cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7724135
title: Octopus: A Novel Approach for Health Data Masking and Retrieving using Physically Unclonable Function and Machine Learning

description: The health equipment is used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because all devices are linked to high-speed internet and phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this proposed paper - Octopus, Physically Unclonable Functions (PUFs) have been used to provide privacy to the healthcare device by masking the data, and machine learning (ML) techniques are used to retrieve the health data back and reduce security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.
DOI: 10.5281/zenodo.7724134
title: Octopus: A Novel Approach for Health Data Masking and Retrieving using Physically Unclonable Function and Machine Learning

description: The health equipment is used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because all devices are linked to high-speed internet and phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this proposed paper - Octopus, Physically Unclonable Functions (PUFs) have been used to provide privacy to the healthcare device by masking the data, and machine learning (ML) techniques are used to retrieve the health data back and reduce security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.
DOI: 10.6084/m9.figshare.22256293
title: MEME Analysis of TLR4 Sequences of Cetacea and Artiodactyla

description: TLR4 Sites Under Positive Selection in Mammals. DataMonkey was used to perform a MEME Analysis of the TLR4 sequences of ten species, including four Cetacea and six Artiodactyla. A high dN/dS ratio (&gt;1) was found on five sites on the TLR4 of the species, indicating positive selection on their TLR4.
DOI: 10.5281/zenodo.7734008
title: A Note on Aerosol Processing by Droplet Collision-Coalescence

description: Simulation results for the above-mentioned publication. The files *_out.nc contain spectral data, *_out_time.nc time series. The prefixes small, medium, and large refer to the initial aerosol size distribution.
DOI: 10.5281/zenodo.7734007
title: A Note on Aerosol Processing by Droplet Collision-Coalescence

description: Simulation results for the above-mentioned publication. The files *_out.nc contain spectral data, *_out_time.nc time series. The prefixes small, medium, and large refer to the initial aerosol size distribution.
DOI: 10.6084/m9.figshare.21989732.v1
title: pch2 data

description: Data from <em>pch2-1 cells for</em> Coarsening dynamics can explain meiotic crossover patterning in both the presence and absence of the synaptonemal complex
DOI: 10.5519/qd.xdfzqx30
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.34740/kaggle/dsv/5166176
title: BERBER-MNIST dataset
DOI: 10.6084/m9.figshare.21610200
title: Insect species richness and biomass in grasslands - R-code

description: This zip-file contains data and R-Scripts to reproduce model results as shown in the article "<strong>Insects benefit from agri-environmental schemes aiming at grassland extensification</strong>" by Bernd Panassiti, Sebastian Wolfrum, Sabine Birnbeck, Johannes Burmeister, Annette Freibauer, Jerome Moriniere, and Roswitha Walter. The dataset provided as a .RData-file contains insect and biomass data. The dataset is a result of the project "<em>Evaluation of agri-environmental measures on insect diversity and biomass</em>" carried out by the Bavarian State Research Center for Agriculture (LfL) during 2019-2023. Insect data was collected using Malaise traps in 4 grassland regions in Bavaria, Germany, during 3 sample periods in 2019 (regions Bavarian Forest, eastern Allgäu) and 2020 (regions (Chiemgau uplands, Upper Palatinate Forest). Insect data comprise insect biomass and the species richness of 8 insect groups: Total number of insect species Coleoptera Diptera Hymenoptera Lepidoptera Hemiptera Syrphidae (hover flies) Apiformes (wild bees and honeybees)
DOI: 10.6084/m9.figshare.21610200.v1
title: Insect species richness and biomass in grasslands - R-code

