cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14469/hpc/6734
title: Lukas mechanism, +ve system

description: Gaussian Calculation
DOI: 10.6084/m9.figshare.11664015.v11
title: Simulated significant wave height (3-hourly, 150a) from FIO-ESM v2.0 1pctCO2 experiment r1i1p1f1

description: Simulated significant wave height (3-hourly, 150a) from FIO-ESM v2.0 1pctCO2 experiment (CO2 prescribed to increase at 1% per year) r1i1p1f1, which is branch from Jan 1, 301 of piControl experiment. The grid, named gx1v6, is the displaced pole (the North Pole displaced to the Greenland) grid with 1.1 degree in longitude and 0.27-0.54 degree in latitude.
DOI: 10.34725/dvn/qrlzqh/4zef5j
title: 4.NH_Product.tab
DOI: 10.34725/dvn/qrlzqh/6z4bo4
title: 4.NH_Forest.tab
DOI: 10.34725/dvn/qrlzqh/g95nxq
title: 4.NH_Association.tab
DOI: 10.34725/dvn/qrlzqh/rceg0o
title: 4.NH_Settlement.tab
DOI: 10.5281/zenodo.3626184
title: Crowd-Annotation Results: Identifying and Classifying User Requirements in Online Feedback

description: Results from the Figure Eight experiment conducted as part of the paper "Identifying and Classifying User Requirements in Online Feedback via Crowdsourcing" published at REFSQ 2020.
description: This version fixes the original one by fixing the delimiters for strings in the CSV, therefore allowing to import into Excel.
description: {"references": ["van Vliet, Martijn et al. (2020). Identifying and Classifying User Requirements in Online Feedback via Crowdsourcing, Proc. of REFSQ 2020"]}
DOI: 10.5281/zenodo.3626185
title: Crowd-Annotation Results: Identifying and Classifying User Requirements in Online Feedback

description: Results from the Figure Eight experiment conducted as part of the paper "Identifying and Classifying User Requirements in Online Feedback via Crowdsourcing" published at REFSQ 2020.
description: {"references": ["van Vliet, Martijn et al. (2020). Identifying and Classifying User Requirements in Online Feedback via Crowdsourcing, Proc. of REFSQ 2020"]}
DOI: 10.17037/data.00001529
title: Data for "Where are patients missed in the tuberculosis diagnostic cascade? A prospective cohort study in Ghana"

description: Dataset produced as part of a study to identify gaps in the tuberculosis (TB) diagnostic cascade prior to starting treatment conducted in urban and rural health facilities in south-east Ghana among presumptive TB patients routinely identified by a health worker as requiring a sputum TB test. The dataset contains 428 records with 30 variables. Each row refers to a study participant and contains variables of the type of health facility attended, age, educational level, place of residence, socioeconomic status, symptoms of TB (cough, fever, night sweat and weight loss), duration of symptoms, number of symptoms, HIV status, history of diabetes, body mass index, Karnofsky score, ever treated for TB, stigma and distance from residence to health facility. There are also variables on whether a sputum sample was submitted for the test, results received, test results, started TB treatment, duration from test request to sputum submission, reason for not submitting a sputum sample, number of health care providers visited included for each participant.
description: The study was a prospective observational cohort study among adults aged ≥18 years with or without symptoms suggesting TB, self-presenting at the health facility, who were identified routinely by a health worker as needing TB investigation and given a laboratory request to do a sputum test or referred to the municipal hospital. Eligible patients were consecutively invited from four rural health centres and the municipal hospital. Trained research assistants conducted face-to-face interviews with consenting patients using a standardised questionnaire. Data was collected electronically using Open Data Kit (ODK) and uploaded onto a secured server hosted by the London School of Hygiene & Tropical Medicine.
DOI: 10.5281/zenodo.3626691
title: Training dataset used in the magazine paper entitled "A Flexible Machine Learning-Aware Architecture for Future WLANs"

