โ™ป๏ธWorkflow

#โš™๏ธ #๐Ÿ—บ๏ธ

Clinic WF

LLM Prompt

Research WF

!2021-10-24 14-25-12.excalidraw

Gantt chart๋กœ ์ง„ํ–‰์‚ฌํ•ญ ํ™•์ธ

๊ฐœ์š”

templates

๐Ÿ“ˆ Research Gantt Chart#Gantt Format

Research Tools

Zotero

Installation & Env. setting

Research env#5. Zotero

Project managements

์กฐํ…Œ๋กœ ๊ธฐ๋ณธ ์‚ฌ์šฉ workflow

Indexing ideas within same projects from multiple articles

์—ฌ๋Ÿฌ ๋…ผ๋ฌธ์˜ ์ •๋ณด๋ฅผ ๋น ๋ฅด๊ฒŒ ์ธ๋ฑ์‹ฑํ•˜๋Š” workflow

refs in core ref.

์ค‘์‹ฌ์ด ๋˜๋Š” ์ €๋„์— ์ธ์šฉ๋œ ๋…ผ๋ฌธ๋“ค์„ ๊ฐ™์ด ์ธ์šฉํ•  ๋•Œ์˜ workflow

CSL

- ๊ด„ํ˜ธ ๋ฐ–์—์„œ ๊ตฌ๋ถ„ํ•˜๊ธฐ
	<layout delimiter=", ">
		<group prefix="[" suffix="]" delimiter=", ">
        <text variable="citation-number"/>
        <text macro="citation-locator"/>
      </group>
- ๊ด„ํ˜ธ ์•ˆ์—์„œ ; ๋กœ ๊ตฌ๋ถ„ํ•˜๊ธฐ
       <layout prefix="(" suffix=")" delimiter="; ">
      <group delimiter=", ">
        <group delimiter=" ">
          <text macro="author-short"/>
          <text macro="year-date"/>
        </group>
        <text macro="locator"/>
      </group>
    </layout>

<names variable="author">
  <name name-as-sort-order="all" sort-separator=" " initialize-with="" delimiter=", " delimiter-precedes-last="always"/>

delimiter-precedes-last="always"/>
        <label form="short" prefix=", "/>
        <substitute>
          <names variable="editor"/>
          <text macro="title"/>
        </substitute>
      </names>
    </group>
  </macro>
  <macro name="access">
    <choose>
      <if type="article-newspaper" match="none">
        <choose>
          <if variable="DOI">
            <text value="doi:"/>
            <text variable="DOI"/>
          </if>
          <else-if variable="URL">
            <group delimiter=". ">
              <choose>
                <if type="webpage post post-weblog" match="any">
                  <date variable="issued" prefix="Published " form="text"/>
                </if>
              </choose>

โŒจ๏ธHotkeys#Zotero

Obsidian

Plugins

Hotkey

โŒจ๏ธHotkeys#Obsidian

Mermaid

About Mermaid | Mermaid
Gantt diagrams | Mermaid

Zettelkasten

Zettelkasten#Obsidian

Overleaf

Draft management

Springer Nature

Table

\begin{table}[htbp] % h: here, t: top, b: bottom, p: page
\centering
\caption{Table Title}
\label{tab2} %๋ณธ๋ฌธ์—์„œ \ref{tab2}๋กœ ์—ฐ๊ฒฐ
\renewcommand{\arraystretch}{1.3} % ํ–‰ ๊ฐ„๊ฒฉ ๋Š˜๋ฆฌ๊ธฐ
\begin{tabular}{@{}p{1.3cm}p{1.5cm}p{1.5cm}p{1.8cm}p{2.0cm}p{1.8cm}@{}}
\toprule
\textbf{Phase} & \textbf{Active Extension (N=55)} & \textbf{Passive Extension (N=55)} & \textbf{Univariate p-value} & \textbf{Multivariate Estimate (SE)} & \textbf{Multivariate p-value} \\
\midrule
\multirow{2}{*}{APA (mV)} & -0.70 & -0.34 & 0.197 & -- & -- \\
                          & [-1.39; 0.15] & [-0.97; 0.31] & & & \\
\midrule
\multirow{2}{*}{PAPA (mV)} & -1.62 & -0.87 & $<$0.001*** & -0.77 (0.14) & $<$0.001*** \\
                          & [-2.99; -0.94] & [-1.39; -0.43] & & & \\
\midrule
\multirow{2}{*}{RPA (mV)} & 0.30 & 0.39 & 0.986 & -- & -- \\
                          & [-0.65; 0.85] & [-0.17; 0.80] & & & \\
\midrule
\multirow{2}{*}{PRPA (mV)} & 1.27 & 0.81 & 0.011* & -0.46 (0.15) & 0.002** \\
                          & [0.64; 2.38] & [0.40; 1.79] & & & \\
\bottomrule
\end{tabular}
\vspace{1ex}
{\footnotesize
Non-normally distributed continuous variables are summarized with median [interquartile range].\\
SE, standard error; APA, activity phase amplitude; PAPA, post-activity phase amplitude; RPA, return phase amplitude; PRPA, post-return phase amplitude.\\
A generalized linear mixed model (GLMM) was fitted for the multivariable analysis, incorporating the following covariates: age, sex, height, BMI, thigh circumference, and calf circumference.\\
* p-value$<$0.05, ** p-value$<$0.01, *** p-value$<$0.001.
}
\end{table}

Figure

\begin{figure}[h]
\centering
\includegraphics[width=0.9\textwidth]{Figure3.pdf} %์ฒจ๋ถ€ํ•œ ํŒŒ์ผ ๋งํฌ
\caption{\textbf{ Deep learning model structure for EAG classification.} The model takes time series data of size (201, 8, 1) as input. The initial Convolutional Layer uses a 3x3 kernel with 16 filters to extract spatial features and introduces nonlinearity through the ReLU activation function. The following MaxPooling2D layer reduces the spatial dimensions to retain important information. The second Convolutional Layer uses a 3x3 kernel and 32 filters to learn more complex features. This is followed by a Reshape layer that converts the 2D output to a 1D format, leading into Conv1D layers. The first Conv1D layer uses a kernel of size 3 with 32 filters to detect temporal patterns, followed by MaxPooling1D to reduce the time dimension. The second Conv1D layer uses a kernel of size 3 with 64 filters to learn more complex temporal patterns, with another MaxPooling1D layer to further reduce the time dimension. Finally, Fully Connected Layers, including a Flatten layer that converts data into a 1D array, and several Dense layers, allow the model to learn features and perform classification. Dropout layers are employed to prevent overfitting during training. The final output layer, with 8 neurons and a softmax activation function, performs multiclass classification.}
\label{fig3}
\end{figure}

Citations

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Research env