How To Cite?

Alinaghi, N., Giannopoulos, I., Kattenbeck, M., & Raubal, M. (2025). Decoding wayfinding: analyzing wayfinding processes in the outdoor environment. International Journal of Geographical Information Science, 1–31. https://doi.org/10.1080/13658816.2025.2473599

Link to the paper: https://www.tandfonline.com/doi/full/10.1080/13658816.2025.2473599

Folder Structure

The folder named “submission” contains the following:

1. “pythonProject”: This folder contains all the Python files and subfolders needed for analysis.
2. ijgis.yml: This file lists all the Python libraries and dependencies required to run the code.

Setting Up the Environment

1. Use the ijgis.yml file to create a Python project and environment. Ensure you activate the environment before running the code.
2. The pythonProject folder contains several .py files and subfolders, each with specific functionality as described below.

Subfolders

1. Data_4_IJGIS

• This folder contains the data used for the results reported in the paper.
• Note: The data analysis that we explain in this paper already begins with the synchronization and cleaning of the recorded raw data. The published data is already synchronized and cleaned. Both the cleaned files and the merged files with features extracted for them are given in this directory. If you want to perform the segmentation and feature extraction yourself, you should run the respective Python files yourself. If not, you can use the “merged_…csv” files as input for the training.

2. results_[DateTime] (e.g., results_20240906_15_00_13)

• This folder will be generated when you run the code and will store the output of each step.
• The current folder contains results created during code debugging for the submission.
• When you run the code, a new folder with fresh results will be generated.

Python Files

1. helper_functions.py

• Contains reusable functions used throughout the analysis.
• Each function includes a description of its purpose and the input parameters required.

2. create_sanity_plots.py

• Generates scatter plots like those in Figure 3 of the paper.
• Although the code has been run for all 309 trials, it can be used to check the sample data provided.
• Output: A .png file for each column of the raw gaze and IMU recordings, color-coded with logged events.
• Usage: Run this file to create visualizations similar to Figure 3.

3. overlapping_sliding_window_loop.py

• Implements overlapping sliding window segmentation and generates plots like those in Figure 4.
• Output:
  • Two new subfolders, “Gaze” and “IMU”, will be added to the Data_4_IJGIS folder.
  • Segmented files (default: 2–10 seconds with a 1-second step size) will be saved as .csv files.
  • A visualization of the segments, similar to Figure 4, will be automatically generated.

4. gaze_features.py & imu_features.py (Note: there has been an update to the IDT function implementation in the gaze_features.py on 19.03.2025.)

• These files compute features as explained in Tables 1 and 2 of the paper, respectively.
• They process the segmented recordings generated by the overlapping_sliding_window_loop.py.
• Usage: Just to know how the features are calculated, you can run this code after the segmentation with the sliding window and run these files to calculate the features from the segmented data.

5. training_prediction.py

• This file contains the main machine learning analysis of the paper. This file contains all the code for the training of the model, its evaluation, and its use for the inference of the “monitoring part”. It covers the following steps:

a. Data Preparation (corresponding to Section 5.1.1 of the paper)

• Prepares the data according to the research question (RQ) described in the paper. Since this data was collected with several RQs in mind, we remove parts of the data that are not related to the RQ of this paper.
• A function named plot_labels_comparison(df, save_path, x_label_freq=10, figsize=(15, 5)) in line 116 visualizes the data preparation results. As this visualization is not used in the paper, the line is commented out, but if you want to see visually what has been changed compared to the original data, you can comment out this line.

b. Training/Validation/Test Split

• Splits the data for machine learning experiments (an explanation can be found in Section 5.1.1. Preparation of data for training and inference of the paper).
• Make sure that you follow the instructions in the comments to the code exactly.
• Output: The split data is saved as .csv files in the results folder.

c. Machine and Deep Learning Experiments

This part contains three main code blocks:

iii. One for the XGboost code with correct hyperparameter tuning:
Please read the instructions for each block carefully to ensure that the code works smoothly. Regardless of which block you use, you will get the classification results (in the form of scores) for unseen data. The way we empirically test the confidence threshold of

• MLP Network (Commented Out): This code was used for classification with the MLP network, and the results shown in Table 3 are from this code. If you wish to use this model, please comment out the following blocks accordingly.
• XGBoost without Hyperparameter Tuning: If you want to run the code but do not want to spend time on the full training with hyperparameter tuning (as was done for the paper), just uncomment this part. This will give you a simple, untuned model with which you can achieve at least some results.
• XGBoost with Hyperparameter Tuning: If you want to train the model the way we trained it for the analysis reported in the paper, use this block (the plots in Figure 7 are from this block). We ran this block with different feature sets and different segmentation files and created a simple bar chart from the saved results, shown in Figure 6.

