Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Civil and geological engineers have used field variable-head permeability tests (VH tests or slug tests) for over one century to assess the local hydraulic conductivity of tested soils and rocks. The water level in the pipe or riser casing reaches, after some rest time, a static position or elevation, z2. Then, the water level position is changed rapidly, by adding or removing some water volume, or by inserting or removing a solid slug. Afterward, the water level position or elevation z1(t) is recorded vs. time t, yielding a difference in hydraulic head or water column defined as Z(t) = z1(t) - z2. The water level at rest is assumed to be the piezometric level or PL for the tested zone, before drilling a hole and installing test equipment. All equations use Z(t) or Z*(t) = Z(t) / Z(t=0). The water-level response vs. time may be a slow return to equilibrium (overdamped test), or an oscillation back to equilibrium (underdamped test). This document deals exclusively with overdamped tests. Their data may be analyzed using several methods, known to yield different results for the hydraulic conductivity. The methods fit in three groups: group 1 neglects the influence of the solid matrix strain, group 2 is for tests in aquitards with delayed strain caused by consolidation, and group 3 takes into account some elastic and instant solid matrix strain. This document briefly explains what is wrong with certain theories and why. It shows three ways to plot the data, which are the three diagnostic graphs. According to experience with thousands of tests, most test data are biased by an incorrect estimate z2 of the piezometric level at rest. The derivative or velocity plot does not depend upon this assumed piezometric level, but can verify its correctness. The document presents experimental results and explains the three-diagnostic graphs approach, which unifies the theories and, most important, yields a user-independent result. Two free spreadsheet files are provided. The spreadsheet "Lefranc-Test-English-Model" follows the Canadian standards and is used to explain how to treat correctly the test data to reach a user-independent result. The user does not modify this model spreadsheet but can make as many copies as needed, with different names. The user can treat any other data set in a copy, and can also modify any copy if needed. The second Excel spreadsheet contains several sets of data that can be used to practice with the copies of the model spreadsheet. En génie civil et géologique, on a utilisé depuis plus d'un siècle les essais in situ de perméabilité à niveau variable (essais VH ou slug tests), afin d'évaluer la conductivité hydraulique locale des sols et rocs testés. Le niveau d'eau dans le tuyau ou le tubage prend, après une période de repos, une position ou élévation statique, z2. Ensuite, on modifie rapidement la position du niveau d'eau, en ajoutant ou en enlevant rapi-dement un volume d'eau, ou en insérant ou retirant un objet solide. La position ou l'élévation du niveau d'eau, z1(t), est alors notée en fonction du temps, t, ce qui donne une différence de charge hydraulique définie par Z(t) = z1(t) - z2. Le niveau d'eau au repos est supposé être le niveau piézométrique pour la zone testée, avant de forer un trou et d'installer l'équipement pour un essai. Toutes les équations utilisent Z(t) ou Z*(t) = Z(t) / Z(t=0). La réponse du niveau d'eau avec le temps peut être soit un lent retour à l'équilibre (cas suramorti) soit une oscillation amortie retournant à l'équilibre (cas sous-amorti). Ce document ne traite que des cas suramortis. Leurs données peuvent être analysées à l'aide de plusieurs méthodes, connues pour donner des résultats différents pour la conductivité hydraulique. Les méthodes appartiennent à trois groupes : le groupe 1 néglige l'influence de la déformation de la matrice solide, le groupe 2 est pour les essais dans des aquitards avec une déformation différée causée par la consolidation, et le groupe 3 prend en compte une certaine déformation élastique et instantanée de la matrice solide. Ce document explique brièvement ce qui est incorrect dans les théories et pourquoi. Il montre trois façons de tracer les données, qui sont les trois graphiques de diagnostic. Selon l'expérience de milliers d'essais, la plupart des données sont biaisées par un estimé incorrect de z2, le niveau piézométrique supposé. Le graphe de la dérivée ou graphe des vitesses ne dépend pas de la valeur supposée pour le niveau piézomé-trique, mais peut vérifier son exactitude. Le document présente des résultats expérimentaux et explique le diagnostic à trois graphiques, qui unifie les théories et donne un résultat indépendant de l'utilisateur, ce qui est important. Deux fichiers Excel gratuits sont fournis. Le fichier"Lefranc-Test-English-Model" suit les normes canadiennes : il sert à expliquer comment traiter correctement les données d'essai pour avoir un résultat indépendant de l'utilisateur. Celui-ci ne modifie pas ce...

Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?:

This data set contains all the experimental raw data, analysis and source files for the final figures reported in the manuscript: "Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?". It is divided into five (1-5) zipped folders, named as the technique used to obtain the data. Each of them, where applicable, consists of three different subfolders (raw data, analysed data, final graph). Read below for more details.

1) ConfocalMicroscopy

1a) Raw_Data: the raw images are reported as .dat and .tif formats, divided into folders (according to date first yymmdd, and within the same day according to composition). Each folder contains a .txt file reporting the experimental details

1b) GUVs_Statistics - GUVs_Statistics.txt explains how we generated the bar plot shown in Fig. 1E

1c) Final_Graph - Figure_1B_1D.png is the figure representing figure 1B and 1D - Figure1E_%ofGUVswithCaMAdsorbptions.csv is the source file x-y of the bar plot shown in figure 1E (% of GUVs which showed adsorption of CaM over the total amount of measured GUVs) - Where_To_Find_Representative_Images.txt states the folders where the raw images chosen for figure 1 can be found

2) FCS 2a) Raw_Data: - 1_points: .ptu files - 2_points: .ht3 files - Raw_Data_Description.docx which compositions and conditions correspond to which point in the two data sets 2b) Final_Graphs: - Figure_2A.xlsx contains the x-y source file for figure 2A

2c) Analysis: - FCS_Fits.xlsx outcome of the global fitting procedure described in the .docx below (each group of points represents a certain composition and calcium concentration, read the Raw_Data_Description.docx in the FCS > Raw_Data) - Notes_for_FCS_Analysis.docx contains a brief description of the analysis of the autocorrelation curves

3) GPLaurdan 3a) Raw Data: all the spectra are stored in folders named by date (yymmdd_lipidcomposition_Laurdan) and are in both .FS and .txt formats

3b) GP calculations: contains all the .xlsx files calculating the GP values from the raw emission and excitation spectra

3c) Final_Graphs - Data_Processing_For_Fig_2D.csv contains the data processing from the GP values calculated from the spectra to the DeltaGP (GP with- GP without CaM) reported in fig. 2D - Figure_2C_2D.xlsx contains the x-y source file for the figure 2C and 2D

4) LiveCellsImaging

3a) Intensity_Protrusions_vs_Cell_Body: - contains all the .xlsx files calculating the intensity of the various images. File renamed by date (yymmdd) - All data in all excel sheets gathered in another Excel file to create a final graph

3b) Final_Graphs - Figure_S2B.xlsx contains the x-y source file for the figure S2B

5) LiveCellImaging_Raw_Data: it contains some of the images, which are given in .tif. They are divided by date (yymmdd) and each contains subfolders renamed by sample name, concentration of ionomycin. Within the subfolders, the images are divided into folders distinguishing the data acquired before and after the ionomycin treatment and the incubation time.

6) 211124_BioCev_Imaging_1 folder has the .jpg files of the time laps, these are shown in fig 1A and S2.

7) 211124_BioCev_Imaging_2 and 8) 211124_BioCev_Imaging_3 contain the images of HeLa cells expressing EGFP-CaM after treatment with ionomycin 200 nM (A1) and 1 uM (A2), respectively.

