cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1067629
title: Relative Distance Measurement With Nsense

description: We have carried out a set of experiments with our middleware NSense installed on three devices and carried by three different individuals. The devices were Samsung S5 Galaxy, Android version 6.0.1. Each device was named User1, User2, and User3. Each experimental scene involved 2 devices (e.g., User1 to User2 or User3 to User1 readings) positioned in different locations, indoor or outdoor, on the same floor or in different floors, within and without LoS. For each scenario, the real distance has been measured horizontally or, if on different floors, vertically. LoS is 1 if the path was free of obstacles and 0 if there were obstacles on the way; SameFloor is 1 if the users are on the same floor and 0 otherwise; Outdoor is 1 if users are outdoors and 0 otherwise; the real distance between the devices, in meters. The experiments have been developed over two weeks, between 10 a.m. and 17 p.m. of different working days of the week. There are two different datasets: BTDataset.csv WiFiDataset.csv Each dataset has the following variables: - timestamp - own_device (identifier of the device) - rssi, RSSI in dB - Scale, RSSI levels (1 as Very Weak, 2 as Weak , 3 as Mild, 4 as Strong and 5 as Very Strong). RSSI deterministic levels are based upon the Android getLevel() method. - freq, corresponding to the sub-frequency of the channel - computed distance, in meters - scenario, label of the different scenarios - refer to ScenarioDescription.xls - realdistance in meters - line_of_sight. 1 for LoS; 0 for nLoS - same floor. 1 for users standing on the same floor; 0 for different floors - outdoor, 1 for both users being outdoors; 0 otherwise - connected_device, device from which the RSSI is being obtained.
DOI: 10.5281/zenodo.1067630
title: Relative Distance Measurement With Nsense

description: We have carried out a set of experiments with our middleware NSense installed on three devices and carried by three different individuals. The devices were Samsung S5 Galaxy, Android version 6.0.1. Each device was named User1, User2, and User3. Each experimental scene involved 2 devices (e.g., User1 to User2 or User3 to User1 readings) positioned in different locations, indoor or outdoor, on the same floor or in different floors, within and without LoS. For each scenario, the real distance has been measured horizontally or, if on different floors, vertically. LoS is 1 if the path was free of obstacles and 0 if there were obstacles on the way; SameFloor is 1 if the users are on the same floor and 0 otherwise; Outdoor is 1 if users are outdoors and 0 otherwise; the real distance between the devices, in meters. The experiments have been developed over two weeks, between 10 a.m. and 17 p.m. of different working days of the week. There are two different datasets: BTDataset.csv WiFiDataset.csv Each dataset has the following variables: - timestamp - own_device (identifier of the device) - rssi, RSSI in dB - Scale, RSSI levels (1 as Very Weak, 2 as Weak , 3 as Mild, 4 as Strong and 5 as Very Strong). RSSI deterministic levels are based upon the Android getLevel() method. - freq, corresponding to the sub-frequency of the channel - computed distance, in meters - scenario, label of the different scenarios - refer to ScenarioDescription.xls - realdistance in meters - line_of_sight. 1 for LoS; 0 for nLoS - same floor. 1 for users standing on the same floor; 0 for different floors - outdoor, 1 for both users being outdoors; 0 otherwise - connected_device, device from which the RSSI is being obtained.
DOI: 10.5281/zenodo.1067636
title: Nearness Sensing And Interest Traces

description: performed with psychology students, in two different institutions, where the purpose was to study interest influence in psychological proximity. NSense has been installed in Android smartphones carried by a population of 50 students of the two different universities, numbered User1 to User50. The students carried NSense around during their daily routines, for 2 days: 05.04.2017-06.04.2017. The readings obtained show a total of 15 students out of the original universe of 50. The traces collected comprise the following fields: - date: DD/MM HH:mm - own_device_name: unique identifier of the device, Userx - connected_device_name: peer observed via Wi-Fi Direct (range of 0 to 100 meters) at date. - latitude, longitude: GPS coordinates of the device - distance: relative distance computed in meters via Wi-Fi Direct. - sound: discrete value for the surrounding sound activity: ALERT, NORMAL, QUIET - physical activity: discrete value for the type of activity (MOVING or STATIONARY) - tct: total contact time. Starts counting after 1 day of use. Corresponds to the sum of contact duration between i and j on a time window of duration h. - social_strength_minute: level of social interaction derived from our work NSense: A People-centric, non-intrusive Opportunistic Sensing Tool for Contextualizing Social Interaction, IEEE Healthcom2016.Social strength of node i towards node j, in a specific hourly sample h, for day d. Node i computes the social strength towards j by adding the different weighted ADs. t has been set for 24 hours - si: Social interaction of node i towards j, computed at instant t - p: propinquity: measures the probability of social interaction occurring over time. - ema_cd. Exponential moving average of the total contact duration between nodes i and j - additional columns: types of interests defined in NSense.
DOI: 10.5258/soton/d0316
title: Spectral Peaks Measurement Sets for Dolphin Whistles