description: This zip-file contains data and R-Scripts to reproduce model results as shown in in the article "Metabarcoding of grassland insects reveals effectiveness of site-specific agri-environmental measures" by Bernd Panassiti, Sebastian Wolfrum, Sabine Birnbeck, Johannes Burmeister, Annette Freibauer, Jerome Moriniere, and Roswitha Walter. The dataset provided as a .RData-file contains insect and biomass data. The dataset is a result of the project "Evaluation of agri-environmental measures on insect diversity and biomass" carried out by the Bavarian State Research Center for Agriculture (LfL) during 2019-2022. Insect data was collected using Malaise traps in 4 grassland regions in Bavaria, Germany, during 3 sample periods in 2019 (regions Bavarian Forest, eastern Allgäu) and 2020 (regions (Chiemgau uplands, Upper Palatinate Forest). Insect data comprises the species richness of 9 insect groups: Total number of insect species Coleoptera Diptera Hymenoptera Lepidoptera Hemiptera Syrphidae (hover flies) Apiformes (wild bees and honeybees) Papilionoidea (butterflies)
DOI: 10.6084/m9.figshare.21608679
title: Insect species richness and biomass in grasslands

description: Excel-file contains insect and biomass data and environmental predictors as used as model input in the article "<strong>Insects benefit from agri-environmental schemes aiming at grassland extensification</strong>" by Bernd Panassiti, Sebastian Wolfrum, Sabine Birnbeck, Johannes Burmeister, Annette Freibauer, Jerome Moriniere, and Roswitha Walter. The dataset is a result of the project "<em>Evaluation of agri-environmental measures on insect diversity and biomass</em>" carried out by the Bavarian State Research Center for Agriculture (LfL) during 2019-2023. Insect data was collected using Malaise traps in 4 grassland regions in Bavaria, Germany, during 3 sample periods in 2019 (regions Bavarian Forest, eastern Allgäu) and 2020 (regions (Chiemgau uplands, Upper Palatinate Forest). Insect data comprise insect biomass and the species richness of 8 insect groups: Total number of insect species Coleoptera Diptera Hymenoptera Lepidoptera Hemiptera Syrphidae (hover flies) Apiformes (wild bees and honeybees)
DOI: 10.6084/m9.figshare.21608679.v2
title: Insect species richness and biomass in grasslands

description: Excel-file contains insect and biomass data and environmental predictors as used as model input in the article "<strong>Insects benefit from agri-environmental schemes aiming at grassland extensification</strong>" by Bernd Panassiti, Sebastian Wolfrum, Sabine Birnbeck, Johannes Burmeister, Annette Freibauer, Jerome Moriniere, and Roswitha Walter. The dataset is a result of the project "<em>Evaluation of agri-environmental measures on insect diversity and biomass</em>" carried out by the Bavarian State Research Center for Agriculture (LfL) during 2019-2023. Insect data was collected using Malaise traps in 4 grassland regions in Bavaria, Germany, during 3 sample periods in 2019 (regions Bavarian Forest, eastern Allgäu) and 2020 (regions (Chiemgau uplands, Upper Palatinate Forest). Insect data comprise insect biomass and the species richness of 8 insect groups: Total number of insect species Coleoptera Diptera Hymenoptera Lepidoptera Hemiptera Syrphidae (hover flies) Apiformes (wild bees and honeybees)
DOI: 10.6084/m9.figshare.21608679.v1
title: Insect species richness and biomass in grasslands

description: The dataset provided as an Excel-file contains insect and biomass data. The dataset is a result of the project "Evaluation of agri-environmental measures on insect diversity and biomass" carried out by the Bavarian State Research Center for Agriculture (LfL) during 2019-2022. Insect data was collected using Malaise traps in 4 grassland regions in Bavaria, Germany, during 3 sample periods in 2019 (regions Bavarian Forest, eastern Allgäu) and 2020 (regions (Chiemgau uplands, Upper Palatinate Forest). Insect data comprises the species richness of 9 insect groups: Total number of insect species Coleoptera Diptera Hymenoptera Lepidoptera Hemiptera Syrphidae (hover flies) Apiformes (wild bees and honeybees) Papilionoidea (butterflies)
DOI: 10.5281/zenodo.7733648
title: Mechanistic models project bird invasions with accuracy

description: This ZIP file contains all 1) input data [occurrence data and environmental predictors] used, 2) all R scripts used, and 3) all model outputs.
DOI: 10.5281/zenodo.7733647
title: Mechanistic models project bird invasions with accuracy

