cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3531429
title: Potential wave 2 video scholen

description: Data collected through the e-PIC video-based comparative judgement instrument in wave 2 (May - June 2018) of the Potential project in primary and secondary schools. SPSS Syntax to construct reasoning scales: ***Schaalaanmaak PTSI Individual Needs*** IF (NMISS(PTSI07, PTSI14, PTSI15, PTSI16) &lt;= 1) <br> PTSI_individualneeds=MEAN(PTSI07, PTSI14, PTSI15, PTSI16).<br> VARIABLE LABELS PTSI_individualneeds 'PTSI reasoning on individual needs'.<br> EXECUTE. RECODE PTSI_individualneeds (SYSMIS=999).<br> EXECUTE.<br> Missing values PTSI_individualneeds (999).<br> Execute. ***Schaalaanmaak PTSI Safe & structured environment*** IF (NMISS(PTSI23, PTSI09, PTSI17, PTSI06, PTSI11, PTSI24) &lt;= 2) <br> PTSI_safestructure=MEAN(PTSI23, PTSI09, PTSI17, PTSI06, PTSI11, PTSI24).<br> VARIABLE LABELS PTSI_safestructure 'PTSI reasoning on a safe and structured environment'.<br> EXECUTE. RECODE PTSI_safestructure (SYSMIS=999).<br> EXECUTE.<br> Missing values PTSI_safestructure (999).<br> Execute. ***Schaalaanmaak PTSI Student involvement*** IF (NMISS(PTSI05, PTSI22, PTSI02, PTSI18, PTSI10, PTSI19) &lt;= 2) <br> PTSI_studentinvolve=MEAN(PTSI05, PTSI22, PTSI02, PTSI18, PTSI10, PTSI19).<br> VARIABLE LABELS PTSI_studentinvolve 'PTSI reasoning on student involvement'.<br> EXECUTE. RECODE PTSI_studentinvolve (SYSMIS=999).<br> EXECUTE.<br> Missing values PTSI_studentinvolve (999).<br> Execute. ***Schaalaanmaak DI_active_learning*** IF (NMISS(DI07,DI16,DI14,DI11,DI15,DI10) &lt;= 2) <br> DI_active_learning=MEAN(DI07,DI16,DI14,DI11,DI15,DI10) .<br> EXECUTE. RECODE DI_active_learning (SYSMIS=999).<br> EXECUTE.<br> Missing values DI_active_learning (999).<br> Execute. ***Schaalaanmaak DI_instructional_clarity*** IF (NMISS(DI27,DI28,DI04,DI03,DI24) &lt;= 2) DI_instructional_clarity=MEAN(DI27,DI28,DI04,DI03,DI24).<br> EXECUTE. RECODE DI_instructional_clarity (SYSMIS=999).<br> EXECUTE.<br> Missing values DI_instructional_clarity (999).<br> Execute. ***Schaalaanmaak DI_adaptive_teaching*** IF (NMISS(DI09,DI08,DI22,DI02,DI19) &lt;= 2) DI_adaptive_teaching=MEAN(DI09,DI08,DI22,DI02,DI19).<br> EXECUTE. RECODE DI_adaptive_teaching (SYSMIS=999).<br> EXECUTE.<br> Missing values DI_adaptive_teaching (999).<br> Execute. ***Schaalaanmaak DI_flexible_grouping*** IF (NMISS(DI06,DI01,DI25,DI26,DI05) &lt;= 1) DI_flexible_grouping=MEAN(DI06,DI01,DI25,DI26,DI05).<br> EXECUTE. RECODE DI_flexible_grouping (SYSMIS=999).<br> EXECUTE.<br> Missing values DI_flexible_grouping (999).<br> Execute.
description: This work was supported by Flanders Innovation and Entrepreneurship (www.vlaio.be/english) under grant 150011
DOI: 10.5281/zenodo.3531428
title: Potential wave 2 video scholen

