Update README.md

README.md

@@ -1,79 +1,15 @@
 ---
 license: cc-by-nc-4.0
+
 tags:
-- time-series
-- computer-vision
-- robotics
-- motor-control
-- longitudinal-study
-- biomechanics
-- analog-archive
-configs:
-- config_name: default
-  data_dir: "Short_Timelapses"
-  drop_labels: true
-  
+  - video
+  - time-series
+  - longitudinal-study
+  - computer-vision
+  - motor-control
+  - archival
 ---
 
+# time-lapse-artifacts
 
-# Time-Lapse-Artifacts
-
-**A 14-Year Archive of Human Physical Endurance, Biomechanics, and Visual Cognition (2012–Present)**
-
-## Dataset Summary
-
-*Time-Lapse-Artifacts* is a longitudinal video dataset documenting unmediated analog execution using ink on paper. This repository isolates fine-motor wrist mechanics from broad shoulder movements, categorizing high-fidelity time-series data by spatial constraints, temporal pacing, and biomechanical execution. It provides clean, sustained data lineages for computational research, approaching complexity as a variable of the problem itself rather than the solution.
-
-## Annotation State & Data Architecture
-
-**Current Status:** Raw / Unannotated / Continuous Ingestion
-
-## Short Time-Lapses
-Quick-reference viewing files are isolated in the `Short_Timelapses/` directory. 
-[Access the Directory Here](https://huggingface.co/datasets/maxwellinked/time-lapse-artifacts/tree/main/Short_Timelapses)
-
-Refer to `short_timelapses_index.csv` for direct file routing and timestamp metadata.
-
-This repository functions as a passive, continuous archive. The core spatial and temporal media are immutable, but researchers should approach the environment as an unstructured dataset built for direct machine parsing.
-
-* **Zero-Shot / Unannotated:** The media is provided entirely raw. There are no bounding boxes, segmentation masks, kinematic joint mappings, or frame-by-frame labels.
-* **Target Workflows:** Formatted strictly for engineering and hard science applications. Optimized for self-supervised learning (SSL), optical flow analysis, motor-control modeling, and custom feature-extraction pipelines.
-* **Passive Infrastructure:** This archive operates on a fire-and-forget data architecture, utilizing flat file-naming structures over complex metadata scripts. The primary mechanism for chronological sorting is a strict, machine-readable `Year.Month.Date` file format to support automated ingestion. Daily upload volume averages 5–10 GB.
-
-## Directory Structure & Technical Parameters
-
-To maintain pristine spatial and temporal data, the archive is strictly organized by physical and temporal execution constraints:
-
-**1. `Short_Timelapses/`** *(Viewer Index)*
-* **Content:** Highly accelerated, compressed previews. 
-* **Purpose:** Acts as a rapid visual index for the dataset without requiring the download of massive, uncompressed workflow files.
-
-**2. `Process_Workflow_4K/`**
-* **Content:** Standard 4K, high-bitrate time-lapses (6x pacing).
-* **Purpose:** Pristine spatial data. Provides AI models with uncompressed edge-detection and line-fidelity data. Denoted by the `wf.` prefix.
-
-**3. `Series_9x12/`** *(July 2025 – June 2026)*
-* **Content:** An 11-month closed ecosystem of spatial data strictly constrained to 9" x 12" dimensions.
-* **Biomechanical Data:** Strictly isolates fine-motor hand and wrist mechanics.
-
-**4. `Series_11x14/`**
-* **Content:** The chronological era and physical constraint immediately preceding the 9x12 series. Contains distinct spatial bounding and expanded forearm biomechanics.
-
-**5. `Large_Scale_30x40/`**
-* **Content:** Video documentation of 30" x 40" physical works. Denoted by the `x.` prefix.
-* **Biomechanical Data:** Wider camera framing capturing broad motor movements (shoulder, elbow, full-torso engagement). Kept strictly separate from the fine-motor datasets.
-
-**6. `Real_Time_Livestreams/`**
-* **Content:** 1x real-time pacing footage. Contains standard livestream compression.
-* **Purpose:** Pristine temporal data. Contains the exact human rhythm, hesitations, and micro-pauses necessary for temporal modeling.
-
-**7. `Legacy_Livestreams_2012_2016/`**
-* **Content:** Foundational historical broadcasts documenting the early era of this continuous practice.
-
-## Note on Data Quality Evolution (2012–Present)
-
-This archive documents 14 years of progression in both physical practice and technical documentation. Researchers should note that data quality scales chronologically:
-
-* **2012–2016 (Foundational Era):** Documentation is raw, capturing the high-variance nature of early execution. Uniquely suited for studies in domain adaptation, noise reduction, and low-fidelity temporal modeling.
-* **2017–2024 (Iterative Era):** Documentation standards stabilize, capturing the maturation of motor-control routines.
-* **2025–Present (High-Fidelity Era):** Rigorously constrained 4K capture, optimized for high-fidelity computer vision and fine-motor biomechanics analysis.
+**A 14-Year Longitudinal Archive of Analog Drawing Process (2012–Present)**