Added better defaults for assets

public/static/assets/development/SubmissionInstructions.md

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-# Submission Instructions
+# To edit the contents of this element you will need to modify the SubmissionInstructions.md asset file
 
-### Validation Data Structure
-
-For each AC power stream analyzed, there are two sets of data:
-1) The associated time series data for the AC power, in kW, and an index for timezone-aware datetime.
-2) The time shift validation file, which gives the associated time shift amount on a daily basis. A daily timezone-unaware timestamp is used as the associated index. 
-
-An example time series and its associated time shift data are shown in Figures XX and XX, respectively. An example dataset is provided for reference here.
-
-### File Submission Requirements
-
-The PV Validation Hub only accepts .zip file submissions. Each zip file must containing the following files:
-1) **submission_wrapper.py**: This Python file contains the master function for running the routines. Please refer to **Submitted Function Structure** below for more information on the associated function structure.
-2) **requirements.txt**: This text file contains the associated python packages (and versions, if applicable) to pip-install in order to run the submission_wrapper.py file.
-3) If applicable, any additional .py files containing support functions for **submission_wrapper.py**
-
-### Submitted Function Structure
-
-In the **submission_wrapper.py** file, a standard function name must be used when validating a submitted algorithm. The standard function definition, which is required to run the validation, is as follows:
-
-```
-def detect_time_shifts(time_series,
-                       latitude=None, longitude=None,
-                       data_sampling_frequency=None):
-    """
-    This is the required function definition for running time shift validation.
-    
-    Parameters 
-    ----------
-    time_series: Required argument. Pandas series with a tz-aware datetime index.
-        This is the AC power time series that is being tested for time shifts.
-    latitude: Optional argument. Float.
-        The associated latitude coordinate of the PV site that the AC power stream is associated with.
-    longitude: Optional argument. Float.
-        The associated longitude coordinate of the PV site that the AC power stream is associated with.
-    data_sampling_frequency: Optional argument. Float.
-        The data frequency of the time series being evaluated. 
-    
-    Returns
-    -------
-    time_shift_series: A pandas series with a tz-unaware date index of daily time shift amounts in minutes.
-    See validation data above as reference.
-    """
-    [YOUR ALGORITHM HERE]
-    return time_shift_series
-```
-
-### Analysis Results

public/static/assets/development/dataset.md

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-Words
+# To edit the contents of this element you will need to modify the dataset.md asset file

public/static/assets/development/description.md

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-
-# Description
-Measured photovolataic (PV) data from fielded systems may be incorrectly labeled by time zone, erroneously
-contain daylight savings time (DST), or have time shift issues due to operator error. Correcting time shifts in PV data is essential for accurate solar performance analysis, including degradation analysis. To illustrate this associated issue, Figures 1, 2, and 3, respectively, show AC power data streams heatmapped by hour of the day for the following scenarios: 
-1) No issues
-2) DST is present
-3) A random time shift is present
-
-![image info](./time_shift_no_issues.png)
-*Heatmapped AC power stream with no time shift issues.*
-
-![image info](./daylight_savings_time.png)
-*Heatmapped AC power stream with daylight savings time.*
-
-![image info](./random_time_shift.png)
-*Heatmapped AC power stream with a random time shift.*
-
-Ideally, we want to run analysis on an AC power data stream like Figure 1, where no random time shifts or DST is present.
-
-This analysis benchmarks algorithm performance for finding time shifts in PV data and correcting them accordingly. To do this, real-world PV data sets are manipulated to create common time shift issues, such as incorrect time zone, partial DST, full DST, and random time shifts. All ground truth time shift issues for each data set are recorded, and used here to validate submitted time shift detection algorithms. In particular, the associated time shift amount at each value in the predicted time series will be compared to the associated ground-truth time shift value. 
-
+# To edit the contents of this element you will need to modify the description.md asset file

public/static/assets/development/shortdesc.md

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-Estimate azimuth and tilt values for fixed-tilt PV systems.
-
-
+# To edit the contents of this element you will need to modify the shortdesc.md asset file