description: <strong>A Flexible Machine Learning-Aware Architecture for Future WLANs</strong> <strong>Authors: </strong>Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta, Cristina Cano, Anders Jonsson & Vishnu Ram. <strong>Abstract: </strong>Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs. <strong>Dataset description: </strong>This is the dataset generated for training a Neural Network (NN) in the Access Point (AP) (re)association problem in IEEE 802.11 Wireless Local Area Networks (WLANs). In particular, the NN is meant to output a prediction function of the throughput that a given station (STA) can obtain from a given Access Point (AP) after association. The features included in the dataset are: Identifier of the AP to which the STA has been associated. RSSI obtained from the AP to which the STA has been associated. Data rate in bits per second (bps) that the STA is allowed to use for the selected AP. Load in packets per second (pkt/s) that the STA generates. Percentage of data that the AP is able to serve before the user association is done. Amount of traffic load in pkt/s handled by the AP before the user association is done. Airtime in % that the AP enjoys before the user association is done. Throughput in pkt/s that the STA receives after the user association is done. The dataset has been generated through random simulations, based on the model provided in https://github.com/toniadame/WiFi_AP_Selection_Framework. More details regarding the dataset generation have been provided in https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans.
DOI: 10.5281/zenodo.3626690
title: Training dataset used in the magazine paper entitled "A Flexible Machine Learning-Aware Architecture for Future WLANs"

description: <strong>A Flexible Machine Learning-Aware Architecture for Future WLANs</strong> <strong>Authors: </strong>Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta, Cristina Cano, Anders Jonsson & Vishnu Ram. <strong>Abstract: </strong>Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs. <strong>Dataset description: </strong>This is the dataset generated for training a Neural Network (NN) in the Access Point (AP) (re)association problem in IEEE 802.11 Wireless Local Area Networks (WLANs). In particular, the NN is meant to output a prediction function of the throughput that a given station (STA) can obtain from a given Access Point (AP) after association. The features included in the dataset are: Identifier of the AP to which the STA has been associated. RSSI obtained from the AP to which the STA has been associated. Data rate in bits per second (bps) that the STA is allowed to use for the selected AP. Load in packets per second (pkt/s) that the STA generates. Percentage of data that the AP is able to serve before the user association is done. Amount of traffic load in pkt/s handled by the AP before the user association is done. Airtime in % that the AP enjoys before the user association is done. Throughput in pkt/s that the STA receives after the user association is done. The dataset has been generated through random simulations, based on the model provided in https://github.com/toniadame/WiFi_AP_Selection_Framework. More details regarding the dataset generation have been provided in https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans.
DOI: 10.21227/f1v4-4208
title: PPG Blood Pressure

description: A dataset collection program was written to obtain information about individual basic physiology, including collecting PPG waveform signals and detecting the instantaneous arterial BP at the same time. The table file contains aggregated subjects of physiological information and disease information. The information records include an identification (ID) number, sex, age, and disease. The dataset was collected from 219 adult subjects aged 21–86 years. Males accounted for 48% of the participants. During the signal acquisition, the sampling waveform was set to a sampling rate of 1 kHz, with a 12 bit analog-to-digital conversion precision.
DOI: 10.7910/dvn/7zzkc7/chz6fv
title: METEC_equipment_groups.R

description: :unav
DOI: 10.7910/dvn/7zzkc7/p1vnch
title: METEC_plotter.R

description: :unav
DOI: 10.7910/dvn/but8ga/lfzogj
title: UCalgary_PoMELO_METEC_2020.pdf

description: Full Report
DOI: 10.7910/dvn/7zzkc7/5x6efx
title: METEC_replication_repairs.R

description: :unav
DOI: 10.7910/dvn/7zzkc7/79vw5l
title: settled_aggregate.tab

description: :unav
DOI: 10.7910/dvn/7zzkc7/hxh9gp
title: sd_quadrature.R

description: :unav
DOI: 10.7910/dvn/7zzkc7/j1ytbm
title: METEC_UCalgary_blind_statement_reporting.pdf

description: :unav
DOI: 10.7910/dvn/7zzkc7/ld7ua8
title: site_stats.tab

description: :unav
DOI: 10.7910/dvn/7zzkc7/m2qdtx
title: unit_conversions.R

description: :unav
DOI: 10.7910/dvn/7zzkc7/m4icng
title: METEC_equipment_map.tab

description: :unav
DOI: 10.7910/dvn/7zzkc7/rjk6xn
title: METEC_comparator.R

description: :unav
DOI: 10.7910/dvn/7zzkc7/6k1jde
title: METECBlindResults_reported.tab

