cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/gjnraokpambfht-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C17H16N2O4/c1-8-5-12(17(21)23-4)19-16-13(22-3)7-10-6-11(9(2)20)18-15(10)14(8)16/h5-7,18H,1-4H3

description: dataset for infrared absorption spectroscopy (IR) IR ṽ (cm-1) = 3406, 3133, 2947, 1738, 1721, 1707, 1653, 1639, 1615, 1590, 1538, 1502, 1480, 1436, 1410, 1380, 1371, 1358, 1273, 1257, 1230, 1208, 1156, 1130, 1040, 1006, 984, 942, 909, 897, 881, 852, 782, 767, 687, 668.
DOI: 10.14272/gjnraokpambfht-uhfffaoysa-n.1
title: C17H16N2O4
title: InChI=1S/C17H16N2O4/c1-8-5-12(17(21)23-4)19-16-13(22-3)7-10-6-11(9(2)20)18-15(10)14(8)16/h5-7,18H,1-4H3

description: This dataset contains experimental data from a sample (InChI=1S/C17H16N2O4/c1-8-5-12(17(21)23-4)19-16-13(22-3)7-10-6-11(9(2)20)18-15(10)14(8)16/h5-7,18H,1-4H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) infrared absorption spectroscopy (IR) high-resolution mass spectrometry (HRMS)
DOI: 10.14272/gjnraokpambfht-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C17H16N2O4/c1-8-5-12(17(21)23-4)19-16-13(22-3)7-10-6-11(9(2)20)18-15(10)14(8)16/h5-7,18H,1-4H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, DMSO-d6, ppm) δ = 10.60 (s, 1H), 8.09 (d, J = 0.7 Hz, 1H), 7.56 (d, J = 1.9 Hz, 1H), 7.46 (s, 1H), 4.00 (s, 3H), 3.95 (s, 3H), 3.11 (s, 3H), 2.61 (s, 3H).
DOI: 10.14272/hiuddufozheufh-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C17H16N2O5/c1-8-5-10(16(20)23-3)19-15-12(22-2)7-9-6-11(17(21)24-4)18-14(9)13(8)15/h5-7,18H,1-4H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, DMSO-d6, ppm) δ = 10.88 (s, 1H), 8.09 (d, J = 0.7 Hz, 1H), 7.47 (s, 1H), 7.38 (d, J = 1.9 Hz, 1H), 3.99 (s, 3H), 3.95 (s, 3H), 3.92 (s, 3H), 3.13 (s, 3H).
DOI: 10.14272/hiuddufozheufh-uhfffaoysa-n.1
title: C17H16N2O5
title: InChI=1S/C17H16N2O5/c1-8-5-10(16(20)23-3)19-15-12(22-2)7-9-6-11(17(21)24-4)18-14(9)13(8)15/h5-7,18H,1-4H3

description: This dataset contains experimental data from a sample (InChI=1S/C17H16N2O5/c1-8-5-10(16(20)23-3)19-15-12(22-2)7-9-6-11(17(21)24-4)18-14(9)13(8)15/h5-7,18H,1-4H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) infrared absorption spectroscopy (IR) high-resolution mass spectrometry (HRMS)
DOI: 10.14272/hiuddufozheufh-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C17H16N2O5/c1-8-5-10(16(20)23-3)19-15-12(22-2)7-9-6-11(17(21)24-4)18-14(9)13(8)15/h5-7,18H,1-4H3

description: dataset for infrared absorption spectroscopy (IR) IR ṽ (cm-1) = 3480, 2953, 1711, 1694, 1648, 1616, 1540, 1507, 1483, 1442, 1431, 1384, 1351, 1294, 1276, 1256, 1205, 1168, 1132, 1102, 1046, 994, 935, 899, 883, 866, 851, 818, 782, 763, 713.
DOI: 10.14272/hiuddufozheufh-uhfffaoysa-n/chmo0000498
title: high-resolution mass spectrometry (HRMS) ()
title: InChI=1S/C17H16N2O5/c1-8-5-10(16(20)23-3)19-15-12(22-2)7-9-6-11(17(21)24-4)18-14(9)13(8)15/h5-7,18H,1-4H3