description: The purpose of the provided measurement set and the corresponding interpolated ground truth data of dolphin whistle contours, is to provide a common measurement set on which the automated algorithms for whistle contour extraction can be tested.
DOI: 10.5281/zenodo.1067635
title: Nearness Sensing And Interest Traces

description: performed with psychology students, in two different institutions, where the purpose was to study interest influence in psychological proximity. NSense has been installed in Android smartphones carried by a population of 50 students of the two different universities, numbered User1 to User50. The students carried NSense around during their daily routines, for 2 days: 05.04.2017-06.04.2017. The readings obtained show a total of 15 students out of the original universe of 50. The traces collected comprise the following fields: - date: DD/MM HH:mm - own_device_name: unique identifier of the device, Userx - connected_device_name: peer observed via Wi-Fi Direct (range of 0 to 100 meters) at date. - latitude, longitude: GPS coordinates of the device - distance: relative distance computed in meters via Wi-Fi Direct. - sound: discrete value for the surrounding sound activity: ALERT, NORMAL, QUIET - physical activity: discrete value for the type of activity (MOVING or STATIONARY) - tct: total contact time. Starts counting after 1 day of use. Corresponds to the sum of contact duration between i and j on a time window of duration h. - social_strength_minute: level of social interaction derived from our work NSense: A People-centric, non-intrusive Opportunistic Sensing Tool for Contextualizing Social Interaction, IEEE Healthcom2016.Social strength of node i towards node j, in a specific hourly sample h, for day d. Node i computes the social strength towards j by adding the different weighted ADs. t has been set for 24 hours - si: Social interaction of node i towards j, computed at instant t - p: propinquity: measures the probability of social interaction occurring over time. - ema_cd. Exponential moving average of the total contact duration between nodes i and j - additional columns: types of interests defined in NSense.
DOI: 10.5281/zenodo.1067641
title: Nsense V1.0 Data Set Ii

description: This data set comprises experiment carried out considering Nine Android devices, each named Copelabs1, 2, 3, 4, 5, 6, 7, 8 and 12, respectively. These devices carried by people sharing the same affiliation during their daily routines (commuting between home and office, going to leisure activities, attending meetings in the office). All the data was collected each and every one minute. We set up experiments making use of Samsung Galaxy S3 devices. For each experiment, there is the following set of data files: * SocialProximity.dat has three columns: Timestamp, DeviceName, Encounter Duration, Average Encounter Duration, Social Strength (Per hour) and Social Strength(Per minute) towards * DistanceOutput.dat has three columns: Timestamp, DeviceName, and Distance towards * Microphone.dat has two columns: Timestamp, and Sound level(QUIET, NORMAL, ALERT, and NOISY) * PhysicalActivity.dat has two columns: Timestamp, and Activity as STATIONARY, WALKING, and RUNNING The experiment was conducted for the period of 12 days from 12th September to 23rd September 2016 . All devices were carried by users sharing affiliation and following their individual daily routines.
DOI: 10.5281/zenodo.1067642
title: Nsense V1.0 Data Set Ii