Note: Please read the instructions for each block carefully to ensure that the code works smoothly. Regardless of which block you use, you will get the classification results (in the form of scores) for unseen data. The way we empirically calculated the confidence threshold of the model (explained in the paper in Section 5.2. Part II: Decoding surveillance by sequence analysis) is given in this block in lines 361 to 380.

d. Inference (Monitoring Part)

• Final inference is performed using the monitoring data. This step produces a .csv file containing inferred labels.
• Figure 8 in the paper is generated using this part of the code.

6. sequence_analysis.py

• Performs analysis on the inferred data, producing Figures 9 and 10 from the paper.
• This file reads the inferred data from the previous step and performs sequence analysis as described in Sections 5.2.1 and 5.2.2.

Licenses

The data is licensed under CC-BY, the code is licensed under MIT.

CodeParrot 🦜 Dataset Cleaned

  What is it?

A dataset of Python files from Github. This is the deduplicated version of the codeparrot.

  Processing

The original dataset contains a lot of duplicated and noisy data. Therefore, the dataset was cleaned with the following steps:

Deduplication Remove exact matches

Filtering Average line length < 100 Maximum line length < 1000 Alpha numeric characters fraction > 0.25 Remove auto-generated files (keyword search)

For… See the full description on the dataset page: https://huggingface.co/datasets/codeparrot/codeparrot-clean.

Dataset

This dataset was created by Eric R.

Contents

US Census Bureau conducts American Census Survey 1 and 5 Yr surveys that record various demographics and provide public access through APIs. I have attempted to call the APIs through the python environment using the requests library, Clean, and organize the data in a usable format.

Data Ingestion and Cleaning:

ACS Subject data [2011-2019] was accessed using Python by following the below API Link: https://api.census.gov/data/2011/acs/acs1?get=group(B08301)&for=county:* The data was obtained in JSON format by calling the above API, then imported as Python Pandas Dataframe. The 84 variables returned have 21 Estimate values for various metrics, 21 pairs of respective Margin of Error, and respective Annotation values for Estimate and Margin of Error Values. This data was then undergone through various cleaning processes using Python, where excess variables were removed, and the column names were renamed. Web-Scraping was carried out to extract the variables' names and replace the codes in the column names in raw data.

The above step was carried out for multiple ACS/ACS-1 datasets spanning 2011-2019 and then merged into a single Python Pandas Dataframe. The columns were rearranged, and the "NAME" column was split into two columns, namely 'StateName' and 'CountyName.' The counties for which no data was available were also removed from the Dataframe. Once the Dataframe was ready, it was separated into two new dataframes for separating State and County Data and exported into '.csv' format

Data Source:

More information about the source of Data can be found at the URL below: US Census Bureau. (n.d.). About: Census Bureau API. Retrieved from Census.gov https://www.census.gov/data/developers/about.html

Final Word:

I hope this data helps you to create something beautiful, and awesome. I will be posting a lot more databases shortly, if I get more time from assignments, submissions, and Semester Projects 🧙🏼‍♂️. Good Luck.

python scripts and functions needed to view and clean saccade data

Copies of Anaconda 3 Jupyter Notebooks and Python script for holistic and clustered analysis of "The Impact of COVID-19 on Technical Services Units" survey results. Data was analyzed holistically using cleaned and standardized survey results and by library type clusters. To streamline data analysis in certain locations, an off-shoot CSV file was created so data could be standardized without compromising the integrity of the parent clean file. Three Jupyter Notebooks/Python scripts are available in relation to this project: COVID_Impact_TechnicalServices_HolisticAnalysis (a holistic analysis of all survey data) and COVID_Impact_TechnicalServices_LibraryTypeAnalysis (a clustered analysis of impact by library type, clustered files available as part of the Dataverse for this project).

The dataset is gathered on Sep. 17th 2020 from GitHub. It has more than 5.2K Python repositories and 4.2M type annotations. The dataset is also de-duplicated using the CD4Py tool. Check out the README.MD file for the description of the dataset. Notable changes to each version of the dataset are documented in CHANGELOG.md. The dataset's scripts and utilities are available on its GitHub repository.