description: :unav
DOI: 10.7910/dvn/7zzkc7/lruwdq
title: METEC_results_post.R

description: :unav
DOI: 10.7910/dvn/7zzkc7/ncdoyo
title: readme.txt

description: :unav
DOI: 10.18112/openneuro.ds002417.v1.0.0
title: Audiovisual Learning MEG Dataset
DOI: 10.3886/e116208v1-21943
title: Replication data for: Identification of and Correction for Publication Bias

description: Some empirical results are more likely to be published than others. Selective publication leads to biased estimates and distorted inference. We propose two approaches for identifying the conditional probability of publication as a function of a study's results, the first based on systematic replication studies and the second on meta-studies. For known conditional publication probabilities, we propose bias-corrected estimators and confidence sets. We apply our methods to recent replication studies in experimental economics and psychology, and to a meta-study on the effect of the minimum wage. When replication and meta-study data are available, we find similar results from both.
DOI: 10.18739/a2vx0636z
title: Seasonal soil temperatures from a Mobile Instrumented System Platform (MISP) grid, Barrow, Alaska, 2016

description: Seasonal soil temperature measurements were collected during the 2016 growing season at each Mobile Instrumented System Platform (MISP) grid meter in Barrow, Alaska. At each meter one soil temperature measurement was acquired, the date, start and end times, and the people present were also recorded. Soil temperature measurements were taken on the north side of the grid in order to reduce disturbance of vegetation. These measurements are part of the Arctic Observing Network (AON) - International Tundra.
DOI: 10.25345/c56t11
title: MassIVE MSV000084836 - Integrated analysis if H2A.Z isoforms function reveals a complex interplay in gene regulation
title: This submission contains the six mass spectrometry files for the manuscript by Assala Lamaa et al. that describes the functional characterization of H2A.Z1 and H2A.Z2 isoforms. AP-MS experiments were performed from K562 cells and MS files were acquired on 5600+ TripleTOF mass spectrometer. For questions, please contact Jacques Cote (Jacques.Cote@crchudequebec.ulaval.ca).
DOI: 10.25345/c5bh62
title: MassIVE MSV000084835 - Large-scale RNAi screening uncovers new therapeutic targets in the human parasite Schistosoma mansoni
title: The sample is recombinant full-length STK25 protein from Schistosoma mansoni (UNIPROT ID G4VR95), modified to be catalytically inactive (kinase dead) by mutation of the catalytic lysine in the VAIK motif to arginine (K48R), and phosphorylated by incubation with recombinant full-length TAO from Schistosoma mansoni (UNIPROT ID G4V6P6) in an in vitro kinase assay. After kinase assay, proteins were separated by SDS-PAGE, and the band at ~75 kDa (STK25) cut out and submitted to the UTSW proteomics core for processing (digestion, cleanup, LC/MS/MS, and analysis done by core)
DOI: 10.25413/sun.11669277.v1
title: Metadata - N mineralisation data used to predict the response to fertilisation in semi-mature pine stands

description: This document provides descriptive information about the multiple regression file and the accompanying data file. The document addresses the discovery, context and technical aspects relating to these files.
DOI: 10.25919/5e2a93eae05cc
title: CCS - MV Seca 2018 V01 General Underwater Video

description: Project Overview: A range of solutions will be required to reach globally agreed emissions reductions targets for carbon dioxide (CO2). Carbon capture and storage (CCS) is part of the suite of technologies that will contribute to lowering atmospheric emissions of CO2 from Australia's energy system. There are a wide variety of technologies at various stages of technical and commercial readiness, with more development underway for cost effective CO2 capture and storage. Our research will provide new knowledge to inform cost-efficient measurement, monitoring and verification (MMV) of the environment of CCS projects in coastal waters. --o-- General_underwater_video. General underwater videos of divers conducting underwater sampling ie. sediment, fish, invertebrates Relevant component details: make = GoPro 5, model = Model 5
DOI: 10.7910/dvn/rayzgy/7vcgh0
title: Data.zip