description: This data set comprises experiment carried out considering Nine Android devices, each named Copelabs1, 2, 3, 4, 5, 6, 7, 8 and 12, respectively. These devices carried by people sharing the same affiliation during their daily routines (commuting between home and office, going to leisure activities, attending meetings in the office). All the data was collected each and every one minute. We set up experiments making use of Samsung Galaxy S3 devices. For each experiment, there is the following set of data files: * SocialProximity.dat has three columns: Timestamp, DeviceName, Encounter Duration, Average Encounter Duration, Social Strength (Per hour) and Social Strength(Per minute) towards * DistanceOutput.dat has three columns: Timestamp, DeviceName, and Distance towards * Microphone.dat has two columns: Timestamp, and Sound level(QUIET, NORMAL, ALERT, and NOISY) * PhysicalActivity.dat has two columns: Timestamp, and Activity as STATIONARY, WALKING, and RUNNING The experiment was conducted for the period of 12 days from 12th September to 23rd September 2016 . All devices were carried by users sharing affiliation and following their individual daily routines.
DOI: 10.5281/zenodo.1067646
title: Nsense Traces, Social Interaction And Propinquity Analysis

description: Data set obtained with the NSense nearness measurement tool.
DOI: 10.5281/zenodo.1067645
title: Nsense Traces, Social Interaction And Propinquity Analysis

description: Data set obtained with the NSense nearness measurement tool.
DOI: 10.5281/zenodo.1067368
title: Raw Diffraction Images Of Eukaryotic Mate Transporter (Atdtx14)

description: Multidrug And Toxic compound Extrusion (MATE) transporter exports xenobiotics by using the gradient of H<sup>+</sup>. The crystals were obtained within the lipidic cubic phase.

288+85 (Auto+manual) small-wedge (5-20°/crystal) datasets collected from loop-harvested microcrystals using MX225HS CCD detector at a wavelength of 1 Å on BL32XU, SPring-8. The crystals belonged to space group P2<sub>1</sub>2<sub>1</sub>2<sub>1</sub> with unit cell parameters a=52.8, b=86.8, c=116.4 Å.

100 datasets were merged at 2.6 Å resolution in the published result (Miyauchi et al. Nature Communications, 2017; PDB code: 5Y50) using KAMO; see processing note https://github.com/keitaroyam/yamtbx/wiki/Processing-AtDTX14-data-(5Y50)

Note that most frames have lipid rings.
description: {"references": ["https://github.com/keitaroyam/yamtbx/wiki/Processing-AtDTX14-data-(5Y50)"]}
DOI: 10.5281/zenodo.1067369
title: Raw Diffraction Images Of Eukaryotic Mate Transporter (Atdtx14)

description: Multidrug And Toxic compound Extrusion (MATE) transporter exports xenobiotics by using the gradient of H<sup>+</sup>. The crystals were obtained within the lipidic cubic phase.

288+85 (Auto+manual) small-wedge (5-20°/crystal) datasets collected from loop-harvested microcrystals using MX225HS CCD detector at a wavelength of 1 Å on BL32XU, SPring-8. The crystals belonged to space group P2<sub>1</sub>2<sub>1</sub>2<sub>1</sub> with unit cell parameters a=52.8, b=86.8, c=116.4 Å.

100 datasets were merged at 2.6 Å resolution in the published result (Miyauchi et al. Nature Communications, 2017; PDB code: 5Y50) using KAMO; see processing note https://github.com/keitaroyam/yamtbx/wiki/Processing-AtDTX14-data-(5Y50)

Note that most frames have lipid rings.
description: {"references": ["https://github.com/keitaroyam/yamtbx/wiki/Processing-AtDTX14-data-(5Y50)"]}
DOI: 10.18434/t43055
title: fipy_1b_travis

description: Serial Travis CI benchmark with FiPy, no-flux domain
DOI: 10.18434/t46p9h
title: fipy_1a_travis

description: Serial Travis CI benchmark with FiPy, periodic domain
DOI: 10.18434/t4bh0m
title: coral_6a

description: Upload-a-thon August 2017 test upload
DOI: 10.18434/t4ps6r
title: fipy_6b_macbookpro

description: Serial benchmark with FiPy on Gmsh mesh with round right
DOI: 10.18434/t4th0z
title: fipy_6a_macbookpro

description: Serial benchmark with FiPy on uniform grid
DOI: 10.18434/t4z91h
title: fipy_1c_travis

description: Serial Travis CI benchmark with FiPy, T-shape domain
DOI: 10.18434/t4k044
title: inl_moose_1b