description: This ZIP file contains all 1) input data [occurrence data and environmental predictors] used, 2) all R scripts used, and 3) all model outputs.
DOI: 10.21227/kqdk-w748
title: Acoustic signals from the knee

description: The dataset consists of acoustic signals acquired from the surface of the knee of 14 subjects. The description of study group and methodology of the experiment can be found in the publication: https://doi.org/10.3390/s21196495.
DOI: 10.3886/e185941
title: Class exercise: Predicting income mobility in PSID

description: This repository contains data for a data science class exercise.<br><br><b>Students</b>: This exercise is about income mobility over three generations: grandparents (g1), parents (g2), and children (g3). Your task is to predict log income in generation 3 using data on log incomes in generations 1 and 2. Additional predictors available include education in each generation, race as reported by the grandparent (g1), and sex of the respondent in g3.<br><br>The data you will use are in for_students.zip.<br>learning.csv contains 1,365 observations for which the outcome g3_log_income is recordedholdout_public.csv contains 1,365 observations for which the outcome g3_log_income is NAYour task is to build a predictive model using learning.csv. Then, make predictions for the cases in holdout_public.csv.<br><br>Here are some details about the variables in the data. All cases are from the cross-sectional Survey Research Sample of the PSID. In each generation, we took each respondent's annual income over several surveys from age 30 to 45, adjusted to 2022 dollars, and took the average. We truncated the data to the range from $5,000 to $448,501.10, where the bottom code is arbitrary and the top code is what we believe to be the lowest PSID top code over the series (in 1978), converted to 2022 dollars. Education is the first report at ages 30-45, coded as less than high school, high school, some college, or 4+ years of college. We merged the data together across generations using the PSID Family Identification Mapping System 3-generation prospective linkage file. See for_replication.zip for code to produce these data as well as a log file noting sample restrictions.<br><br>We are trusting the students to not open the instructor data, which contains the outcomes you are trying to predict. You could peek of course, but that would be no fun! We are trusting you not to peek.<br><br><b>Instructors</b>: The file for_instructors.zip contains the true holdout outcomes in holdout_private.csv. You can use these to evaluate students' predictive performance (as long as you trust that they have not peeked).<br><br><b>For those replicating: </b>The file for_replication.zip contains the directory structure and code that produced this exercise from raw files downloaded from the PSID.<br>
DOI: 10.3886/e185941v1
title: Class exercise: Predicting income mobility in PSID

description: This repository contains data for a data science class exercise.<br><br><b>Students</b>: This exercise is about income mobility over three generations: grandparents (g1), parents (g2), and children (g3). Your task is to predict log income in generation 3 using data on log incomes in generations 1 and 2.<br><br>The data you will use are in for_students.zip.<br>learning.csv contains 2,260 observations for which the outcome is recordedholdout_public.csv contains 2,260 observations for which the outcome is NAYour task is to build a predictive model using learning.csv. Then, make predictions for the cases in holdout_public.csv.<br><br>Here are some details about the variables in the data: In each generation, we took each respondent's annual income over several surveys from age 30 to 45, adjusted to 2022 dollars, and took the average. We truncated the data to the range from $5,000 to $448,501.10, where the bottom code is arbitrary and the top code is what we believe to be the lowest PSID top code over the series (in 1978), converted to 2022 dollars. We merged the data together across generations using the PSID Family Identification Mapping System 3-generation prospective linkage file.<br><br>We are trusting the students to not open the instructor data, which contains the outcomes you are trying to predict. You could peek of course, but that would be no fun! We are trusting you not to peek.<br><br><b>Instructors</b>: For you, the file for_instructors.zip contains the true holdout outcomes in holdout_private.csv. You can use these to evaluate students' predictive performance (as long as you trust that they have not peeked).<br><br><b>For those replicating: </b>For you, the file for_replication.zip contains the directory structure and code that produced this exercise from raw files downloaded from the PSID.<br>
DOI: 10.3886/e185941v2
title: Class exercise: Predicting income mobility in PSID

