cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/yv552m8s98.1
title: Reconstitution of a 26-subunit human kinetochore reveals cooperative microtubule binding by CENP-OPQUR and NDC80. Pesenti et al

description: The approximately thirty core subunits of kinetochores assemble on centromeric chromatin containing the histone H3 variant CENP-A and connect chromosomes with spindle microtubules. The chromatin proximal 16-subunit CCAN (constitutive centromere associated network) creates a mechanically stable bridge between CENP-A and the kinetochore’s microtubule-binding machinery, the 10-subunit KMN assembly. Here, we reconstituted a stoichiometric 11-subunit human CCAN core that forms when the CENP-OPQUR complex binds to a joint interface on the CENP-HIKM and CENP-LN complexes. The resulting CCAN particle is globular and connects KMN and CENP-A in a 26-sub- unit recombinant particle. The disordered, basic N-terminal tail of CENP-Q binds microtubules and promotes accurate chromosome alignment, cooperating with KMN in microtubule binding. The N-terminal basic tail of the NDC80 complex, the microtubule-binding subunit of KMN, can function- ally replace the CENP-Q tail. Our work dissects the connectivity and architecture of CCAN and re- veals unexpected functional similarities between CENP-OPQUR and the NDC80 complex.
DOI: 10.17632/52vk5yybxd.1
title: SeriousGames_DevalS

description: Research Questiona: In this study we aim to answer the following research questions: - RQ1. Do university students show an improvement in generic skills after interacting with serious games? - RQ2. Do serious games make learning about the key components of assessment easier? - RQ3. Do serious games promote the development of students' competence in assessment? - RQ4. Are serious games useful and do they serve to transfer learning about the components of the assessment process, the skills and competencies required into other academic and professional contexts? These data were collected by two different questionnaires: QSG-EVA-SG Questionnaire A Day with Eva and QSG-EVONG-SG Questionnaire EVONG-Assessment in Action. The questionnaires are structured into categorical questions (university, year of study, degree course, subject area and gender), Likert-type questions with six levels of response (1 = minimum; 6 = maximum) organized in dimensions.
DOI: 10.17632/7psjn7wrfp.2
title: RIG-I uses an ATPase-powered translocation-throttling mechanism for kinetic proofreading of RNAs and oligomerization. Devarkar et al

description: Biotin-Streptavidin displacement Assays and EMSA Gel Scans
DOI: 10.17632/ch5ztx4dsm.1
title: Raw data of Figures 5 and 7

description: Experimental validation of a CFD model using a narrow wave flume Raw data of Figure 5: EWF and NWF wave probe signals in an individual test: Comparison of the free surface location between experimental and computational signals in the position of the first probe and the second probe, with their corresponding correlation index. Experimental conditions: h=0.07 m, Ap=0.03 m and u=0.070 m/s. Raw data of Figure 7: Experimental and computational free surface evolution measured by L1 and L2 fitted with the second-order Stokes’ expression. Individual test: h=0.07 m, Ap=0.02 m and u=0.050 m/s.
DOI: 10.17632/ch5ztx4dsm.2
title: Experimental validation of a CFD model using a narrow wave flume: Raw data of Figures 5 and 7

description: Experimental validation of a CFD model using a narrow wave flume Raw data of Figure 5: EWF and NWF wave probe signals in an individual test: Comparison of the free surface location between experimental and computational signals in the position of the first probe and the second probe, with their corresponding correlation index. Experimental conditions: h=0.07 m, Ap=0.03 m and u=0.070 m/s. Raw data of Figure 7: Experimental and computational free surface evolution measured by L1 and L2 fitted with the second-order Stokes’ expression. Individual test: h=0.07 m, Ap=0.02 m and u=0.050 m/s.
DOI: 10.17632/ggbkmcxs5n.1
title: Reinforcement Learning Based Network Boolean Tomography

description: It includes all the data generated by the numerical simulations as well as the source code written in Python.
DOI: 10.17632/j49s64jjfg.1
title: Processed data for manuscript Effects of Cu addition to n-type 𝛃-FeSi2/Si Composite on the Si precipitation and Its Thermoelectric Properties