description: dataset for high-resolution mass spectrometry (HRMS) HRMS–EI (m/z): 327.0988 [M-H]-
DOI: 10.14272/hiuddufozheufh-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C17H16N2O5/c1-8-5-10(16(20)23-3)19-15-12(22-2)7-9-6-11(17(21)24-4)18-14(9)13(8)15/h5-7,18H,1-4H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, DMSO-d6, ppm) δ = 165.6, 161.0, 150.9, 144.0, 143.6, 140.1, 128.1, 126.2, 125.3, 122.9, 119.9, 109.7, 102.6, 55.7, 52.4, 52.0, 22.0.
DOI: 10.14272/ndtwzhurudspqv-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (methyl 2-methyl-3-oxobutanoate)
title: InChI=1S/C6H10O3/c1-4(5(2)7)6(8)9-3/h4H,1-3H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, CDCl3, ppm) δ = 203.7, 171.1, 53.6, 52.6, 28.6, 12.9.
DOI: 10.14272/ndtwzhurudspqv-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (methyl 2-methyl-3-oxobutanoate)
title: InChI=1S/C6H10O3/c1-4(5(2)7)6(8)9-3/h4H,1-3H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3, ppm) δ = 3.74 (s, 3H), 3.52 (q, J = 7.2 Hz, 1H), 2.24 (s, 3H), 1.35 (d, J = 7.2 Hz, 3H).
DOI: 10.14272/ndtwzhurudspqv-uhfffaoysa-n.1
title: methyl 2-methyl-3-oxobutanoate
title: InChI=1S/C6H10O3/c1-4(5(2)7)6(8)9-3/h4H,1-3H3

description: This dataset contains experimental data from a sample (methyl 2-methyl-3-oxobutanoate, InChI=1S/C6H10O3/c1-4(5(2)7)6(8)9-3/h4H,1-3H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) infrared absorption spectroscopy (IR) high-resolution mass spectrometry (HRMS)
DOI: 10.14272/ndtwzhurudspqv-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (methyl 2-methyl-3-oxobutanoate)
title: InChI=1S/C6H10O3/c1-4(5(2)7)6(8)9-3/h4H,1-3H3

description: dataset for infrared absorption spectroscopy (IR) IR, ṽ (cm-1) = 2995, 2954, 2364, 1741, 1713, 1454, 1435, 1359, 1334, 1244, 1206, 1152, 1100, 1078, 1051, 987, 955, 859.
DOI: 10.14272/ndtwzhurudspqv-uhfffaoysa-n/chmo0000498
title: high-resolution mass spectrometry (HRMS) (methyl 2-methyl-3-oxobutanoate)
title: InChI=1S/C6H10O3/c1-4(5(2)7)6(8)9-3/h4H,1-3H3

description: dataset for high-resolution mass spectrometry (HRMS) HRMS–EI (m/z): 99.0411 [M-OMe]+
DOI: 10.14272/glvnzyodmkseps-onegzznksa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (methyl (E)-4-oxopent-2-enoate)
title: InChI=1S/C6H8O3/c1-5(7)3-4-6(8)9-2/h3-4H,1-2H3/b4-3+

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3, ppm) δ = 7.01 (d, J = 16.1 Hz, 1H), 6.64 (d, J = 16.1 Hz, 1H), 3.81 (s, 3H), 2.35 (s, 3H).
DOI: 10.14272/glvnzyodmkseps-onegzznksa-n.1
title: methyl (E)-4-oxopent-2-enoate
title: InChI=1S/C6H8O3/c1-5(7)3-4-6(8)9-2/h3-4H,1-2H3/b4-3+

description: This dataset contains experimental data from a sample (methyl (E)-4-oxopent-2-enoate, InChI=1S/C6H8O3/c1-5(7)3-4-6(8)9-2/h3-4H,1-2H3/b4-3+). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) infrared absorption spectroscopy (IR) high-resolution mass spectrometry (HRMS)
DOI: 10.14272/glvnzyodmkseps-onegzznksa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (methyl (E)-4-oxopent-2-enoate)
title: InChI=1S/C6H8O3/c1-5(7)3-4-6(8)9-2/h3-4H,1-2H3/b4-3+

