321 Works

Emergent behaviour in the energy transition

E.J.L. Chappin & R. Blomme

Faludi Blogging: Chasing Territorialism

Andreas Faludi

Data presented in the paper: \"Microscale modeling of rate-dependent failure in thermoplastic composites under off-axis loading\"

Dragan Kovačević, Bharath Sundararajan & Frans van der Meer
Tex files containing data used for plots in the paper.

sj-docx-1-pec-10.1177_03010066221122697 - Supplemental material for How do people distribute their attention while observing The Night Watch?

Joost C. F. de Winter, Dimitra Dodou & Wilbert Tabone
Supplemental material, sj-docx-1-pec-10.1177_03010066221122697 for How do people distribute their attention while observing The Night Watch? by Joost C. F. de Winter, Dimitra Dodou, and Wilbert Tabone in Perception

How do people distribute their attention while observing The Night Watch?

Joost C. F. de Winter, Dimitra Dodou & Wilbert Tabone
This study explored how people look at The Night Watch (1642), Rembrandt's masterpiece. Twenty-one participants each stood in front of the painting for 5 min, while their eyes were recorded with a mobile eye-tracker and their thoughts were verbalized with a think-aloud method. We computed a heatmap of the participants’ attentional distribution using a novel markerless mapping method. The results showed that the participants’ attention was mainly directed at the faces of the two central...

Data underlying the article: \"Guided-Acoustic Stimulated Brillouin Scattering in Silicon Nitride Photonic Circuits\"

Roel Botter, kaixuan ye, Yvan Klaver, Radius N. S. Suryadharma, Okky Daulay, Gaojian Liu, Jasper van den Hoogen, Lou Kanger, Peter J. M. van der Slot, Edwin Klein, Marcel Hoekman, Chris Roeloffzen, Yang Liu & David Marpaung
Data underlying the article: "Guided-Acoustic Stimulated Brillouin Scattering in Silicon Nitride Photonic Circuits". The data set consists of several different measurement and simulation results for the Brillouin gain, Brillouin shift and microwave photonic filter responses as published in the resource article. All data is stored in files using the CSV-format. The set is structered to give the data per figure (subfigure), where each subfigure has a folder containing a README-file describing the data in that...

Data Extracted for the Systematic Literature Review on Non-profit Open data Intermediaries and their effects on Open data Usability Barriers

Liubov Pilshchikova, Anneke Zuiderwijk-van Eijk & Marijn Janssen
The dataset contains the data extracted from the literature for the Systematic Literature Review and is referenced or used in the extended abstract titled "How do Non-profit Open data Intermediaries enhance Open data Usability? A Systematic Literature Review", submitted to the 18th International Symposium on Open Collaboration (Companion), September 6–10, 2022, Madrid, Spain. https://doi.org/10.1145/3555051.3555061

Supporting information: \"Chenier Formation Through Wave Winnowing and Tides\"

Silke Tas, Bas van Maren & Ad Reniers
This dataset accompanies the paper entitled, "Chenier Formation Through Wave Winnowing and Tides", published in the Journal of Geophysical Research: Earth Surface. The Delft3D model input files used in this study have been included here as supporting information. One of the 28 scenarios modelled has been included here (the scenario shown in Figure 1), the model files of all scenarios are identical except for the boundary conditions (waves and water level), the MorFac (morphological acceleration...

Delft Systematic Yacht Hull Series hydrostatics data

Jasper den Ouden
These files contain the bare hull hydrostatics of all DSYHS models. Both on full scale (Lwl=10m) and at model scale. The values of Lwl, Bwl etc. are given at 0, 10, 20 and 30 degrees of heel. In order to get this overview all models were 'loaded' in Maxsurf Stability with the correct mass at the correct LCB. In the heeled conditions the models were free to trim, which matches the situation during the model...

Additional file 2 of An engineered non-oxidative glycolytic bypass based on Calvin-cycle enzymes enables anaerobic co-fermentation of glucose and sorbitol by Saccharomyces cerevisiae

Aafke C. A. van Aalst, Robert Mans & Jack T. Pronk
Additional file 2. Measurement data used to prepare Tables 1 and 2.

Additional file 4 of An engineered non-oxidative glycolytic bypass based on Calvin-cycle enzymes enables anaerobic co-fermentation of glucose and sorbitol by Saccharomyces cerevisiae

Aafke C. A. van Aalst, Robert Mans & Jack T. Pronk
Additional file 4. Measurement data used to prepare Tables S2 and S3.

Additional file 1 of An engineered non-oxidative glycolytic bypass based on Calvin-cycle enzymes enables anaerobic co-fermentation of glucose and sorbitol by Saccharomyces cerevisiae

Aafke C. A. van Aalst, Robert Mans & Jack T. Pronk
Additional file 1. Measurement data used to prepare Fig. 3.

Additional file 5 of An engineered non-oxidative glycolytic bypass based on Calvin-cycle enzymes enables anaerobic co-fermentation of glucose and sorbitol by Saccharomyces cerevisiae

Aafke C. A. van Aalst, Robert Mans & Jack T. Pronk
Additional file 5: Table S1. Predicted ethanol yields on substrate, biomass-specific substrate-uptake rates (qsubstrate) and biomass yields on substrate for wild-type S. cerevisiae (WT) and strains with an engineered PRK-RuBisCO bypass of the oxidative reaction in glycolysis on both glucose and on sorbitol. Rates and yields were predicted for cultures growing at different specific growth rates, using an extended stoichiometric model of the core metabolic network of S. cerevisiae (1, 2). A Cmol biomass (CH1.8O0.5N0.2,...