description: This is the processed data in PNG format for 1) Phase analysis by XRD 2) Microstructure analysis by SEM 3) Electrical Properties analysis of electrical resistivity and Seebeck coefficient 4) Thermal properties analysis of thermal conductivity and its component (carrier and lattice) 5) Calculated power factor value 6) Calculated thermoelectric performance value (ZT)
DOI: 10.17632/j49s64jjfg.2
title: Processed data for manuscript Effects of Cu addition to n-type 𝛃-FeSi2/Si Composite on the Si precipitation and Its Thermoelectric Properties

description: This is the processed data in PNG format for 1) Phase analysis by XRD 2) Microstructure analysis by SEM 3) Electrical Properties analysis of electrical resistivity and Seebeck coefficient 4) Thermal properties analysis of thermal conductivity and its component (carrier and lattice) 5) Calculated power factor value 6) Calculated thermoelectric performance value (ZT)
DOI: 10.17632/nmf5hjdr3x.1
title: Raw Data for manuscript Effects of Cu addition to n-type 𝛃-FeSi2/Si Composite on the Si precipitation and Its Thermoelectric Properties

description: This raw data in .txt version include 1) Phase analysis by XRD 2) Electrical properties analysis of Electrical resistivity and Seebeck coefficient 3) Thermal properties analysis of Thermal Conductivity and its component of electronic thermal conductivity and lattice thermal conductivity 4) Thermoelectric performance values of calculated power factor and ZT
DOI: 10.17632/t9xtxy6tvd.1
title: world bank data - Healthcare systems

description: The attached file includes the set on inputs, outputs and the flexible measure used from the World bank open data(https://data.worldbank.org/) in an efficiency analysis of 120 countries using Data Envelopment Analysis (DEA).
DOI: 10.5061/dryad.tk82cd1.1/2.1
title: Geological stage boundaries for analysis

description: These give the temporal bins that the occrrence and environmental data were binned into.
DOI: 10.5061/dryad.tk82cd1.1/3.1
title: Overall extinction rates

description: This file contains 100 subsampled iterations for the overall extinction rates to plot a basic version of Figure 1. Use with 'TimeBinningCode.R'
DOI: 10.5061/dryad.tk82cd1.1/4.1
title: Tropical and temperate genus extinction rates

description: This file contains 100 subsampled iterations for the tropical and temperate affinity genera extinction rates to plot a basic version of Figure 1. Use with 'TimeBinningCode.R'
DOI: 10.5061/dryad.tk82cd1.1/5.1
title: R code for time binning and Figure 1

description: This code allows for binning of the raw data as in the analysis to some substage levels, then allows reading in of the original pre-calculated extinction rates for the Phanerozoic, and allows a basic version of Figure 1 to be plotted. To perform the exact analyses, the reader is directed to the URL home of the R package divDyn.
DOI: 10.5281/zenodo.1444093
title: A-Force Bc Data From U-Tokyo

description: Please see the README file included in each ZIP file.
description: {"references": ["Oshima, N., et al. (2012),  J. Geophys. Res., 117, D03204, doi:10.1029/2011JD016552.", "Kondo, Y., et al. (2016), J. Geophys. Res. Atmos., 121, 4691\u20134712, doi:10.1002/2015JD024479.", "Koike, M., et al. (2012), J. Geophys. Res., 117, D17205, doi:10.1029/2011JD017324."]}
DOI: 10.5281/zenodo.1444094
title: A-Force Bc Data From U-Tokyo

description: Please see the README file included in each ZIP file.
description: {"references": ["Oshima, N., et al. (2012),  J. Geophys. Res., 117, D03204, doi:10.1029/2011JD016552.", "Kondo, Y., et al. (2016), J. Geophys. Res. Atmos., 121, 4691\u20134712, doi:10.1002/2015JD024479.", "Koike, M., et al. (2012), J. Geophys. Res., 117, D17205, doi:10.1029/2011JD017324."]}
DOI: 10.17632/8kz5t2xv6r.1
title: Do dictatorships redistribute more?