description: dataset for infrared absorption spectroscopy (IR) IR, ṽ (cm-1) = 3061, 3008, 2957, 1719, 1675, 1644, 1456, 1429, 1362, 1316, 1264, 1253, 1197 1180, 1023, 1002, 994, 913, 879, 822, 717.
DOI: 10.14272/glvnzyodmkseps-onegzznksa-n/chmo0000498
title: high-resolution mass spectrometry (HRMS) (methyl (E)-4-oxopent-2-enoate)
title: InChI=1S/C6H8O3/c1-5(7)3-4-6(8)9-2/h3-4H,1-2H3/b4-3+

description: dataset for high-resolution mass spectrometry (HRMS) HRMS–EI (m/z): 113.0233 [M-CH3]·+
DOI: 10.14272/glvnzyodmkseps-onegzznksa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (methyl (E)-4-oxopent-2-enoate)
title: InChI=1S/C6H8O3/c1-5(7)3-4-6(8)9-2/h3-4H,1-2H3/b4-3+

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (101 MHz, CDCl3, ppm) δ = 197.6, 166.0, 140.2, 131.2, 52.5, 28.3.
DOI: 10.5281/zenodo.5497500
title: Supplementary data: "Secondary control activation analysed and predicted with explainable AI"

description: This repository contains processed data and result files for the paper Secondary control activation analysed and predicted with explainable AI . The code for producing the processed data and the results is available at github. <strong>Data</strong> The data folder contains the feature and target data used to train the ML model. The data for Germany comprises the following folders and files: <em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features. <em>inputs_&lt;model_type&gt;.h5 </em><em>:</em> The input features for the different model types used in the paper including the engineered features. Depending on the model type, the input files also contain the IGCC features. <em>outputs.h5 </em>: The activated aFRR volumes in Germany. <em>version_2021-08-20</em>: Folder containing the training and test sets used for the results. <em>documentation_of_data_download</em>: Information files concerning the ENTSO-E raw data and its aggregation. In addition to the German time series, the data folder contains the raw input data for the remaining IGCC states. Note that the results contain more model types as actually discussed in the paper. <strong>Data sources</strong> The data for input features (<em>raw_input_data.h5</em> and <em>input_&lt;model_type&gt;.h5</em>) is derived from ENTSO-E Transparency Platform data [1]. The target data (<em>outputs.h5</em>) is based on publicly available data from the German Transmission System Operators (TSOs) [2]. <strong>Results</strong> The result folder comprises the results of hyper-parameter optimization, model prediction and interpretation via SHAP. The model type, the loss function to train the model and the data set for prediction/interpretation were varied. <em>cv_results_&lt;model_type&gt;_&lt;loss_function&gt;.csv</em> : Performance results for each combination in the hyper-parameter grid search. <em>cv_best_params_&lt;model_type&gt;_&lt;loss_function&gt;.csv</em> : Hyper-parameters used in the final (optimized) model. <em>shap_values_&lt;data_set&gt;_&lt;model_type&gt;_&lt;loss_function&gt;.npy</em> : First-order SHAP values calculated on different data sets: The train set, the randomized test set and the continuous test set. <em>y_pred_&lt;data_set&gt;.h5</em> : Predictions of daily profile predictor and Machine Learning models. <strong>Disclaimer</strong> The data might be subject to copyright or related rights. Please consult the primary data owner.
description: {"references": ["[1] ENTSO-E Transparency Platform. https://transparency.entsoe.eu/ (Accessed on 01.07.2021)", "[2] 50Hertz Transmission GmbH, Amprion GmbH, TransnetBW GmbH and TenneT TSO GmbH, \"Regelleistung.Net\", https://www.regelleistung.net/ext/ (Accessed on 20.07.2021)"]}
DOI: 10.5281/zenodo.5497499
title: Supplementary data: "Secondary control activation analysed and predicted with explainable AI"