description: All data files
DOI: 10.18739/a2r49g913
title: Seasonal soil temperatures from a Mobile Instrumented System Platform (MISP) grid, Atqasuk, Alaska, 2016

description: Seasonal soil temperature measurements were collected during the 2016 growing season at each Mobile Instrumented System Platform (MISP) grid meter in Atqasuk, Alaska. At each meter one soil temperature measurement was acquired, the date, start and end times, and the people present were also recorded. Soil temperature measurements were taken on the north side of the grid in order to reduce disturbance of vegetation. These measurements are part of the Arctic Observing Network (AON) - International Tundra.
DOI: 10.7910/dvn/dt3vte/hdf46f
title: DCAD-v1.0-dyadic.tab

description: :unav
DOI: 10.18739/a2736m25d
title: Seasonal water table depth (WTD) measurements from a Mobile Instrumented System Platform (MISP) grid, Atqasuk, Alaska, 2016

description: Water table depth (WTD) measurements were taken along the Mobile Instrumented System Platform (MISP) grid in Atqasuk, Alaska during the summer of 2016. The MISP grid is situated about 50 meters to the south of the International Tundra Experiment (ITEX) dry site. If a uniform representation of the water table within each plot was visible, then a WTD tube was placed there to measure the change in water table throughout the season. For instance if the plot was half saturated and half dry, then that plot was skipped and no tube was placed there. The exact location of WTD tubes are as follows: Between meters 0-1, 3-4, 5-6, 7-8, 9-10, 11-12, 13-14, 15-16, 17-18, 19-20, 21-22, 23-24, 25-26, 32-33, 34-35, 36-37, 38-39, 40-41, 42-43, 44-45, 46-47 &amp; 48-49. A total of 22 WTD tubes were installed into the MISP grid in Atqasuk. Measurements were made once a week during the summer snow free period between mid June and mid August. WTD was calculated as the difference between the depth (centimeter) of the water table above or below the measuring tube (- Values = water above tube, + Values= water level below the height of the tube) and the height of the top of the measuring tube above the ground level. Measurements were not made (NA in data sheet) if there was persistent snow cover, interference or disturbance from other experimental infrastructure, or if water table depth tubes were dry to the upper surface of the permafrost.
DOI: 10.18739/a2bv79v8b
title: Seasonal water table depth (WTD) measurements from a Mobile Instrumented System Platform (MISP) grid, Atqasuk, Alaska, 2017

description: Water table depth (WTD) measurements were taken along the Mobile Instrumented System Platform (MISP) grid in Atqasuk, Alaska during the summer of 2017. The MISP grid is situated about 50 meters to the south of the International Tundra Experiment (ITEX) dry site. If a uniform representation of the water table within each plot was visible, then a WTD tube was placed there to measure the change in water table throughout the season. For instance if the plot was half saturated and half dry, then that plot was skipped and no tube was placed there. The exact location of WTD tubes are as follows: Between meters 0-1, 3-4, 5-6, 7-8, 9-10, 11-12, 13-14, 15-16, 17-18, 19-20, 21-22, 23-24, 25-26, 32-33, 34-35, 36-37, 38-39, 40-41, 42-43, 44-45, 46-47 &amp; 48-49. A total of 22 WTD tubes were installed into the MISP grid in Atqasuk. Measurements were made once a week during the summer snow free period between mid June and mid August. WTD was calculated as the difference between the depth (centimeter) of the water table above or below the measuring tube (- Values = water above tube, + Values= water level below the height of the tube) and the height of the top of the measuring tube above the ground level. Measurements were not made (NA in data sheet) if there was persistent snow cover, interference or disturbance from other experimental infrastructure, or if water table depth tubes were dry to the upper surface of the permafrost.
DOI: 10.18739/a2gm81p23
title: Seasonal water temperature depth (WTD) measurements from a Mobile Instrumented System Platform (MISP) grid, Barrow, Alaska, 2017