description: Data collected through the e-PIC video-based comparative judgement instrument in wave 2 (May - June 2018) of the Potential project in primary and secondary schools. SPSS Syntax to construct reasoning scales: ***Schaalaanmaak PTSI Individual Needs*** IF (NMISS(PTSI07, PTSI14, PTSI15, PTSI16) &lt;= 1) <br> PTSI_individualneeds=MEAN(PTSI07, PTSI14, PTSI15, PTSI16).<br> VARIABLE LABELS PTSI_individualneeds 'PTSI reasoning on individual needs'.<br> EXECUTE. RECODE PTSI_individualneeds (SYSMIS=999).<br> EXECUTE.<br> Missing values PTSI_individualneeds (999).<br> Execute. ***Schaalaanmaak PTSI Safe & structured environment*** IF (NMISS(PTSI23, PTSI09, PTSI17, PTSI06, PTSI11, PTSI24) &lt;= 2) <br> PTSI_safestructure=MEAN(PTSI23, PTSI09, PTSI17, PTSI06, PTSI11, PTSI24).<br> VARIABLE LABELS PTSI_safestructure 'PTSI reasoning on a safe and structured environment'.<br> EXECUTE. RECODE PTSI_safestructure (SYSMIS=999).<br> EXECUTE.<br> Missing values PTSI_safestructure (999).<br> Execute. ***Schaalaanmaak PTSI Student involvement*** IF (NMISS(PTSI05, PTSI22, PTSI02, PTSI18, PTSI10, PTSI19) &lt;= 2) <br> PTSI_studentinvolve=MEAN(PTSI05, PTSI22, PTSI02, PTSI18, PTSI10, PTSI19).<br> VARIABLE LABELS PTSI_studentinvolve 'PTSI reasoning on student involvement'.<br> EXECUTE. RECODE PTSI_studentinvolve (SYSMIS=999).<br> EXECUTE.<br> Missing values PTSI_studentinvolve (999).<br> Execute. ***Schaalaanmaak DI_active_learning*** IF (NMISS(DI07,DI16,DI14,DI11,DI15,DI10) &lt;= 2) <br> DI_active_learning=MEAN(DI07,DI16,DI14,DI11,DI15,DI10) .<br> EXECUTE. RECODE DI_active_learning (SYSMIS=999).<br> EXECUTE.<br> Missing values DI_active_learning (999).<br> Execute. ***Schaalaanmaak DI_instructional_clarity*** IF (NMISS(DI27,DI28,DI04,DI03,DI24) &lt;= 2) DI_instructional_clarity=MEAN(DI27,DI28,DI04,DI03,DI24).<br> EXECUTE. RECODE DI_instructional_clarity (SYSMIS=999).<br> EXECUTE.<br> Missing values DI_instructional_clarity (999).<br> Execute. ***Schaalaanmaak DI_adaptive_teaching*** IF (NMISS(DI09,DI08,DI22,DI02,DI19) &lt;= 2) DI_adaptive_teaching=MEAN(DI09,DI08,DI22,DI02,DI19).<br> EXECUTE. RECODE DI_adaptive_teaching (SYSMIS=999).<br> EXECUTE.<br> Missing values DI_adaptive_teaching (999).<br> Execute. ***Schaalaanmaak DI_flexible_grouping*** IF (NMISS(DI06,DI01,DI25,DI26,DI05) &lt;= 1) DI_flexible_grouping=MEAN(DI06,DI01,DI25,DI26,DI05).<br> EXECUTE. RECODE DI_flexible_grouping (SYSMIS=999).<br> EXECUTE.<br> Missing values DI_flexible_grouping (999).<br> Execute.
description: This work was supported by Flanders Innovation and Entrepreneurship (www.vlaio.be/english) under grant 150011
DOI: 10.17632/hnh26bm7xb.1
title: Data for: Towards a Model for Spoken Conversational Search