9) SPR

9a) Raw Data: - SPR_Raw_Data.xlsx x/y exported sensorgrams - the .jpg files of the software are also reported and named by lipid composition

9b) Final_Graph: - Fig.2B.xlsx contains the x-y source file for the figure 2B

9c) Analysis - SPR_Analysis.xlsx: excel file containing step-by-step (sheet by sheet) how we processed the raw data to obtain the final figure (details explained in the .docx below) - Analysis of SPR data_notes.docx: read me for detailed explanation

-- About Thagri: The National Electronics and Computer Technology Center (NECTEC) has designed and developed an Open Data Platform with the objective of providing Thailand with a central repository of Open Data that is beneficial for use by Application Developers and Data Scientists. This aims to spark creativity among students, researchers, developers, and those interested in using the data to further develop and extend its utility endlessly, in accordance with the intention of creating Open Innovation with Open Data. Open Data is data in a Machine-readable Data format that is open for use without charge (Open License). Recognizing the increasing importance of Open Data, govtech.in.th will serve as another channel, acting as a central hub to support the provision of open data from both the public and private sectors, providing a channel to publish Open Data sets. A key feature is that the data published through this website will be automatically converted into a RESTFul API (Application Programming Interface). It emphasizes providing those interested in Data Science with tools to support data analysis and report creation in various types of graphs (Data Visualization tools) from open data, such as creating graphs for time-series data and heatmaps for spatial statistics data at the provincial, district, and sub-district levels of Thailand, using a drill-down method on a hierarchical map (treemap). -- About Platform: It focuses on enabling users who are application developers to access data in each dataset through an API (data API), which will allow application developers to access data more flexibly compared to disclosing data in excel or CSV spreadsheet files that application developers have to download each file and process it themselves. It focuses on enabling users who are researchers or data analysts to flexibly analyze and visualize open dataset data (data analysis and visualization) through the website and be able to publish the analysis results in graph form via online media (data visualization sharing) or incorporate them into articles on web pages (data visualization embedding) with greater flexibility compared to disclosing data in excel or CSV spreadsheet files that researchers or data analysts have to download each file and analyze the data themselves through external programs. It focuses on enabling users who are data publishers to disclose data in a way that can be easily used and further developed (improved data accessibility). Data publishers can prepare data in CSV spreadsheet files, and the system will automatically convert the data so that users can access it in the ways mentioned in points 1 and 2. In addition, the CSV data file will be checked to