Hello data people ! 😄

This is the BBC news dataset (cleaned version) which I have uploaded after my previous dataset post. The original dataset downloaded from the UCI Machine Learning Repository was unclean. The dataset was cleaned by extracting the keywords from the description column into the noisy 'keys' column data.

About the Dataset 🔢

The BBC news dataset consists of the following data 1. # - News ID. 2. descr - description/detail of the news provided. 3. tags - the tags/keywords related to the corresponding news in the 'descr' label.

This tar file contains all 100 trained models in the MME-only ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data). To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.

The dataset is gathered on Sep. 17th 2020 from GitHub. It has clean and complete versions (from v0.7): The clean version has 5.1K type-checked Python repositories and 1.2M type annotations. The complete version has 5.2K Python repositories and 3.3M type annotations. The dataset's source files are type-checked using mypy (clean version). The dataset is also de-duplicated using the CD4Py tool. Check out the README.MD file for the description of the dataset. Notable changes to each version of the dataset are documented in CHANGELOG.md. The dataset's scripts and utilities are available on its GitHub repository. {"references": ["A. Mir, E. Latoskinas and G. Gousios, "ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-Based Type Inference," in 2021 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), 2021 pp. 585-589. doi: 10.1109/MSR52588.2021.00079"]}

The EPA GitHub repository PAU4ChemAs as described in the README.md file, contains Python scripts written to build the PAU dataset modules (technologies, capital and operating costs, and chemical prices) for tracking chemical flows transfers, releases estimation, and identification of potential occupation exposure scenarios in pollution abatement units (PAUs). These PAUs are employed for on-site chemical end-of-life management. The folder datasets contains the outputs for each framework step. The Chemicals_in_categories.csv contains the chemicals for the TRI chemical categories. The EPA GitHub repository PAU_case_study as described in its readme.md entry, contains the Python scripts to run the manuscript case study for designing the PAUs, the data-driven models, and the decision-making module for chemicals of concern and tracking flow transfers at the end-of-life stage. The data was obtained by means of data engineering using different publicly-available databases. The properties of chemicals were obtained using the GitHub repository Properties_Scraper, while the PAU dataset using the repository PAU4Chem. Finally, the EPA GitHub repository Properties_Scraper contains a Python script to massively gather information about exposure limits and physical properties from different publicly-available sources: EPA, NOAA, OSHA, and the institute for Occupational Safety and Health of the German Social Accident Insurance (IFA). Also, all GitHub repositories describe the Python libraries required for running their code, how to use them, the obtained outputs files after running the Python script modules, and the corresponding EPA Disclaimer. This dataset is associated with the following publication: Hernandez-Betancur, J.D., M. Martin, and G.J. Ruiz-Mercado. A data engineering framework for on-site end-of-life industrial operations. JOURNAL OF CLEANER PRODUCTION. Elsevier Science Ltd, New York, NY, USA, 327: 129514, (2021).

Part of the dissertation Pitch of Voiced Speech in the Short-Time Fourier Transform: Algorithms, Ground Truths, and Evaluation Methods.© 2020, Bastian Bechtold. All rights reserved. Estimating the fundamental frequency of speech remains an active area of research, with varied applications in speech recognition, speaker identification, and speech compression. A vast number of algorithms for estimatimating this quantity have been proposed over the years, and a number of speech and noise corpora have been developed for evaluating their performance. The present dataset contains estimated fundamental frequency tracks of 25 algorithms, six speech corpora, two noise corpora, at nine signal-to-noise ratios between -20 and 20 dB SNR, as well as an additional evaluation of synthetic harmonic tone complexes in white noise.The dataset also contains pre-calculated performance measures both novel and traditional, in reference to each speech corpus’ ground truth, the algorithms’ own clean-speech estimate, and our own consensus truth. It can thus serve as the basis for a comparison study, or to replicate existing studies from a larger dataset, or as a reference for developing new fundamental frequency estimation algorithms. All source code and data is available to download, and entirely reproducible, albeit requiring about one year of processor-time.Included Code and Data

ground truth data.zip is a JBOF dataset of fundamental frequency estimates and ground truths of all speech files in the following corpora:

CMU-ARCTIC (consensus truth) [1]FDA (corpus truth and consensus truth) [2]KEELE (corpus truth and consensus truth) [3]MOCHA-TIMIT (consensus truth) [4]PTDB-TUG (corpus truth and consensus truth) [5]TIMIT (consensus truth) [6]

noisy speech data.zip is a JBOF datasets of fundamental frequency estimates of speech files mixed with noise from the following corpora:NOISEX [7]QUT-NOISE [8]

synthetic speech data.zip is a JBOF dataset of fundamental frequency estimates of synthetic harmonic tone complexes in white noise.noisy_speech.pkl and synthetic_speech.pkl are pickled Pandas dataframes of performance metrics derived from the above data for the following list of fundamental frequency estimation algorithms:AUTOC [9]AMDF [10]BANA [11]CEP [12]CREPE [13]DIO [14]DNN [15]KALDI [16]MAPSMBSC [17]NLS [18]PEFAC [19]PRAAT [20]RAPT [21]SACC [22]SAFE [23]SHR [24]SIFT [25]SRH [26]STRAIGHT [27]SWIPE [28]YAAPT [29]YIN [30]

noisy speech evaluation.py and synthetic speech evaluation.py are Python programs to calculate the above Pandas dataframes from the above JBOF datasets. They calculate the following performance measures:Gross Pitch Error (GPE), the percentage of pitches where the estimated pitch deviates from the true pitch by more than 20%.Fine Pitch Error (FPE), the mean error of grossly correct estimates.High/Low Octave Pitch Error (OPE), the percentage pitches that are GPEs and happens to be at an integer multiple of the true pitch.Gross Remaining Error (GRE), the percentage of pitches that are GPEs but not OPEs.Fine Remaining Bias (FRB), the median error of GREs.True Positive Rate (TPR), the percentage of true positive voicing estimates.False Positive Rate (FPR), the percentage of false positive voicing estimates.False Negative Rate (FNR), the percentage of false negative voicing estimates.F₁, the harmonic mean of precision and recall of the voicing decision.

Pipfile is a pipenv-compatible pipfile for installing all prerequisites necessary for running the above Python programs.

The Python programs take about an hour to compute on a fast 2019 computer, and require at least 32 Gb of memory.References:

John Kominek and Alan W Black. CMU ARCTIC database for speech synthesis, 2003.Paul C Bagshaw, Steven Hiller, and Mervyn A Jack. Enhanced Pitch Tracking and the Processing of F0 Contours for Computer Aided Intonation Teaching. In EUROSPEECH, 1993.F Plante, Georg F Meyer, and William A Ainsworth. A Pitch Extraction Reference Database. In Fourth European Conference on Speech Communication and Technology, pages 837–840, Madrid, Spain, 1995.Alan Wrench. MOCHA MultiCHannel Articulatory database: English, November 1999.Gregor Pirker, Michael Wohlmayr, Stefan Petrik, and Franz Pernkopf. A Pitch Tracking Corpus with Evaluation on Multipitch Tracking Scenario. page 4, 2011.John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallett, Nancy L. Dahlgren, and Victor Zue. TIMIT Acoustic-Phonetic Continuous Speech Corpus, 1993.Andrew Varga and Herman J.M. Steeneken. Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recog- nition systems. Speech Communication, 12(3):247–251, July 1993.David B. Dean, Sridha Sridharan, Robert J. Vogt, and Michael W. Mason. The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms. Proceedings of Interspeech 2010, 2010.Man Mohan Sondhi. New methods of pitch extraction. Audio and Electroacoustics, IEEE Transactions on, 16(2):262—266, 1968.Myron J. Ross, Harry L. Shaffer, Asaf Cohen, Richard Freudberg, and Harold J. Manley. Average magnitude difference function pitch extractor. Acoustics, Speech and Signal Processing, IEEE Transactions on, 22(5):353—362, 1974.Na Yang, He Ba, Weiyang Cai, Ilker Demirkol, and Wendi Heinzelman. BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):1833–1848, December 2014.Michael Noll. Cepstrum Pitch Determination. The Journal of the Acoustical Society of America, 41(2):293–309, 1967.Jong Wook Kim, Justin Salamon, Peter Li, and Juan Pablo Bello. CREPE: A Convolutional Representation for Pitch Estimation. arXiv:1802.06182 [cs, eess, stat], February 2018. arXiv: 1802.06182.Masanori Morise, Fumiya Yokomori, and Kenji Ozawa. WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications. IEICE Transactions on Information and Systems, E99.D(7):1877–1884, 2016.Kun Han and DeLiang Wang. Neural Network Based Pitch Tracking in Very Noisy Speech. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):2158–2168, Decem- ber 2014.Pegah Ghahremani, Bagher BabaAli, Daniel Povey, Korbinian Riedhammer, Jan Trmal, and Sanjeev Khudanpur. A pitch extraction algorithm tuned for automatic speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pages 2494–2498. IEEE, 2014.Lee Ngee Tan and Abeer Alwan. Multi-band summary correlogram-based pitch detection for noisy speech. Speech Communication, 55(7-8):841–856, September 2013.Jesper Kjær Nielsen, Tobias Lindstrøm Jensen, Jesper Rindom Jensen, Mads Græsbøll Christensen, and Søren Holdt Jensen. Fast fundamental frequency estimation: Making a statistically efficient estimator computationally efficient. Signal Processing, 135:188–197, June 2017.Sira Gonzalez and Mike Brookes. PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(2):518—530, February 2014.Paul Boersma. Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In Proceedings of the institute of phonetic sciences, volume 17, page 97—110. Amsterdam, 1993.David Talkin. A robust algorithm for pitch tracking (RAPT). Speech coding and synthesis, 495:518, 1995.Byung Suk Lee and Daniel PW Ellis. Noise robust pitch tracking by subband autocorrelation classification. In Interspeech, pages 707–710, 2012.Wei Chu and Abeer Alwan. SAFE: a statistical algorithm for F0 estimation for both clean and noisy speech. In INTERSPEECH, pages 2590–2593, 2010.Xuejing Sun. Pitch determination and voice quality analysis using subharmonic-to-harmonic ratio. In Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on, volume 1, page I—333. IEEE, 2002.Markel. The SIFT algorithm for fundamental frequency estimation. IEEE Transactions on Audio and Electroacoustics, 20(5):367—377, December 1972.Thomas Drugman and Abeer Alwan. Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics. In Interspeech, page 1973—1976, 2011.Hideki Kawahara, Masanori Morise, Toru Takahashi, Ryuichi Nisimura, Toshio Irino, and Hideki Banno. TANDEM-STRAIGHT: A temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, F0, and aperiodicity estimation. In Acous- tics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on, pages 3933–3936. IEEE, 2008.Arturo Camacho. SWIPE: A sawtooth waveform inspired pitch estimator for speech and music. PhD thesis, University of Florida, 2007.Kavita Kasi and Stephen A. Zahorian. Yet Another Algorithm for Pitch Tracking. In IEEE International Conference on Acoustics Speech and Signal Processing, pages I–361–I–364, Orlando, FL, USA, May 2002. IEEE.Alain de Cheveigné and Hideki Kawahara. YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4):1917, 2002.

Data set consisting of data joined for analyzing the SBIR/STTR program. Data consists of individual awards and agency-level observations. The R and python code required for pulling, cleaning, and creating useful data sets has been included. Allard_Get and Clean Data.R This file provides the code for getting, cleaning, and joining the numerous data sets that this project combined. This code is written in the R language and can be used in any R environment running R 3.5.1 or higher. If the other files in this Dataverse are downloaded to the working directory, then this Rcode will be able to replicate the original study without needing the user to update any file paths. Allard SBIR STTR WebScraper.py This is the code I deployed to multiple Amazon EC2 instances to scrape data o each individual award in my data set, including the contact info and DUNS data. Allard_Analysis_APPAM SBIR project Forthcoming Allard_Spatial Analysis Forthcoming Awards_SBIR_df.Rdata This unique data set consists of 89,330 observations spanning the years 1983 - 2018 and accounting for all eleven SBIR/STTR agencies. This data set consists of data collected from the Small Business Administration's Awards API and also unique data collected through web scraping by the author. Budget_SBIR_df.Rdata 246 observations for 20 agencies across 25 years of their budget-performance in the SBIR/STTR program. Data was collected from the Small Business Administration using the Annual Reports Dashboard, the Awards API, and an author-designed web crawler of the websites of awards. Solicit_SBIR-df.Rdata This data consists of observations of solicitations published by agencies for the SBIR program. This data was collected from the SBA Solicitations API. Primary Sources Small Business Administration. “Annual Reports Dashboard,” 2018. https://www.sbir.gov/awards/annual-reports. Small Business Administration. “SBIR Awards Data,” 2018. https://www.sbir.gov/api. Small Business Administration. “SBIR Solicit Data,” 2018. https://www.sbir.gov/api.