description: Conversation is the natural mode for information exchange in daily life, a spoken conversational interaction for search input and output is a logical format for information seeking. However, the conceptualisation of user–system interactions or information exchange in spoken conversational search (SCS) has not been explored. The first step in conceptualising SCS is to understand the conversational moves used in an audio-only communication channel for search. This paper explores conversational actions for the task of search. We define a qualitative methodology for creating conversational datasets, propose analysis protocols, and develop the SCSdata. Furthermore, we use the SCSdata to create the first annotation schema for SCS: the SCoSAS, enabling us to investigate interactivity in SCS. We further establish that SCS needs to incorporate interactivity and pro-activity to overcome the complexity that the information seeking process in an audio-only channel poses. In summary, this exploratory study unpacks the breadth of SCS. Our results highlight the need for integrating discourse in future SCS models and contributes the advancement in the formalisation of SCS models and the design of SCS systems.
DOI: 10.17632/nhjr4zybdt.1
title: Data for: Towards a Model for Spoken Conversational Search

description: Conversation is the natural mode for information exchange in daily life, a spoken conversational interaction for search input and output is a logical format for information seeking. However, the conceptualisation of user–system interactions or information exchange in spoken conversational search (SCS) has not been explored. The first step in conceptualising SCS is to understand the conversational moves used in an audio-only communication channel for search. This paper explores conversational actions for the task of search. We define a qualitative methodology for creating conversational datasets, propose analysis protocols, and develop the SCSdata. Furthermore, we use the SCSdata to create the first annotation schema for SCS: the SCoSAS, enabling us to investigate interactivity in SCS. We further establish that SCS needs to incorporate interactivity and pro-activity to overcome the complexity that the information seeking process in an audio-only channel poses. In summary, this exploratory study unpacks the breadth of SCS. Our results highlight the need for integrating discourse in future SCS models and contributes the advancement in the formalisation of SCS models and the design of SCS systems.
DOI: 10.5281/zenodo.3531556
title: High resolution ICON simulation over the Tropical Atlantic

description: This dataset contains binary info on liquid water content of an ICON simulation performed over the subtropics. Detailed information available in the readme file. This data is used in the publication titled: On the size dependence of cumulus cloud spacing.
DOI: 10.5281/zenodo.3531557
title: High resolution ICON simulation over the Tropical Atlantic

description: This dataset contains binary info on liquid water content of an ICON simulation performed over the subtropics. Detailed information available in the readme file. This data is used in the publication titled: On the size dependence of cumulus cloud spacing.
DOI: 10.17632/2v6rj69mzv.1
title: Data for: Protracted shearing at mid crustal conditions during large-scale thrusting in the Scandinavian Caledonides
DOI: 10.21943/auas.10265873
title: client input on core competencies for professionals in dealing with child unsafety

description: interviews with clients / representatives of clients on core competencies for professionals in dealing with families were child safety is an issue. eight small group interviews.coded MaxQDA file. please contact l.boendermaker@hva.nl for more information on this data.
DOI: 10.5281/zenodo.3531470
title: Data for the prediction of chatter vibrations in robotic milling of aluminium parts based on previous experiences using neural network