ensure that it is in a suitable table data format (dataset validation). If the check passes, it means that the dataset is in a machine-readable data format. If it is not in a suitable format, it will not be able to be imported into the system, thus making the open data provided of higher quality compared to disclosing data in excel or CSV spreadsheet files, where there is normally no quality check of the dataset files to ensure that they are actually in a machine-readable format. Translated from Thai Original Text: -- About Thagri ศูนย์เทคโนโลยีอิเล็กทรอนิกส์และคอมพิวเตอร์แห่งชาติ (เนคเทค) ออกแบบและพัฒนาแพลตฟอร์มข้อมูล แบบเปิด (Open Data Platform) โดยมีวัตถุปะสงค์ให้ประเทศไทยได้มีแหล่งรวมชุดข้อมูลแบบเปิด (Open Data) ที่เป็นประโยชน์ต่อการนำไปใช้โดยนักพัฒนาโปรแกรม (Application Developers) และนักวิทยาศาสตร์ข้อมูล(Data Scientists) ช่วยจุดประกายความคิดสร้างสรรค์ให้กับนิสิตนักศึกษานักวิจัยนักพัฒนา และผู้สนใจนำข้อมูลไปพัฒนาต่อยอดใช้ประโยชน์อย่างไม่มีที่สิ้นสุดตามเจตนารมณ์ของการสร้างนวัตกรรมแบบเปิด (Open Innovation)ด้วยข้อมูลแบบเปิด (Open Data) ข้อมูลเปิด (Open Data) คือ ข้อมูลในแบบที่เครื่องคอมพิวเตอร์สามารถประมวลผลได้ (Machine-readable Data) ที่เปิดให้นำไปใช้ประโยชน์ได้โดยไม่คิดมูลค่า(Open License) จากความ ตระหนักในความสำคัญของข้อมูลแบบเปิดที่มีมากขึ้น govtech.in.th จะเป็นอีกหนึ่งช่องทางทำหน้าที่ เป็นศูนย์กลางรองรับการให้บริการเผยแพร่ข้อมูลแบบเปิด ทั้งจากภาครัฐและภาคเอกชนได้มีช่องทาง เผยแพร่ชุดข้อมูลแบบเปิด (Publish) ด้วยจุดเด่นคือ ชุดข้อมูลที่เผยแพร่ผ่านเว็บไซต์นี้จะถูกแปลง เป็น API (Application Programming Interface) ชนิด RESTFul API ให้อย่างอัตโนมัติ เน้นให้ผู้สนใจ ด้านวิทยาการข้อมูล (Data Science) ได้มีเครื่องมือสนับสนุนการวิเคราะห์ข้อมูลและสร้างรายงานใน แบบกราฟชนิดต่างๆ(Data Visualization tools) จากข้อมูลแบบเปิด เช่น การสร้างกราฟสำหรับ ข้อมูลในแบบเวลา (time-series) และสำหรับข้อมูลสถิติเชิงพื้นที่ (heatmap) ในระดับจังหวัด อำเภอ และตำบลของประเทศไทย ด้วยวิธีการ drill-down บนแผนที่แบบลำดับชั้น (treemap) -- About Platform เน้นให้ผู้ใช้ที่เป็นนักพัฒนาแอปพลิเคชัน สามารถเข้าถึงข้อมูลในแต่ละชุดข้อมูลได้ผ่าน API (data API) ซึ่งจะช่วยให้นักพัฒนาแอปพลิเคชันสามารถเข้าถึงข้อมูลได้อย่างยืดหยุ่นยิ่งขึ้น เมื่อเทียบการเปิดเผยข้อมูลในแบบไฟล์ ตารางคำนวณแบบ excel หรือ CSV ที่นักพัฒนาแอปพลิเคชันต้องดาวน์โหลดแต่ละไฟล์ไปประมวลผลด้วยตนเอง เน้นให้ผู้ใช้ที่เป็นนักวิจัยหรือนักวิเคราะห์ข้อมูล (researcher/ data analyst) สามารถวิเคราะห์และแสดงผลข้อมูล (data analysis and visualization) ชุดข้อมูลเปิดได้อย่างยืดหยุ่นผ่านเว็บไซต์และสามารถนำผลการวิเคราะห์ ในแบบกราฟไปเผยแพร่ผ่านสื่อออนไลน์ (data visualization sharing) หรือ นำไปประกอบบทความในหน้าเว็บต่างๆ ได้ (data visualization embedding) อย่างยืดหยุ่นมากกว่า เมื่อเทียบการเปิดเผยข้อมูลในแบบไฟล์ตาราง คำนวณแบบ excel หรือ CSVที่นักวิจัยหรือนักวิเคราะห์ข้อมูลต้องดาวน์โหลดแต่ละไฟล์ไปวิเคราะห์ข้อมูลด้วยตนเองผ่านโปรแกรมภายนอก เน้นให้ผู้ใช้ที่เป็นผู้เผยแพร่ข้อมูล สามารถเปิดเผยข้อมูลในแบบที่สามารถนำไปใช้ประโยชน์และต่อยอดได้ง่ายยิ่งขึ้น (improved data accessibility) โดยผู้เผยแพร่ข้อมูลสามารถเตรียมข้อมูลในแบบไฟล์ตารางคำนวณชนิด CSV โดยระบบจะทำการแปลงข้อมูลให้ผู้ใช้สามารถเข้าถึงในแบบที่กล่าวมาในข้อ 1 และ 2 ได้อย่างอัตโนมัติ ทั้งนี้ไฟล์ข้อมูล CSV จะถูกตรวจสอบว่าอยู่ในแบบข้อมูลตาราง (dataset validation) ที่เหมาะสมหรือไม่ ซึ่งหากตรวจสอบผ่าน