description: The attached archive file includes instructions and replication files for “Do dictatorships redistribute more?.”
DOI: 10.17632/ch5ztx4dsm.3
title: Experimental validation of a CFD model using a narrow wave flume: Raw data of Figures 5 and 7

description: Experimental validation of a CFD model using a narrow wave flume Raw data of Figure 5: EWF and NWF wave probe signals in an individual test: Comparison of the free surface location between experimental and computational signals in the position of the first probe and the second probe, with their corresponding correlation index. Experimental conditions: h=0.07 m, Ap=0.03 m and u=0.070 m/s. Raw data of Figure 7: Experimental and computational free surface evolution measured by L1 and L2 fitted with the second-order Stokes’ expression. Individual test: h=0.07 m, Ap=0.02 m and u=0.050 m/s.
DOI: 10.17632/d2kv9964h6.1
title: Simulation Scenarios

description: A full description of the simulation scenarios used in my research
DOI: 10.17632/jvs5w3hz3h.3
title: VDSP instances

description: These are the 69 instances used in the article: B. Domínguez-Martín, I. Rodríguez-Martín, J.J. Salazar-González, 2017. An exact algorithm for a Vehicle-and-Driver Scheduling Problem. Computers & Operations Research, vol. 81, pp.247-256. https://doi.org/10.1016/j.cor.2016.12.022. The instance name gives information about the number of nodes, the number of vehicles of each type available at the depots, and the class of the instance. For example, n16-1-1-1-1a.dat is an instance with 16 nodes, including the depots, one vehicle of each type at each depot, and the class of the instance is a. These data files have the same format as the classical CVRP instances from the literature, but some new parameters have been added. That is: The first line contains the name of the instance. The next lines contain: TYPE = ‘CVRP’ because the instances are CVRP instances adapted to our problem. DIMENSION = the number of nodes, including the depots EDGE_WEIGHT_TYPE = ‘EUC_2D’ because we use the Eucliden distance for all our instances. The next lines contains the capacity of the vehicles of type 1 (CAPACITY Y) and the capacity of the vehicles of type 2 (CAPACITY X). VEHICLES1 and VEHICLES 2 denotes the number of vehicles of type 2 available at first and second depot, respectively. DRIVERS1 and DRIVERS2 denotes the number of vehicle of type 1 available ar first and seconds depot, respectively. The lines after NODE_COORD_SECTION contain the following information: n x y where n=customer number (1 and the last number correspond to the depots), x=x coordinate, y=y coordinate. The lines after DEMAND_SECTION show the following information: n d where n=customer number (0 and the last number correspond to the depots), d=customer demand(0 for the depots and 1 for the customers) Finally, DEPOT_SECTION1 and DEPOT_SECTION2 indicate that the first and the last node represent the first and the second depot, respectively.
DOI: 10.17632/rs8n356tpk.1
title: Data for: Wind Energy in the United Kingdom: Modelling the Effect of Increases in Installed Capacity on Generation Efficiency

description: Background data for analysis of wind fleet
DOI: 10.17632/s37xwnm7n2.1
title: Health related quality of life in autoinflammatory diseases

description: This dataset refers to the data of 31 people, aged from 19 to 59 years, diagnosed with an autoinflammatory disease in a specialized clinic in Northern Italy. Data concerns socio-demographic and clinical information and results to 6 questionnaires, as follows. The Short Form Health Survey (SF-36) is composed by 36 items grouped into 8 subscales. Four subscales constitute the physical HRQOL: physical functioning, role limitations due to physical health, bodily pain, general health perception. Four subscales reflect the mental HQOL: role limitations due to emotional problems, vitality, mental health, social functioning. Scores range from 0 (worst) to 100 (best). The State–Trait Anxiety Inventory Form Y (STAI-Y) consists of two scales measuring state (STAT-Y1) and trait anxiety (STAT-Y2), respectively. For the purpose of this study, only the trait scale (Y2) has been employed because it reflects a more general and long-standing condition of anxiety. The scale is composed of 20 items rated on a frequency scale from 1 (“almost never”) to 4 (“almost always”). A sum score of 50, representing the 75th percentile among general medical patients, was used to label the patient as “anxious”. The Italian version of the Beck Depression Inventory – II (BDI-II) consists of 21 items reflecting the main symptoms of depression. Items are rated on a 4-point scale, indicating the degree of severity, from 0 (not at all) to 3 (the extreme form of each symptom). The New Italian Version of the Coping Orientation to Problems Experience (COPE) is a 60-item questionnaire based on a five-factor model: social support, avoidance strategies, positive attitude, problem solving and transcendent orientation. The Illness Perception Questionnaire-Revised (IPQ-R) is divided into three parts, as follows. In the illness identity part the participant has to rate ‘yes’ or ‘no’ with regard to a list of 14 symptoms. The illness representation part consists of 38 items rated on a 5-point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’. Higher scores reflect a stronger belief in a specific dimension. The illness causes part consists of 18 possible causes that the patient has to evaluate as responsible for the disease onset on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree) and of the three main causes that s/he considers to be the basis of his/her illness. The Dependency Grids consists in a list of 23 problem situations. The person is asked to think of a time when each situation had occurred, and to indicate with a cross the person or the people (including him or herself) that he or she went to for help. The following indices were calculated: the amount of resources; the amount of resources effectively used in the grid; the dependency percentage concentrated on self and on the most significant person for the patient (significant other); the uncertainty index by column which points out the distribution of dependency among different resources.
DOI: 10.17632/svr83ty225.1
title: Probit Data of Brineshrimp Lethality Assay of Anti-Tumour Plant Extracts and Fractions of Medicinal Plants from Southwest Nigeria