Additional file 11 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 11: Fig. S3. Screenshot of the user-friendly interface for the virtual chondrocytes App. The standalone Matlab-based applications can be launched and used without Matlab license, provided that the compiler Matlab Runtime is installed ( https://nl.mathworks.com/products/compiler/matlab-runtime.html ). The virtual chondrocyte initial state can be set as healthy or hypertrophic, allowing the user to test any scenarios. All the 60 components may be perturbed alone or in any sort of combination by forcing the variables...

Additional file 12 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 12: Table S4. Targets and associated small molecules or growth factors for in vitro validation. The first row indicates the targets to be perturbed, [+] stands for activation while [-] stands for inhibition. The name (resp. cat number) of the small molecule or growth factor employed to achieve that effect is indicated in the row called ‘Molecule name’ (resp. ‘Cat n°’).

Additional file 14 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 14: Fig. S5. Illustration of the algorithm for the asynchronous updating of variables with a simplified (3 nodes) example network. The network represents interactions happening in one of the subnetworks (protein= fast reactions or genetic = slow reactions). Inhibitions are represented in red and activations are in black. The mathematical rules corresponding to the network are displayed. If the rules result in a value lower than 0 (resp. higher than 1), the value...

Supplementary Information

Jingyu Lin, Brett Bryan, Xudong Zhou, Peirong Lin, Hong Xuan Do, Lei Gao, Xinchen Gu, Zhifeng Liu, Luwen Wang, Shanlin Tong, Jiacong Huang, Qian Wang, Yuan Zhang, Hongkai Gao, Zilong Chen, Weili Duan, Zheyu Xie, Tong Cui, Junzhi Liu, Mingqian Li, Xiaodong Li, Zhenwu Xu, Fei Guo, Lele Shu, Bin Li … & Zhifeng Yang
This is the Supplementary Information for the paper

Setting Physical Activity Goals with a Virtual Coach: Data and Analysis Code

Nele Albers, Beyza Hizli, Bouke L. Scheltinga, Eline Meijer & Willem-Paul Brinkman
This is the data and analysis code underlying the paper "Setting Physical Activity Goals with a Virtual Coach: Vicarious Experiences, Personalization and Acceptance" by Nele Albers, Beyza Hizli, Bouke L. Scheltinga, Eline Meijer, and Willem-Paul Brinkman. The paper examines the use of personalized vicarious experiences in a goal-setting dialog for physical activity with a virtual coach.
Study The paper is based on the study conducted in March 2022 for the publicly available Master's thesis...

Word file S1

Jingyu Lin, Brett Bryan, Xudong Zhou, Peirong Lin, Hong Xuan Do, Lei Gao, Xinchen Gu, Zhifeng Liu, Luwen Wang, Shanlin Tong, Jiacong Huang, Qian Wang, Yuan Zhang, Hongkai Gao, Zilong Chen, Weili Duan, Zheyu Xie, Tong Cui, Junzhi Liu, Mingqian Li, Xiaodong Li, Zhenwu Xu, Fei Guo, Lele Shu, Bin Li … & Zhifeng Yang
Specific content of the survey

Additional file 2 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 2. Supplementary computational method. Equations, mathematical framework and justification of deviation from the general rule in the equations.

Additional file 2 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 2. Supplementary computational method. Equations, mathematical framework and justification of deviation from the general rule in the equations.

Additional file 3 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 3: Table S2. List of variables and mouse genes correspondence. All mathematical variables and the corresponding node in the network have names written in upper cases and do not reflect the official human or mouse nomenclatures. To relate those variables to actual genes more easily, we provide this table of correspondence. A related mouse gene name and NCBI ID is indicated for each variable. Nevertheless, this is not exhaustive since some variables represent...

Additional file 9 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 9: Data S4. In silico screening predictions of combinatorial treatments. (Microsoft Excel Worksheet). This file reports the results of a screening of combinatorial perturbations. More particularly, it reports all conditions that lead to a transition towards a healthy chondrocyte (SOX9+) when starting from a hypertrophic-like chondrocyte (Runx2+). The first sheet reports conditions leading to such transition 100% of the time, between 99 and 90% of the time for the next sheet and so...

Additional file 2 of Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques

Jasmijn A. Baaijens, Alessandro Zulli, Isabel M. Ott, Ioanna Nika, Mart J. van der Lugt, Mary E. Petrone, Tara Alpert, Joseph R. Fauver, Chaney C. Kalinich, Chantal B. F. Vogels, Mallery I. Breban, Claire Duvallet, Kyle A. McElroy, Newsha Ghaeli, Maxim Imakaev, Malaika F. Mckenzie-Bennett, Keith Robison, Alex Plocik, Rebecca Schilling, Martha Pierson, Rebecca Littlefield, Michelle L. Spencer, Birgitte B. Simen, William P. Hanage, Nathan D. Grubaugh … & Michael Baym
Additional file 2. Review history.

Additional file 2 of Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques

Jasmijn A. Baaijens, Alessandro Zulli, Isabel M. Ott, Ioanna Nika, Mart J. van der Lugt, Mary E. Petrone, Tara Alpert, Joseph R. Fauver, Chaney C. Kalinich, Chantal B. F. Vogels, Mallery I. Breban, Claire Duvallet, Kyle A. McElroy, Newsha Ghaeli, Maxim Imakaev, Malaika F. Mckenzie-Bennett, Keith Robison, Alex Plocik, Rebecca Schilling, Martha Pierson, Rebecca Littlefield, Michelle L. Spencer, Birgitte B. Simen, William P. Hanage, Nathan D. Grubaugh … & Michael Baym
Additional file 2. Review history.

Registration Year

  • 2023
    27
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    203
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    51
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    23
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    6
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    2
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    4
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    3
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Resource Types

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Affiliations

  • Delft University of Technology
    321
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    38
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    34
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    13
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    10
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    5
  • Central South University
    5
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    5
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    5