in five public sector case study organisations in the UK (three organisations, 17 interviews) and Germany (two organisations, 24 interviews) with Human Resources Managers, Line Managers, woman workers and Employee Representatives. Thematic analysis findings point towards similarities and differences across countries.
DOI: 10.6084/m9.figshare.19375850.v1
title: Raw Data.zip

description: Raw Data of the original article entitled “Altered intestinal microbiota are associated with sleep disturbances in patients with minimal hepatic encephalopathy caused by hepatitis B-related liver cirrhosis”.<br>