description: This data has been used for the validation of the software developed by DFKI in collaboration with IDEKO for the prediction of stability in robotic milling of aluminium parts, in the framework of COROMA research project funded by the European Union. www.coroma-project.eu The source of information is stability lobes obtained from FRFs obtained mixing by receptance coupling experimental FRFs of the robot, spindle and toolholder with FRFs of the tool obtained analitycally using beams theory. Real machinings have not been done since they would be very time consuming. Once the stability lobes where available random sampling has been done in the lobes between certain boundaries of axial depth of cut and spindle speed to represent machining with different conditions. The information contained here includes: - Data sets for different conditions, with tools of different diameters and different number of cutting teeth. (in the naming of the folder D represents diameter, Z represents number of teeth). - Most of the data sets also include figures with the milling stability lobe charts for different radial depths of cut and different diameters and number of teeth. In these figures the random sampling representing machining tests has been marked with a black X. - There are also versions of the data sets with different number of samples (20 or 40) in order to test the prediction algorithm with a different number of information. - In the data sets an extended version has been created, representing the know-how of the operator that if a machining is unstable all the machinings with higher axial depth of cut will be unstable, and if a machining is stable all the machinings with lower axial depth of cut will be stable. - Companion documents in PDF format in order to provide more detailed information on the datasets and results. Keywords: Milling, machining, vibration, chatter, stability, prediction, neural network, robot, robotic, AI, artificial intelligence. www.ideko.es<br> www.dfki.de Asier Barrios<br> IDEKO research centre<br> Arriaga Kalea, 2<br> Elgoibar 20870, Spain<br> Phone: +34 943748000<br> abarrios@ideko.es October 2019
DOI: 10.5281/zenodo.3531471
title: Data for the prediction of chatter vibrations in robotic milling of aluminium parts based on previous experiences using neural network

description: This data has been used for the validation of the software developed by DFKI in collaboration with IDEKO for the prediction of stability in robotic milling of aluminium parts, in the framework of COROMA research project funded by the European Union. www.coroma-project.eu The source of information is stability lobes obtained from FRFs obtained mixing by receptance coupling experimental FRFs of the robot, spindle and toolholder with FRFs of the tool obtained analitycally using beams theory. Real machinings have not been done since they would be very time consuming. Once the stability lobes where available random sampling has been done in the lobes between certain boundaries of axial depth of cut and spindle speed to represent machining with different conditions. The information contained here includes: - Data sets for different conditions, with tools of different diameters and different number of cutting teeth. (in the naming of the folder D represents diameter, Z represents number of teeth). - Most of the data sets also include figures with the milling stability lobe charts for different radial depths of cut and different diameters and number of teeth. In these figures the random sampling representing machining tests has been marked with a black X. - There are also versions of the data sets with different number of samples (20 or 40) in order to test the prediction algorithm with a different number of information. - In the data sets an extended version has been created, representing the know-how of the operator that if a machining is unstable all the machinings with higher axial depth of cut will be unstable, and if a machining is stable all the machinings with lower axial depth of cut will be stable. - Companion documents in PDF format in order to provide more detailed information on the datasets and results. Keywords: Milling, machining, vibration, chatter, stability, prediction, neural network, robot, robotic, AI, artificial intelligence. www.ideko.es<br> www.dfki.de Asier Barrios<br> IDEKO research centre<br> Arriaga Kalea, 2<br> Elgoibar 20870, Spain<br> Phone: +34 943748000<br> abarrios@ideko.es October 2019
DOI: 10.5281/zenodo.3531802
title: Source codes for preprint Newmark algorithm for dynamic analysis with generalized Maxwell model

description: Snapshot of a GitLab repository, containing Python source codes of results presented in the preprint by the same authors.
DOI: 10.5281/zenodo.3531812
title: KPIs for the validation of the GreenSoul behavioural models

description: This document presents both the methodology followed to select the Key Performance Indicators (KPI) and the final KPIs selected to measure the impact achieved by the actions carried on during the pilots. To select the KPIs we first have prepared a comprehensive list of KPIs to consider, then we follow a Delphi Method to reach a consensus among a panel of experts about the how to score every KPI in several aspects and finally we perform a descriptive statistical analysis to select the best set of KPIs.
DOI: 10.17632/b9j8jkrwts.1
title: Scenario III: CHEI2 Data for a single cybersecurity domain audit (Awareness Education)

description: Dataset to validate a single cybersecurity domain audit in a second organization (Awareness Education)
DOI: 10.5281/zenodo.3531811
title: KPIs for the validation of the GreenSoul behavioural models