description: This repository contains processed data and result files for the paper Secondary control activation analysed and predicted with explainable AI . The code for producing the processed data and the results is available at github. <strong>Data</strong> The data folder contains the feature and target data used to train the ML model. The data for Germany comprises the following folders and files: <em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features. <em>inputs_&lt;model_type&gt;.h5 </em><em>:</em> The input features for the different model types used in the paper including the engineered features. Depending on the model type, the input files also contain the IGCC features. <em>outputs.h5 </em>: The activated aFRR volumes in Germany. <em>version_2021-08-20</em>: Folder containing the training and test sets used for the results. <em>documentation_of_data_download</em>: Information files concerning the ENTSO-E raw data and its aggregation. In addition to the German time series, the data folder contains the raw input data for the remaining IGCC states. Note that the results contain more model types as actually discussed in the paper. <strong>Data sources</strong> The data for input features (<em>raw_input_data.h5</em> and <em>input_&lt;model_type&gt;.h5</em>) is derived from ENTSO-E Transparency Platform data [1]. The target data (<em>outputs.h5</em>) is based on publicly available data from the German Transmission System Operators (TSOs) [2]. <strong>Results</strong> The result folder comprises the results of hyper-parameter optimization, model prediction and interpretation via SHAP. The model type, the loss function to train the model and the data set for prediction/interpretation were varied. <em>cv_results_&lt;model_type&gt;_&lt;loss_function&gt;.csv</em> : Performance results for each combination in the hyper-parameter grid search. <em>cv_best_params_&lt;model_type&gt;_&lt;loss_function&gt;.csv</em> : Hyper-parameters used in the final (optimized) model. <em>shap_values_&lt;data_set&gt;_&lt;model_type&gt;_&lt;loss_function&gt;.npy</em> : First-order SHAP values calculated on different data sets: The train set, the randomized test set and the continuous test set. <em>y_pred_&lt;data_set&gt;.h5</em> : Predictions of daily profile predictor and Machine Learning models. <strong>Disclaimer</strong> The data might be subject to copyright or related rights. Please consult the primary data owner.
description: {"references": ["[1] ENTSO-E Transparency Platform. https://transparency.entsoe.eu/ (Accessed on 01.07.2021)", "[2] 50Hertz Transmission GmbH, Amprion GmbH, TransnetBW GmbH and TenneT TSO GmbH, \"Regelleistung.Net\", https://www.regelleistung.net/ext/ (Accessed on 20.07.2021)"]}
DOI: 10.5281/zenodo.7273600
title: Data and code for peer review, Silk et al.

description: Editors and peer reviewers of the journal can access the zip file. A private github repository will be linked to Zenodo and assigned a DOI on acceptance. Thank you very much.
DOI: 10.5281/zenodo.7273599
title: Data and code for peer review, Silk et al.

description: Editors and peer reviewers of the journal can access the zip file. A private github repository will be linked to Zenodo and assigned a DOI on acceptance. Thank you very much.
DOI: 10.5281/zenodo.5118351
title: Supplementary data: "Revealing drivers and risks for power grid frequency stability with explainable AI"