Coffee Quality Institute The Coffee Quality Institute (CQI) is a non-profit organization that works to improve the quality and value of coffee worldwide. It was founded in 1996 and has its headquarters in California, USA.

CQI's mission is to promote coffee quality through a range of activities that include research, training, and certification programs. The organization works with coffee growers, processors, roasters, and other stakeholders to improve coffee quality standards, promote sustainability, and support the development of the specialty coffee industry.

Data CQI maintains a web database that serves as a resource for coffee professionals and enthusiasts who are interested in learning about coffee quality and sustainability. The database includes a range of information on coffee production, processing, and sensory evaluation. It also contains data on coffee genetics, soil types, and other factors that can affect coffee quality.

Sensory evaluations (coffee quality scores) Aroma: Refers to the scent or fragrance of the coffee. Flavor: The flavor of coffee is evaluated based on the taste, including any sweetness, bitterness, acidity, and other flavor notes. Aftertaste: Refers to the lingering taste that remains in the mouth after swallowing the coffee. Acidity: Acidity in coffee refers to the brightness or liveliness of the taste. Body: The body of coffee refers to the thickness or viscosity of the coffee in the mouth. Balance: Balance refers to how well the different flavor components of the coffee work together. Uniformity: Uniformity refers to the consistency of the coffee from cup to cup. Clean Cup: A clean cup refers to a coffee that is free of any off-flavors or defects, such as sourness, mustiness, or staleness. Sweetness: It can be described as caramel-like, fruity, or floral, and is a desirable quality in coffee. PLEASE NOTE: 'Total Cup Points' is literally the total of 10 features given above. There were some notebooks trying to predict the total cup points given these features. We know the exact function underlying the total cup points.

Defects Defects are undesirable qualities that can occur in coffee beans during processing or storage. Defects can be categorized into two categories: Category One and Category Two defects.

Category One defects are primary defects that can be perceived through visual inspection of the coffee beans. These defects include Black beans, sour beans, insect-damaged beans, fungus-damaged beans, etc.

Category Two defects are secondary defects that are more subtle and can only be detected through tasting. These defects include Over-fermentation, staleness, rancidness, chemical taste, etc.

Data Scraping On this part, great thanks to James LeDoux. His repo coffee-quality-database from 2018 is efficiently written and well documented. To scrape the data, Fatih B. used most of his code, but due to some changes on the website, Fatih B. modified some of the lines. Also, some practices on modules were deprecated and deleted, updated those codes also. Therefore, in May-2023 we can use this updated Python program to scrape data from this database.

Only data was collected for the arabica type.

🧥 Snitch Fashion Sales (Uncleaned) Dataset 📌 Context This is a synthetic dataset representing sales transactions from Snitch, a fictional Indian clothing brand. The dataset simulates real-world retail sales data with uncleaned records, designed for learners and professionals to practice data cleaning, exploratory data analysis (EDA), and dashboard building using tools like Python, Power BI, or Excel.

📊 What You’ll Find The dataset includes over 2,500 records of fashion product sales across various Indian cities. It contains common data issues such as:

Missing values

Incorrect date formats

Duplicates

Typos in categories and city names

Unrealistic discounts and profit values

🧾 Columns Explained Column --Description Order_ID ------Unique ID for each sale (some duplicates) Customer_Name ------Name of the customer (inconsistent formatting) Product_Category ---Clothing category (e.g., T-Shirts, Jeans — includes typos) Product_Name -----Specific product sold Units_Sold --Quantity sold (some negative or null) Unit_Price --Price per unit (some missing or zero) Discount_% ----Discount applied (some >100% or missing) Sales_Amount ------Total revenue after discount (some miscalculations) Order_Date ---------Order date (multiple formats or missing) City -------Indian city (includes typos like "Hyd", "bengaluru") Segment----- Market segment (B2C, B2B, or missing) Profit ---------Profit made on the sale (some unrealistic/negative)

💡 How to Use This Dataset Clean and standardize messy data

Convert dates and correct formats

Perform EDA to find:

Top-selling categories

Impact of discounts on sales and profits

Monthly/quarterly trends

Segment-based performance

Create dashboards in Power BI or Excel Pivot Table

Document findings in a PDF/Markdown report

🎯 Ideal For Aspiring data analysts and data scientists

Excel / Power BI dashboard learners

Portfolio project creators

Kaggle competitions or practice

📌 License This is a synthetic dataset created for educational use only. No real customer or business data is included.