description: This document presents both the methodology followed to select the Key Performance Indicators (KPI) and the final KPIs selected to measure the impact achieved by the actions carried on during the pilots. To select the KPIs we first have prepared a comprehensive list of KPIs to consider, then we follow a Delphi Method to reach a consensus among a panel of experts about the how to score every KPI in several aspects and finally we perform a descriptive statistical analysis to select the best set of KPIs.
DOI: 10.17632/fbjznmh9h8.1
title: Scenario II: CHEI2 Data for a Cybersecurity audit of several domains (Governance and Strategy, Legal and Compliance, Cyber risks, Frameworks and Regulations, Incident Management, Cyber Insurance and Evolving Technologies)

description: Dataset to validate a Cybersecurity audit of several domains in a second organization (Governance and Strategy, Legal and Compliance, Cyber risks, Frameworks and Regulations, Incident Management, Cyber Insurance and Evolving Technologies).
DOI: 10.17632/kym459cj6h.1
title: Scenario I: CHEI2 Data for a Cybersecurity audit including all domains of the CyberSecurity Audit Model (CSAM)

description: Dataset to validate all domains of the CyberSecurity Audit Model (CSAM) in a second organization
DOI: 10.17632/dbwtsxnr29.1
title: Mantle-driven uplift and transpression influence shallow crustal exhumation across the India-Asia convergent margin (Fission Track Data)
DOI: 10.17632/hxwny6ggc6.1
title: Cognitive follow-up six and nine months after intervention with NeuroEPO in Cuban Parkison's patients

description: The study was part of a randomized double-blind physician-lead placebo-controlled trial to evaluate the safety of NeuroEPO in Parkinson’s Disease patients (https://clinicaltrials.gov/ number NCT04110678) with motor and cognitive secondary outcomes measures. The sample was composed of twenty-six patients (10 women) with a clinical diagnosis of idiopathic Parkinson’s disease according to the UK Brain Bank Criteria. The average age was 53.88 years (SD=7.66) and the duration of disease was 5.5 years (SD=3.49). The patients were in stage Ⅰ-Ⅱ of Hoehn and Yahr. The NeuroEPO group included fifteen patients (7 women) who received a weekly 1mL dose of intranasal NeuroEPO for 5 weeks and the placebo group included eleven patients (3 women) who received a similar formulation containing the same ingredients except for EPO. Six months after the safety trial finished, 10 patients from the EPO group and 8 patients from the original placebo group received 9 months of intensive NeuroEPO treatment, with 1mL bulb, 3 times per week, for one month (induction phase) and later 1/2 bulb three times per week, for eight months. To evaluate the motor symptoms the Unified Parkinson Disease Rating Scale (UPDRS) motor section was used. For evaluation of cognition the neuropsychological battery included the Mini Mental State Examination, Dementia Rating Scale (global cognitive functioning), the Rey Auditory Verbal Learning test (episodic verbal memory), the Working Memory Index from WAIS Ⅲ (working memory) and the Rey Complex Figure, copy and delayed recall/reproduction of the copy (visuospatial ability and non-verbal memory), Delis-Kaplan verbal fluency, Trail-Making, and the Stroop color word Interference test and the Frontal Assessment Battery were used to assess executive functions. Mood was assessed with the Hospital Anxiety Depression Scale (HADS). For the follow-up of the patients 9 months after the second intervention, a subset of the full battery was employed.
DOI: 10.17632/6st2v4yg7z.1
title: Data from: A novel approach to ensembles applied to energy consumption time series

description: Monthly data from two sets of series across European economies: (i) Gross Inland Natural Gas Consumption (in terajoules, TJ); and (ii) Energy Supplied (in gigawatt-hour, GWh). Data for the former were collected from the Statistical Office of the European Union database (EUROSTAT, 2019). Data for the latter were compiled from the International Energy Agency (IEA) Monthly Electricity Statistics reports, which provide information on energy production and trade for all OECD Member Countries (IEA, 2019). References: EUROSTAT (2019). European Statistics supply of gas – gross inland consumption – monthly data. https://ec.europa.eu/eurostat/web/energy/data/database. Accessed: 2019-10-02. IEA (2019). International Energy Agency monthly electricity statistics. http://www.iea.org/statistics/monthlystatistics/monthlyelectricitystatistics/. Accessed: 2019-10-02.
DOI: 10.17632/hg4x8cn7zb.1
title: Academic Motivation of Incoming Freshmen (2019)