description: This repository contains processed data and result files for the paper Revealing drivers and risks for power grid frequency stability with explainable AI. The code for producing the processed data and the results is available at github. <strong>Data</strong> For each area, the data folder contains the feature and target data used to train the ML model. <em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features. <em>input_forecast.h5 </em>and<em> input_actual.h5: </em>The day-ahead available (forecast) and ex-post available (actual) data of external features including the engineered features. <em>outputs.h5 </em>: The grid frequency stability indicators. <em>version_2021-07-01</em>: Folder containing the training and test sets used for the results. <em>documentation_of_data_download</em><strong>: </strong>Plots and information files concerning the ENTSO-E raw data and its aggregation. <strong>Data sources</strong> The data for input features (<em>raw_input_data.h5, input_forecast.h5 </em>and<em> input_actual.h5</em>) is derived from ENTSO-E Transparency Platform data [1]. The target data (<em>outputs.h5</em>) is based on power grid frequency recordings from the German Transmission System Operator TransnetBW [2]. <strong>Results</strong> For each area and each target, the result folder comprises the results of hyper-parameter optimization, model prediction and interpretation via SHAP. The results refer to the full model and the restricted model (containing only day-ahead features). <em>cv_results_gtb_full.csv</em> and <em>cv_results_gtb_day_ahead.csv</em> : Performance results for each combination in the hyper-parameter grid search. <em>cv_best_params_gtb_full.csv</em> and <em>cv_best_params_gtb_day_ahead.csv</em> : Hyper-parameters used in the final (optimized) model. <em>shap_values_gtb_full.npy</em> and <em>shap_interaction_values_gtb_full.npy </em>: First-order SHAP values and second-order SHAP interaction values for the full model. <em>y_pred.h5</em> : Predictions of daily profile predictor, full model and day-ahead model. <strong>Disclaimer</strong> The data might be subject to copyright or related rights. Please consult the primary data owner.
description: {"references": ["[1] ENTSO-E Transparency Platform. https://transparency.entsoe.eu/ (Accessed on 01.07.2021)", "[2] TransnetBW GmbH. \"Netzfrequenz\". https://www.transnetbw.de/de/strommarkt/systemdienstleistungen/regelenergie-bedarf-und-abruf (accessed on 26.03.2020)"]}
DOI: 10.25625/ayxfzs/uspt94
title: JabalAkhdar_2007_2018.tab

description: Tabular data, all variables explained in comment fields of header row
DOI: 10.25625/ayxfzs
title: Jabal Akhdar data of 2007-2018, survey data

description: Data from 38 (2007) and 28 (2018) interviews with oases farmers of the Al-Jabal-al-Akhdar region, Oman.
DOI: 10.26302/sshade/experiment_fs_20201007_002
title: Fe K edge XAS fluorescence of FeSO4 in buffer for the study of the Iron binding site in the mature and C-terminal regions of wild-type and mutant human frataxin.
DOI: 10.6084/m9.figshare.21458274
title: open questionnaire.xlsx

description: this data set is about the learners' perception of PLEs
DOI: 10.6084/m9.figshare.21458274.v1
title: open questionnaire.xlsx

description: this data set is about the learners' perception of PLEs
DOI: 10.21227/wvvx-9914
title: ABAQUS solver based finite element model of curvilinear cylindrical gear sets (*.inp file)

description: Since the result files (*.odb files) obtained after the ABAQUS solution take up too much storage space, only the *.inp files for FEA are uploaded in this dataset, and all *.inp files are automatically generated by the Matlab program.
DOI: 10.5281/zenodo.6198946
title: "Dynamics of Endothelial Engagement and Filopodia Formation in Complex 3D Microscaffolds" : Raw Data

description: Raw data of the paper "Dynamics of Endothelial Engagement and Filopodia Formation in Complex 3D Microscaffolds", <strong> </strong> International Journal of Molecular Sciences, 2022. <strong>Abstract: </strong>The understanding of endothelium–extracellular matrix interactions during the initiation of new blood vessels is of great medical importance; however, the mechanobiological principles governing endothelial protrusive behaviours in 3D microtopographies remain imperfectly understood. In blood capillaries submitted to angiogenic factors (such as vascular endothelial growth factor, VEGF), endothelial cells can transiently transdifferentiate in filopodia-rich cells, named tip cells, from which angiogenesis processes are locally initiated. This protrusive state based on filopodia dynamics contrasts with the lamellipodia-based endothelial cell migration on 2D substrates. Using two-photon polymerization, we generated 3D microstructures triggering endothelial phenotypes evocative of tip cell behaviour. Hexagonal lattices on pillars (“open”), but not “closed” hexagonal lattices, induced engagement from the endothelial monolayer with the generation of numerous filopodia. The development of image analysis tools for filopodia tracking allowed to probe the influence of the microtopography (pore size, regular vs.<em> </em>elongated structures, role of the pillars) on orientations, engagement and filopodia dynamics, and to identify MLCK (myosin light-chain kinase) as a key player for filopodia-based protrusive mode. Importantly, these events occurred independently of VEGF treatment, suggesting that the observed phenotype was induced through microtopography. These microstructures are proposed as a model research tool for understanding endothelial cell behaviour in 3D fibrillary networks.
description: Funding: This work was supported by a doctoral grant from the program "Interfaces pour le Vivant", Sorbonne Université (recipient P.U.) and a doctoral grant from the Oversea Study Program of Guangzhou Elite Project (recipient X.J.). This work was supported by the Centre National de la Recherche Scientifique (CNRS), Inserm (Institut National de la Santé et de la Recherche Médicale), Sorbonne Université, Chimie ParisTech and Institut Curie. This work received support from grant ANR-08-PCVI-0012-02.
DOI: 10.5281/zenodo.6198945
title: "Dynamics of Endothelial Engagement and Filopodia Formation in Complex 3D Microscaffolds" : Raw Data