Overview This repository contains ready-to-use frequency time series as well as the corresponding pre-processing scripts in python. The data covers three synchronous areas of the European power grid: Continental Europe Great Britain Nordic This work is part of the paper "Predictability of Power Grid Frequency"[1]. Please cite this paper, when using the data and the code. For a detailed documentation of the pre-processing procedure we refer to the supplementary material of the paper. Data sources We downloaded the frequency recordings from publically available repositories of three different Transmission System Operators (TSOs). Continental Europe [2]: We downloaded the data from the German TSO TransnetBW GmbH, which retains the Copyright on the data, but allows to re-publish it upon request [3]. Great Britain [4]: The download was supported by National Grid ESO Open Data, which belongs to the British TSO National Grid. They publish the frequency recordings under the NGESO Open License [5]. Nordic [6]: We obtained the data from the Finish TSO Fingrid, which provides the data under the open license CC-BY 4.0 [7]. Content of the repository A) Scripts In the "Download_scripts" folder you will find three scripts to automatically download frequency data from the TSO's websites. In "convert_data_format.py" we save the data with corrected timestamp formats. Missing data is marked as NaN (processing step (1) in the supplementary material of [1]). In "clean_corrupted_data.py" we load the converted data and identify corrupted recordings. We mark them as NaN and clean some of the resulting data holes (processing step (2) in the supplementary material of [1]). The python scripts run with Python 3.7 and with the packages found in "requirements.txt". B) Yearly converted and cleansed data The folders "

The purpose of this data release is to provide data in support of the Bureau of Land Management's (BLM) Reasonably Foreseeable Development (RFD) Scenario by estimating water-use associated with oil and gas extraction methods within the BLM Carlsbad Field Office (CFO) planning area, located in Eddy and Lea Counties as well as part of Chaves County, New Mexico. Three comma separated value files and two python scripts are included in this data release. It was determined that all reported oil and gas wells within Chaves County from the FracFocus and New Mexico Oil Conservation Division (NM OCD) databases were outside of the CFO administration area and were excluded from well_records.csv and modeled_estimates.csv. Data from Chaves County are included in the produced_water.csv file to be consistent with the BLM’s water support document. Data were synthesized into comma separated values which include, produced_water.csv (volume) from NM OCD, well_records.csv (including _location and completion) from NM OCD and FracFocus, and modeled_estimates.csv (using FracFocus as well as Ball and others (2020) as input data). The results from modeled_estimates.csv were obtained using a previously published regression model (McShane and McDowell, 2021) to estimate water use associated with unconventional oil and gas activities in the Permian Basin (Valder and others, 2021) for the period of interest (2010-2021). Additionally, python scripts to process, clean, and categorize FracFocus data are provided in this data release.

Three Cases: Metadata and ProceduresThe data sets described here were used in an article submitted to the journal GeoHealth in 2021. The data files and further supplemental links (including general information about GLOBE data) can be accessed at https://observer.globe.gov/get-data/mosquito-habitat-data.Case 1: Removal of records with suspect geolocation data. A Python script was applied to remove records where the measured position (in decimal degrees) was identical to the GLOBE MGRS site position. GPS-obtained latitude and longitude coordinates are reported in decimal degrees, so records identified by whole numbers were also removed. This procedure removed 5704 (23%) of the 24983 records in the Mosquito Habitat Mapper database, with 19,279 records remaining. The secondary data sets cleaned only for geolocation anomalies were labeled Case 1.Case 2: Identifying suspected training events. For this test, we sought to identify groups of data that exceeded 10 records sharing these characteristics. Another Python script was employed to extract the photos for ease of visual inspection. Because we needed to manually review the photo records, we set the threshold for groups at >10, so that the analysis could be completed in the time allotted. Groups identified thought this procedure were outputted as case 2: groups. The resulting data set cleaned of groups >10 was labeled Case 2. The resulting data set included 20,006 records and identified 2,447 records found in clusters we postulated were training events.Case 3: The Case 3 secondary dataset result from applying the Python scripts used to create Cases 1 and 2. We used the Case 3 data sets, with improved geolocation and large groups eliminated, in the following analysis.The information in this description was last updated 2021-04-12

🌿 Overview

Dive into the Indoor Plant Health & Growth Dataset, a comprehensive collection of environmental and care-related observations across 1,000 samples of common houseplants. Whether you're building a plant health prediction model, exploring smart gardening applications, or practicing data preprocessing, this dataset offers a rich and realistic foundation.