description: Benchmark with MOOSE on macPro, no-flux domain
DOI: 10.18434/t45s7g
title: inl_moose_6a

description: Benchmark problem 6a, energy vs time. 160 x 160 uniform mesh, iteration adaptive time stepping, target iterations=8
DOI: 10.18434/t49g9c
title: inl_moose_1c

description: Benchmark with MOOSE on macPro, T-shaped no-flux domain
DOI: 10.18434/t4f927
title: inl_moose_1b_raspi

description: Raspberry Pi 3 benchmark with MOOSE, no-flux domain
DOI: 10.20381/ruor36968
title: Emerging Adults & Prosocial Behaviour On- and Offline

description: Poster presented at the 43rd Association for Moral Education Conference 2017, Saint Louis, MO. Social media has become integrated into the daily lives of most emerging adults as a means of general communication as well as a source of social and global information; what is less well known is how emerging adults use social media to help others. Most literature on online prosocial behaviour focuses on traditional volunteerism and charitable giving, or on how online activities breed ‘slacktivism,’ i.e., acts that have only surface features of prosocial behaviour, but are of little benefit to others (Bucy & Gregson, 2001; Kristofferson, White, and Peloza., 2014; Penny, 2014). What is unique to emerging adults is that they, unlike previous generations, have ‘grown up with the Internet’ and they may engage in and express prosociality through less traditional actions and behaviours, such as, participation in online forums and communities, petitions, and advocacy. This exploratory study aims to examine the various forms of prosocial and civic participation emerging adults may undertake in online contexts and its relation to more traditional forms of service and volunteering. Looking at data from approximately 100 undergraduate students, including those engaged in traditional service and volunteering, and others who are not, this study will examine how emerging adults express prosociality online and attempt to categorize these activities, and explore their relation to current levels of service and volunteering.
DOI: 10.18434/t4204g
title: inl_moose_6b

description: Benchmark problem 6b, gmsh triangular elements with element target length 1, Adaptive time stepping with IterationAdaptive, target iterations = 8
DOI: 10.18434/t4j056
title: mmsp_1b_raspi

description: Raspberry Pi 3 cluster benchmark with MMSP, no-flux domain
DOI: 10.18434/t4ns7t
title: mmsp_1a_wrksttn

description: MPI parallel workstation benchmark with MMSP, periodic domain
DOI: 10.18434/t4sh0n
title: mmsp_1a_travis

description: Parallel Travis CI benchmark with MMSP, periodic domain
DOI: 10.18434/t4x92k
title: mmsp_1a_raspi

description: Raspberry Pi 3 cluster benchmark with MMSP, periodic domain
DOI: 10.5281/zenodo.1067832
title: Refuges_And_Predator_Overfishing_Vulnerability

description: Model code and data to accompany paper: High refuge availability on coral reefs increases the vulnerability of reef-associated predators to overexploitation
DOI: 10.18434/t4133t
title: mmsp_1c_travis

description: Parallel Travis CI benchmark with MMSP, T-shape domain
DOI: 10.18434/t44s75
title: mmsp_1c_raspi

description: Raspberry Pi 3 cluster benchmark with MMSP, T-shape domain
DOI: 10.18434/t48h00
title: mmsp_1b_wrksttn

description: MPI parallel workstation benchmark with MMSP, no-flux domain
DOI: 10.18434/t4d92x
title: mmsp_1b_travis

description: Parallel Travis CI benchmark with MMSP, no-flux domain
DOI: 10.18434/t4w928
title: mmsp_1c_wrksttn

description: MPI parallel workstation benchmark with MMSP, T-shape domain
DOI: 10.5281/zenodo.1067831
title: Refuges_And_Predator_Overfishing_Vulnerability

description: Model code and data to accompany paper: High refuge availability on coral reefs increases the vulnerability of reef-associated predators to overexploitation
DOI: 10.17632/4mbht66zw6.1
title: Data for: Environmental disturbance, resource availability, and biologic turnover at the dawn of animal life

description: Dataset accompanying manuscript by Muscente et al.
DOI: 10.18434/t43s6g
title: moose_1d_ia

description: Information for the Benchmark1 problems
DOI: 10.18434/t47k9s
title: moose_1c_ia