description: Raw data of the paper "Dynamics of Endothelial Engagement and Filopodia Formation in Complex 3D Microscaffolds", <strong> </strong> International Journal of Molecular Sciences, 2022. <strong>Abstract: </strong>The understanding of endothelium–extracellular matrix interactions during the initiation of new blood vessels is of great medical importance; however, the mechanobiological principles governing endothelial protrusive behaviours in 3D microtopographies remain imperfectly understood. In blood capillaries submitted to angiogenic factors (such as vascular endothelial growth factor, VEGF), endothelial cells can transiently transdifferentiate in filopodia-rich cells, named tip cells, from which angiogenesis processes are locally initiated. This protrusive state based on filopodia dynamics contrasts with the lamellipodia-based endothelial cell migration on 2D substrates. Using two-photon polymerization, we generated 3D microstructures triggering endothelial phenotypes evocative of tip cell behaviour. Hexagonal lattices on pillars (“open”), but not “closed” hexagonal lattices, induced engagement from the endothelial monolayer with the generation of numerous filopodia. The development of image analysis tools for filopodia tracking allowed to probe the influence of the microtopography (pore size, regular vs.<em> </em>elongated structures, role of the pillars) on orientations, engagement and filopodia dynamics, and to identify MLCK (myosin light-chain kinase) as a key player for filopodia-based protrusive mode. Importantly, these events occurred independently of VEGF treatment, suggesting that the observed phenotype was induced through microtopography. These microstructures are proposed as a model research tool for understanding endothelial cell behaviour in 3D fibrillary networks.
description: Funding: This work was supported by a doctoral grant from the program "Interfaces pour le Vivant", Sorbonne Université (recipient P.U.) and a doctoral grant from the Oversea Study Program of Guangzhou Elite Project (recipient X.J.). This work was supported by the Centre National de la Recherche Scientifique (CNRS), Inserm (Institut National de la Santé et de la Recherche Médicale), Sorbonne Université, Chimie ParisTech and Institut Curie. This work received support from grant ANR-08-PCVI-0012-02.
DOI: 10.5281/zenodo.6606672
title: JGR2022BGS

description: The datasets for Nagatomo et al. (2022)
DOI: 10.5281/zenodo.6606673
title: JGR2022BGS

description: The datasets for Nagatomo et al. (2022)
DOI: 10.5281/zenodo.7273833
title: JGR2022BGS

description: The datasets for Nagatomo et al. (2022)
DOI: 10.5519/qd.zgrbsftw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1541 records)
DOI: 10.5281/zenodo.894274
title: Lab513/CyberSwitch: Real time control of a genetic toggle switch

description: Release 1.0 | CyberSwitch | Master Branch This repository contains the code and data that were used in the paper: Lugagne, J.-B., Carillo, S. S., Kirch, M., Köhler, A., Batt, G., & Hersen, P. (2017). Balancing a genetic toggle switch by real-time feedback control and periodic forcing. Nature Communications. This article is accessible in open access : http://rdcu.be/A0lH
description: {"references": ["Lugagne, J.-B. et al., Balancing a genetic toggle switch by real-time feedback control and periodic forcing. Nature Communications (2017) 8: 1671; DOI: 10.1038/s41467-017-01498-0"]}