description: This repository contains data for a data science class exercise.<br><br><b>Students</b>: This exercise is about income mobility over three generations: grandparents (g1), parents (g2), and children (g3). Your task is to predict log income in generation 3 using data on log incomes in generations 1 and 2. Additional predictors available include education in each generation, race as reported by the grandparent (g1), and sex of the respondent in g3.<br><br>The data you will use are in for_students.zip.<br>learning.csv contains 1,365 observations for which the outcome g3_log_income is recordedholdout_public.csv contains 1,365 observations for which the outcome g3_log_income is NAYour task is to build a predictive model using learning.csv. Then, make predictions for the cases in holdout_public.csv.<br><br>Here are some details about the variables in the data. All cases are from the cross-sectional Survey Research Sample of the PSID. In each generation, we took each respondent's annual income over several surveys from age 30 to 45, adjusted to 2022 dollars, and took the average. We truncated the data to the range from $5,000 to $448,501.10, where the bottom code is arbitrary and the top code is what we believe to be the lowest PSID top code over the series (in 1978), converted to 2022 dollars. Education is the first report at ages 30-45, coded as less than high school, high school, some college, or 4+ years of college. We merged the data together across generations using the PSID Family Identification Mapping System 3-generation prospective linkage file. See for_replication.zip for code to produce these data as well as a log file noting sample restrictions.<br><br>We are trusting the students to not open the instructor data, which contains the outcomes you are trying to predict. You could peek of course, but that would be no fun! We are trusting you not to peek.<br><br><b>Instructors</b>: The file for_instructors.zip contains the true holdout outcomes in holdout_private.csv. You can use these to evaluate students' predictive performance (as long as you trust that they have not peeked).<br><br><b>For those replicating: </b>The file for_replication.zip contains the directory structure and code that produced this exercise from raw files downloaded from the PSID.<br>
DOI: 10.25919/8kzv-db91
title: Parkes observations for project P574 semester 2022OCTS_08

description: We request time to observe 260 pulsars on a regular basis in order to provide the accurate ephemerides necessary for the detection and characterisation of gamma-ray pulsars with the Fermi satellite. The main science goals are to increase the number of known gamma-ray pulsars (both radio loud and radio quiet), to determine accurate pulse profiles, and to characterise their high energy (phase-resolved) spectra. In the radio, the observations will also allow us to investigate: neutron stars and pulsar emission via detecting glitches and determining pulse timing parameters; and the ISM via measuring and monitoring dispersion measures, Faraday rotation measures, and scintillation. To date, we are (co-)authors on over 95 papers arising from the collaboration and P574 data, including four which use UWL observations. The data have contributed to the PhD theses of students from Bordeaux, Manchester, Oxford, Stanford, and Swinburne.
DOI: 10.25919/tngf-ma46
title: Parkes observations for project P1021 semester 2022OCTS_10

description: We propose to observe PSR J1812-15 and PSR J1831-04, a pair of long spin-period binary pulsars which both show degrees of orbitally-dependent variability. Binary pulsars are valuable objects of scientific study, allowing for multiple applications including tests of gravity, probes of the neutron star equation of state, and fossil records of stellar evolution. Long spin-period pulsars in binary systems are generally much rarer than faster-spinning `recycled’ pulsars, and represent an underexplored region of pulsar binary evolution. This is particularly true of PSR J1812-15, for which only one other pulsar (B1718-19) seems remotely comparable, while PSR J1831-04 may yet turn out to be a third member of this class Our objectives differ for each pulsar. For PSR J1812-15, a minimal low-cadence timing campaign is still required to address ongoing problems with phase connection, which may be caused by an undiagnosed glitch or other un-modelled timing effect. Beyond this, the utter uniqueness of the source warrants further monitoring in the event it displays further undocumented behaviour. For PSR J1831-04 (to date detected only once), our campaign is designed to provide the optimal chance of redetecting the pulsar through a randomised observing schedule, thereby characterising the pulsar and its binary system. If confirmed as similar to PSRs J1812 and B1718, it would contribute significantly to our understanding of this rare class of binary pulsar.
DOI: 10.34740/kaggle/ds/3002436
title: Telecom-bigdata
DOI: 10.5281/zenodo.7499382
title: Trajectory files for "Where does the energy go during the interstellar NH3 formation on water ice? A computational study"