🧠 Context

Indoor plants are highly sensitive to their environment, and factors like sunlight, watering, humidity, and soil quality play crucial roles in their growth and health. This dataset brings together diverse parameters — from leaf count and fertilizer use to pest detection and soil moisture — to support a range of machine learning, horticultural analysis, and environmental modeling tasks.

Use this dataset to explore the complex interplay between plant care and outcomes, or to develop applications that support sustainable and data-driven plant maintenance.

📊 Dataset Details

Size: 1,000 rows × 17 columns

Data Format: CSV file, fully compatible with Python, R, Excel, etc.

🔑 Features:

Plant_ID: Scientific plant name (e.g., Ficus lyrata, Aloe vera)

Height_cm: Height of the plant in centimeters

Leaf_Count: Total number of leaves

New_Growth_Count: Number of new buds or leaves observed

Health_Notes: Qualitative notes on plant appearance (e.g., “yellowing leaves”)

Watering_Amount_ml:Amount of water given in milliliters

Watering_Frequency_days: Days between watering sessions

Sunlight_Exposure: Descriptive light exposure (e.g., “3 hrs morning sun”)

Room_Temperature_C: Room temperature in Celsius

Humidity_%:Room humidity percentage

Fertilizer_Type:Type of fertilizer used

Fertilizer_Amount_ml: Amount of fertilizer applied

Pest_Presence: Detected pest type (if any)

Pest_Severity: Level of pest infestation

Soil_Moisture_%: Soil moisture percentage

Soil_Type: Soil classification (e.g., loamy, clay, sandy)

Health_Score: Rating from 1 (dying) to 5 (thriving)

📈 Data Quality Notes

Clean and ready for modeling, with realistic variation across samples

Features a mix of numerical, categorical, and textual data

Ideal for data cleaning, feature engineering, and exploratory analysis

Great practice for encoding categorical variables and building regression/classification models

💡 Potential Use Cases

Train machine learning models to predict plant health

Build smart gardening or IoT-based plant monitoring applications

Analyze the impact of environmental and care parameters on plant growth

Visualize trends in plant care routines and outcomes

Practice data preprocessing, transformation, and modeling workflows

📌 Please consider upvoting if you find this dataset helpful. Happy growing — and modeling! 🌱

Description This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. We also include sample codes of baseline models for benchmarking of more elaborated models. Data usage The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper: Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494 Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data. Sample code As part of the data release, we are also including the sample code written in Python 3. The preprocessed data used in the scripts are also provided. The code can be used to reproduce the results presented in this work and as a starting point for future studies. Besides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models. All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip. Units All time stamps are in UTC (YYYY-MM-DD HH:MM:SS). All irradiance and weather data are in SI units. Sky image features are derived from 8-bit RGB (256 color levels) data. Satellite images are derived from 8-bit gray-scale (256 color levels) data. Missing data The string "NAN" indicates missing data File formats All time series data files as in CSV (comma separated values) Images are given in tar.bz2 files Files Folsom_irradiance.csv Primary One-minute GHI, DNI, and DHI data. Folsom_weather.csv Primary One-minute weather data. Folsom_sky_images_{YEAR}.tar.bz2 Primary Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2. Folsom_NAM_lat{LAT}_lon{LON}.csv Primary NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper. Folsom_sky_image_features.csv Secondary Features derived from the sky images. Folsom_satellite.csv Secondary 10 pixel by 10 pixel GOES-15 images centered in the target location. Irradiance_features_{horizon}.csv Secondary Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}). Sky_image_features_intra-hour.csv Secondary Sky image features for the intra-hour forecasting issuing times. Sat_image_features_intra-day.csv Secondary Satellite image features for the intra-day forecasting issuing times. NAM_nearest_node_day-ahead.csv Secondary NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting. Target_{horizon}.csv Secondary Target data for the different forecasting horizons. Forecast_{horizon}.py Code Python script used to create the forecasts for the different horizons. Postprocess.py Code Python script used to compute the error metric for all the forecasts. {"references": ["Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494"]}