description: Results of the Academic Motivation Scale (AMS), applied to 591 incoming freshmen of a Japanese private university. Students were surveyed during Orientation Week, before the start of their first classes. Student IDs have been removed to protect the identities of the survey participants. Variables for Department (1: electrical engineering; 2: mechanical engineering; 3: civil engineering; 4: nuclear engineering; 5: environmental sciences; 6: information sciences; 7: design; 8: sports & health sciences), Proficiency (1: highest; 2: high; 3: medium; 4: low), Gender (1: male; 2: female), and Nationality (1: Japanese; 2: other).
DOI: 10.17632/y34czv2f7c.3
title: Estaillades limestone Sample 7 - Vp vs. Sw during drainage - 50 kHz

description: The data consist of P-wave velocities measured during a drainage experiment over a limestone sample. It consists of 31 samples for water saturations ranging from 1 to 0. The values for water saturation and P-wave velocity are respectively expressed in percentage and kilometers per second. The values were digitalized from the plot in Figure 5 of the article: Cadoret, T., Marion, D. and Zinszner, B. , 1995. Influence of frequency and fluid distribution on elastic wave velocities in partially saturated limestone. JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 100, NO. B6, PAGES 9789-9803. THe cite authors exemplified the effect of patchy saturation on the effective elastic properties of rocks obtained from bench top measurements on various carbonate rocks. At different water saturation levels, they recorded the time it took for a P-wave to traverse the rock sample from one end of the rock to the other. By measuring the thickness of the sample, then they computed the velocity of the P-wave. Patches of water were shown to form as fully water saturated rocks were dried.
DOI: 10.17632/y34czv2f7c.4
title: Estaillades limestone Sample 7 - Vp vs. Sw during drainage - 50 kHz

description: The data consist of P-wave velocities measured during a drainage experiment over a limestone sample. It consists of 31 samples for water saturations ranging from 1 to 0. The values for water saturation and P-wave velocity are respectively expressed in percentage and kilometers per second. The values were digitalized from the plot in Figure 5 of the article: Cadoret, T., Marion, D. and Zinszner, B. , 1995. Influence of frequency and fluid distribution on elastic wave velocities in partially saturated limestone. JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 100, NO. B6, PAGES 9789-9803. THe cite authors exemplified the effect of patchy saturation on the effective elastic properties of rocks obtained from bench top measurements on various carbonate rocks. At different water saturation levels, they recorded the time it took for a P-wave to traverse the rock sample from one end of the rock to the other. By measuring the thickness of the sample, then they computed the velocity of the P-wave. Patches of water were shown to form as fully water saturated rocks were dried.
DOI: 10.5281/zenodo.3531425
title: Metadata for the paper "Technology Boom, Labor Reallocation, and Human Capital Depreciation"

description: This document lists the confidential datasets used for this study and how to request access to them.
DOI: 10.5281/zenodo.3531426
title: Metadata for the paper "Technology Boom, Labor Reallocation, and Human Capital Depreciation"

description: This document lists the confidential datasets used for this study and how to request access to them.
DOI: 10.17632/y34czv2f7c.1
title: Estaillades S7 - Vp vs. Sw during drainage

description: The data consist of P-wave velocities measured during a drainage experiment over a limestone sample. It consists of 31 samples for water saturations ranging from 0 to 1. The values for water saturation and P-wave velocity are respectively expressed in percentage and kilometers per second. The values were digitalized from the plot in Figure 5 of the article: Cadoret, T., Marion, D. and Zinszner, B. , 1995. Influence of frequency and fluid distribution on elastic wave velocities in partially saturated limestone. JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 100, NO. B6, PAGES 9789-9803.