description: CP2K trajectory files for the N+H, NH + H and NH2 + H reactions on the amorphous water ice surface
DOI: 10.5281/zenodo.7499383
title: Trajectory files for "Where does the energy go during the interstellar NH3 formation on water ice? A computational study"

description: CP2K trajectory files for the N+H, NH + H and NH2 + H reactions on the amorphous water ice surface
DOI: 10.7924/r4q52sj36
title: Multi-region local field potential recordings during a tail-suspension test

description: We collected a dataset of LFPs recorded from 26 mice from two different genetic backgrounds (14 wild type, 12 CLOCK Delta-19). The CLOCK Delta-19 line of mice have been proposed as a model of bipolar disorder . There are 20 minutes of recordings for each mouse: 5 minutes while the mouse was in its home cage, 5 minutes during open field exploration, and 10 minutes during a tail suspension test. The tail suspension test is used as an assay of response to a challenging experience. Eleven distinct brain regions were recorded: Nucleus Accumbens Core, Nucleus Accumbens Shell, Basolateral Amygdala, Infralimbic Cortex, Mediodorsal Thalamus, Prelimbic Cortex, Ventral Tegmental Area, Lateral Dorsal Hippocampus, Lateral Substantia Nigra Pars Compacta, Medial Dorsal Hippocampus, and Medial Substantia Nigra Pars Compacta.
DOI: 10.5281/zenodo.7541887
title: Diffusion jump structures for H, N and O atoms

description: Structures of minima and TSs or directly entire minimum energy paths for diffusion jumps of H, N and O atoms on water clusters.
DOI: 10.5281/zenodo.7541886
title: Diffusion jump structures for H, N and O atoms

description: Structures of minima and TSs or directly entire minimum energy paths for diffusion jumps of H, N and O atoms on water clusters.
DOI: 10.5281/zenodo.7495558
title: Acetaldehyde binding energies: a coupled experimental and theoretical study

description: CRYSTAL17 output files of the atomic structures used in the related publication
DOI: 10.5281/zenodo.7495559
title: Acetaldehyde binding energies: a coupled experimental and theoretical study

description: CRYSTAL17 output files of the atomic structures used in the related publication
DOI: 10.25573/data.10304303.v13
title: Monthly Rain_ACP_Horizontal.xlsx

description: Monthly summaries of the Panama Canal Authority meteorological monitoring program. These data were collected from approximately 50 stations located in the Panama Canal watershed. Information about station locations can be found in the metadata in the file.<br><br>This file has been formatted horizontally so that monthly and seasonal patterns can be easily analyzed.<br><br>Published with permission from the Panama Canal Authority.
DOI: 10.5281/zenodo.7115983
title: Supporting Information for "Where does the energy go during the interstellar NH3 formation on water ice? A computational study"

description: Supplementary Material consisting of: The energetics of the gas-phase reactions for both the H-additions and H-abstractions, The evolution with time of the total, potential and kinetic energies of the studied processes Results of the NVE AIMD simulations for the NH<sub>3</sub> formation from Pos2
DOI: 10.5281/zenodo.7115984
title: Supporting Information for "Where does the energy go during the interstellar NH3 formation on water ice? A computational study"

description: Supplementary Material consisting of: The energetics of the gas-phase reactions for both the H-additions and H-abstractions, The evolution with time of the total, potential and kinetic energies of the studied processes Results of the NVE AIMD simulations for the NH<sub>3</sub> formation from Pos2
DOI: 10.25573/data.10304303.v14
title: Monthly Rain_ACP_Horizontal.xlsx

description: Monthly summaries of the Panama Canal Authority meteorological monitoring program. These data were collected from approximately 50 stations located in the Panama Canal watershed. Information about station locations can be found in the metadata in the file.<br><br>This file has been formatted horizontally so that monthly and seasonal patterns can be easily analyzed.<br><br>Published with permission from the Panama Canal Authority.