description: Our data describes the ethanol extraction and solvent (n-hexane, ethylacetate, chloroform ) partitioning yields of 16 anti-tumour medicinal plants collected from south west Nigeria. At the end of the partitioning process, we separated polyphenols from the aqueous fractions using polyamide-6 solid phase extraction (SPE) cartridges. Purified extracts and fractions were tested on brineshrimps and percentage mortality was used for probit analysis.
DOI: 10.17632/w5sbtwy8y9.3
title: DVRP instances

description: These are the 25 instances, wiht 10, 15, 20, and 25 nodes, used in the article: Domínguez-Martín B., Rodríguez-Martín I., Salazar-González J.J. (2018) The Driver and Vehicle Routing Problem. Computers & Operations Research, vol. 92, pp.56-64. https://doi.org/10.1016/j.cor.2017.12.010, plus another 16 instances with 50 nodes used in: Domínguez-Martín B., Rodríguez-Martín I., Salazar-González JJ. (2018) A Heuristic Approach to the Driver and Vehicle Routing Problem. In: Cerulli R., Raiconi A., Voß S. (eds) Computational Logistics. ICCL 2018. Lecture Notes in Computer Science, vol 11184, pp 295-305. Springer, Cham. https://doi.org/10.1007/978-3-030-00898-7_19 Instances' names give information about the number of nodes. For example, n10-1.dat is the first instance with 10 nodes. In each file, the first lines contain: NAME = the name of the instances TYPE = does not apply here EDGE_WEIGHT_TYPE = ‘EUC_2D’ because we use the Eucliden distance for all our instances. DIMENSION = the number of nodes, including the depots The lines after NODE_COORD_SECTION contain the following information: n x y where n = node number x = x coordinate, y = y coordinate. Finally, DEPOT_SECTION1 and DEPOT_SECTION2 indicate that the first and the last node represent the first and the second depot, respectively.
DOI: 10.3886/e106542v1
title: City of Hamilton 2018 Municipal Election Issues Poll

description: The purpose of this project was to gather attitudes and opinions on municipal issues facing the City of Hamilton, on behalf of the Beasley Neighbourhood Association. The Beasley Neighbourhood Association was seeking information to enhance the quality of life in the downtown Hamilton area. The questions explore issues related to the performance of city council, affordable housing, and the priority of issues facing the City of Hamilton.  Results of this survey are considered accurate to +/- 5.3 percentage points 19 times out of 20. <br>
description: Weights are included based on age and gender.
description: Response Rates: A response rate can be calculated using all active lines or only people who answered. The response rate using the 21,188 answered numbers was 2.1% for phone book listed landlines and 1.2% for random dialed numbers (which were likely mostly cell phone numbers but may have also included unlisted landlines and businesses). The overall response rate for answered numbers was 1.8%. These rates were calculated based on the number of people who completed the entire survey. The overall response rate for answered numbers for the first question of the survey was 2.6%. The response rate using all active lines was 0.347% when examining all questions or 0.511% when examining only the first question.
description: telephone audio computer-assisted self interview (TACASI)~~telephone interview~~
description: Smallest Geographic Unit: City of Hamilton, Ontario
description: A total of 401,333
phone numbers were called as part of this campaign. A total of 109,867 of these
numbers were active lines (i.e. the call was an active number that either
generated no answer, an answering machine or the call was answered). A contact
rate of 19.3% was achieved (i.e. 21,188 of the active lines answered the phone).
DOI: 10.5061/dryad.tk82cd1.1/1.1
title: Post-Cambrian Phanerozoic raw occurrences