description: Water table depth (WTD) measurements were taken along the Mobile Instrumented System Platform (MISP) grid in Barrow, Alaska during the summer of 2017. The MISP grid is situated about 200 meters to the north east side of the International Tundra Experiment (ITEX) dry sites. If a uniform representation of the water table within each plot was visible, then a WTD tube was placed there to measure the change in water table throughout the season. For instance if the plot was half saturated and half dry, then that plot was skipped and no tube was placed there. The exact location of WTD tubes are as follows: Between meters 0-1, 2-3, 5-6, 7-8, 9-10, 11-12, 13-14, 15-16, 18-19, 20-21, 24-25, 26-27, 28-29, 30-31, 32-33, 34-35, 36-37, 38-39, 42-43, 44-45, 47-48, &amp; 49-50. A total of 22 WTD tubes were installed into the MISP grid in Barrow. Measurements were made once a week during the summer snow free period between mid June and mid August. WTD was calculated as the difference between the depth (centimeter) of the water table above or below the measuring tube (- Values = water above tube, + Values= water level below the height of the tube) and the height of the top of the measuring tube above the ground level. Measurements were not made (NA in data sheet) if there was persistent snow cover, interference or disturbance from other experimental infrastructure, or if water table depth tubes were dry to the upper surface of the permafrost.
DOI: 10.18739/a2mc8rg6v
title: Seasonal water temperature depth (WTD) measurements from a Mobile Instrumented System Platform (MISP) grid, Barrow, Alaska, 2016

description: Water table depth (WTD) measurements were taken along the Mobile Instrumented System Platform (MISP) grid in Barrow, Alaska during the summer of 2016. The MISP grid is situated about 200 meters to the north east side of the International Tundra Experiment (ITEX) dry sites. If a uniform representation of the water table within each plot was visible, then a WTD tube was placed there to measure the change in water table throughout the season. For instance if the plot was half saturated and half dry, then that plot was skipped and no tube was placed there. The exact location of WTD tubes are as follows: Between meters 0-1, 2-3, 5-6, 7-8, 9-10, 11-12, 13-14, 15-16, 18-19, 20-21, 24-25, 26-27, 28-29, 30-31, 32-33, 34-35, 36-37, 38-39, 42-43, 44-45, 47-48, &amp; 49-50. A total of 22 WTD tubes were installed into the MISP grid in Barrow. Measurements were made once a week during the summer snow free period between mid June and mid August. WTD was calculated as the difference between the depth (centimeter) of the water table above or below the measuring tube (- Values = water above tube, + Values= water level below the height of the tube) and the height of the top of the measuring tube above the ground level. Measurements were not made (NA in data sheet) if there was persistent snow cover, interference or disturbance from other experimental infrastructure, or if water table depth tubes were dry to the upper surface of the permafrost.
DOI: 10.7910/dvn/dt3vte/hrqdrw
title: Codebook1.0.pdf

description: :unav
DOI: 10.7910/dvn/dt3vte/pc6ytm
title: DCAD-v1.0-main.tab

description: :unav
DOI: 10.5281/zenodo.3626712
title: cldf-datasets/lapollaqiang: Annotated texts from LaPolla and Huang's Grammar of Qiang

description: Cite the source as Randy J. LaPolla and Chenglong Huang. 2003. A Grammar of Qiang with annotated texts and glossary. (Mouton Grammar Library, 31.) Berlin: Mouton de Gruyter.
DOI: 10.5281/zenodo.3626713
title: cldf-datasets/lapollaqiang: Annotated texts from LaPolla and Huang's Grammar of Qiang

description: Cite the source as Randy J. LaPolla and Chenglong Huang. 2003. A Grammar of Qiang with annotated texts and glossary. (Mouton Grammar Library, 31.) Berlin: Mouton de Gruyter.
DOI: 10.18739/a2fn10s2d
title: Seasonal soil moisture measurements from a Mobile Instrumented System Platform (MISP) grid, Atqasuk, Alaska, 2017

description: Soil Moisture measurements were taken at each Mobile Instrumented System Platform (MISP) grid plot. The University of Texas at El Paso Systems Ecology Lab (SEL)'s Fieldscout TDR 300 Soil Moisture Probe was used to take all measurements. Each measurement was taken between the matted trail and each white board at every meter throughout the 2017 summer in Atqasuk, Alaska.