description: Information for the Benchmark1 problems
DOI: 10.18434/t4c917
title: moose_1b_sta

description: Information for the Benchmark1 problems
DOI: 10.18434/t4h34j
title: moose_1b_ia

description: Information for the Benchmark1 problems
DOI: 10.18434/t4ms7h
title: moose_1a_ia

description: Metadata for the Benchmark1 problems
DOI: 10.18434/t4rk82
title: mmsp_6a_wrksttn

description: MMSP 12-core workstation, red-black Gauss-Seidel solver, dx=1.0, dt=0.05, CFL=240
DOI: 10.18434/t4034w
title: moose_1d_sta

description: Information for the Benchmark1 problems
DOI: 10.18434/t46k9g
title: moose_2c_sta

description: Metadata for the Benchmark2 problems
DOI: 10.18434/t4g347
title: moose_2b_sta

description: Metadata for the Benchmark2 problems
DOI: 10.18434/t4ks76
title: moose_2b_ia

description: Metadata for the Benchmark2 problems
DOI: 10.18434/t4qk94
title: moose_2a_sta

description: Metadata for the Benchmark2 problems
DOI: 10.18434/t4v91k
title: moose_2a_ia

description: Metadata for the Benchmark2 problems
DOI: 10.5258/soton/d0315
title: Adaptive Coding and Modulation for Large-Scale Antenna Array Based Aeronautical Communications in the Presence of Co-channel Interference

description: This dataset supports the publication:
Zhang, Jiankang; Chen, Sheng; Maunder, Robert; Zhang, Rong; Hanzo, Lajos.
Adaptive Coding and Modulation for Large-Scale Antenna Array Based Aeronautical Communications in the Presence of Co-channel Interference.
IEEE Transactions on Wireless CommunicationsThis dataset contains which are used for generating Fig.3 to Fig.9. These figures are plotted using GLE (Graphics Layout Engine). The scripts of Gle are also included in the folds for each figures. In order to generate these figures, you should install Gle
http://glx.sourceforge.net/
DOI: 10.17632/4mbht66zw6.2
title: Data for: Environmental disturbance, resource availability, and biologic turnover at the dawn of animal life

description: Dataset accompanying manuscript by Muscente et al.
DOI: 10.18434/t42w5t
title: moose_2d_ia

description: Metadata for the Benchmark2 problems
DOI: 10.18434/t4b929
title: moose_2c_ia

description: Metadata for the Benchmark2 problems
DOI: 10.18434/t4f33j
title: moose_psu_1b_STA

description: Metadata for the Benchmark1 problems
DOI: 10.18434/t4jw55
title: moose_psu_1b_IA

description: Metadata for the Benchmark1 problems
DOI: 10.18434/t4pk9t
title: moose_psu_1a_STA

description: Metadata for the Benchmark1 problems
DOI: 10.18434/t4td0x
title: moose_psu_1a_IA

description: Metadata for the Benchmark1 problems
DOI: 10.18434/t4z34k
title: moose_2d_sta

description: Metadata for the Benchmark2 problems
DOI: 10.5281/zenodo.1067750
title: Summary-level results from the AFGen, GWAS and educational attainment GWAS

description: Merged summary-level results to calculate genetic correlations between various traits. The columns are as follows:


	SNP: rs identifier for the SNP
	AFGen.EUR.Z: z-score from the AFGen GWAS of atrial fibrillation performed in European-ancestry samples
	EduYrs.Z: z-score from the educational attainment GWAS (data available from LDHub)
	afib.broad.Z: z-scores from the SiGN GWAS of atrial fibrillation
	allstroke.Z: z-scores from the SiGN GWAS of all stroke 
	CCScCEmajor.Z: z-scores from the SiGN GWAS of CE stroke (defined by CCSc)
	CCScCRYPTCE.Z: z-scores from the SiGN GWAS of cryptogenic/CE minor stroke (defined by CCSc)
	CCScINCUNC.Z: z-scores from the SiGN GWAS of incomplete/unclassified stroke (defined by CCSc)
	CCScLAA.Z: z-scores from the SiGN GWAS of LAA stroke (defined by CCSc)
	CCScSAO.Z: z-scores from the SiGN GWAS of SAO stroke (defined by CCSc)
	CCScUNDETER.Z: z-scores from the SiGN GWAS of undetermined stroke (defined by CCSc)
	CCSpCEmajincl.Z: z-scores from the SiGN GWAS of CE stroke (defined by CCSp)
	CCSpCryptoincl.Z: z-scores from the SiGN GWAS of cryptogenic stroke (defined by CCSp) 
	CCSpLAAmajincl.Z: z-scores from the SiGN GWAS of LAA stroke (defined by CCSp)
	CCSpSAOmajincl.Z: z-scores from the SiGN GWAS of SAO stroke (defined by CCSp)
	toastCE.Z: z-scores from the SiGN GWAS of CE stroke (defined by TOAST)
	toastLAA.Z: z-scores from the SiGN GWAS of LAA stroke (defined by TOAST)
	toastSAO.Z: z-scores from the SiGN GWAS of SAO stroke (defined by TOAST)
	toastUNDETER.Z: z-scores from the SiGN GWAS of undetermined stroke (defined by TOAST)


CCSc, CCS Causative subtyping system; CCSp, CCS Phenotypic subtyping system; TOAST, TOAST subtyping system; CE, cardioembolic stroke; LAA, large artery atherosclerosis; SAO, small artery occlusion.
DOI: 10.5281/zenodo.1067751
title: Summary-level results from the AFGen, GWAS and educational attainment GWAS

description: Merged summary-level results to calculate genetic correlations between various traits. The columns are as follows:


	SNP: rs identifier for the SNP
	AFGen.EUR.Z: z-score from the AFGen GWAS of atrial fibrillation performed in European-ancestry samples
	EduYrs.Z: z-score from the educational attainment GWAS (data available from LDHub)
	afib.broad.Z: z-scores from the SiGN GWAS of atrial fibrillation
	allstroke.Z: z-scores from the SiGN GWAS of all stroke 
	CCScCEmajor.Z: z-scores from the SiGN GWAS of CE stroke (defined by CCSc)
	CCScCRYPTCE.Z: z-scores from the SiGN GWAS of cryptogenic/CE minor stroke (defined by CCSc)
	CCScINCUNC.Z: z-scores from the SiGN GWAS of incomplete/unclassified stroke (defined by CCSc)
	CCScLAA.Z: z-scores from the SiGN GWAS of LAA stroke (defined by CCSc)
	CCScSAO.Z: z-scores from the SiGN GWAS of SAO stroke (defined by CCSc)
	CCScUNDETER.Z: z-scores from the SiGN GWAS of undetermined stroke (defined by CCSc)
	CCSpCEmajincl.Z: z-scores from the SiGN GWAS of CE stroke (defined by CCSp)
	CCSpCryptoincl.Z: z-scores from the SiGN GWAS of cryptogenic stroke (defined by CCSp) 
	CCSpLAAmajincl.Z: z-scores from the SiGN GWAS of LAA stroke (defined by CCSp)
	CCSpSAOmajincl.Z: z-scores from the SiGN GWAS of SAO stroke (defined by CCSp)
	toastCE.Z: z-scores from the SiGN GWAS of CE stroke (defined by TOAST)
	toastLAA.Z: z-scores from the SiGN GWAS of LAA stroke (defined by TOAST)
	toastSAO.Z: z-scores from the SiGN GWAS of SAO stroke (defined by TOAST)
	toastUNDETER.Z: z-scores from the SiGN GWAS of undetermined stroke (defined by TOAST)


CCSc, CCS Causative subtyping system; CCSp, CCS Phenotypic subtyping system; TOAST, TOAST subtyping system; CE, cardioembolic stroke; LAA, large artery atherosclerosis; SAO, small artery occlusion.
DOI: 10.15785/sbgrid/529
title: X-Ray Diffraction data from Human CASTOR1, Arg-bound, source of 5I2C structure

description: Native dataset
DOI: 10.18434/t41w6w
title: moose_psu_1d_IA_2

description: Metadata for the Benchmark1 problems
DOI: 10.18434/t45k8s
title: moose_psu_1c_STA

description: Metadata for the Benchmark1 problems
DOI: 10.18434/t49d1n
title: moose_psu_1c_IA

description: Metadata for the Benchmark1 problems