description: This is the raw occurrence dataset used for analyses, downloaded from the Paleobiology Database (http://www.paleobiodb.org/) on 18th December ‎2017
DOI: 10.5281/zenodo.1442902
title: Fertilization Data

description: Nitrogen cycling data from a fertilization study conduced in the Luquillo Experiment Forest in Puerto Rico during the summer of 2015.
DOI: 10.5281/zenodo.1442903
title: Fertilization Data

description: Nitrogen cycling data from a fertilization study conduced in the Luquillo Experiment Forest in Puerto Rico during the summer of 2015.
DOI: 10.5281/zenodo.1442916
title: Minion 1D² Reads From Mus Musculus Gl261 Cell Lines

description: Called FASTQ and raw FAST5 MinION cDNA reads (1D²) from a murine GL261 neuroblastoma cell line, cultured at the Malaghan Institute of Medical Research, sequenced on a R9.5 flow cell in August 2017 using the LSK309 1D² kit for ligating ONT adapters to cDNA generated using strand-switching primers.

The called reads for the entire sequencing run are available:


	called_reads_1Dsq_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- called reads from both/all runs (1D²-corrected fastq files only).
	called_reads_uncorrected_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- uncorrected reads from both/all runs.
	metadata_called_reads_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- metadata associated with all called sequences (e.g. sequencing_summary.txt)


This dataset only includes a subset of the total reads as raw signal / FAST5 files:


	Actb_GL261_cDNA_2017-Aug-04_1D2.tar -- reads from one run that mapped (in whole or in part) to a mouse beta-actin transcript [ENSMUST00000100497.10].
	Ubb_GL261_cDNA_2017-Aug-04_1D2.tar -- reads from one run that mapped (in whole or in part) to a mouse ubiquitin transcript [ENSMUST00000019649.3].
DOI: 10.17632/88dxsnwpzb.1
title: Probit Mortality Data of Extracts and Fractions of Anti-Tumour Medicinal Plants from Southwest Nigeria.

description: Our data describes the ethanol extraction and solvent (n-hexane, ethylacetate, chloroform ) partitioning yields of 16 anti-tumour medicinal plants collected from south west Nigeria. At the end of the partitioning process, we separated polyphenols from the aqueous fractions using polyamide-6 solid phase extraction (SPE) cartridges. Purified extracts and fractions were tested on brineshrimps and percentage mortality was used for probit analysis.
DOI: 10.17632/kvj8k99y3f.1
title: Daytime and nighttime thermal effect

description: Thermal performance curves show that increases in temperatures below optima have positive effects whereas increases in temperatures above optima have negative effects. Organisms often experience adverse thermal environments caused by daytime extreme high temperatures while they may also recover or repair themselves during nighttime moderate temperatures. Thermal effects of daily fluctuating temperatures may thus be divided into two opposite processes, i.e. negative effect of daytime heat stress and positive effects of nighttime recovery. Despite recent progress on the consequences of increased daily temperature variability, the independent effect of daytime high temperature and nighttime mild temperature on organism performance and their combination in determining thermal effect of daily temperature fluctuations still remains unclear. By independently manipulating daily maximum and minimum temperatures, we tested how changes in daytime heat stress and nighttime recovery affect development and survival of eggs and the critical thermal maximum (CTmax) of first-instar larva in a lady beetle species Propylea japonica. We find that thermal effects on development rate and survival differed between daytime and nighttime temperatures, with daytime high temperatures have negative effects whereas nighttime mild temperatures have positive effects. The extent of daytime heat stress and nighttime recovery also affect egg development and survival and CTmax of first-instar larva, indicating independent and combined effects of daytime and nighttime temperatures in determining thermal performances. Our findings provide an insight into the thermal effect of day-to-night fluctuating temperatures and have important implications for predicting the impacts of daytime and nighttime warming under climate change.
DOI: 10.17632/wkwfxmvfdm.1
title: RNADemocracy Production Data

description: This data was created during the creation of the novel RNAdemocracy RNA secondary structure prediction program. This method utilizes consensus scoring to produce ensemble models.
DOI: 10.25489/d4wfbx
title: DOI for test