Dataset

This dataset was created by Muhammad Altaf Khan

Contents

• Cleaned_Dataset.csv – The combined CSV files of all scraped documents from DABI, e-LiS, o-bib and Springer.
• Data_Cleaning.ipynb – The Jupyter Notebook with python code for the analysis and cleaning of the original dataset.
• ger_train.csv – The German training set as CSV file.
• ger_validation.csv – The German validation set as CSV file.
• en_test.csv – The English test set as CSV file.
• en_train.csv – The English training set as CSV file.
• en_validation.csv – The English validation set as CSV file.
• splitting.py – The python code for splitting a dataset into train, test and validation set.
• DataSetTrans_de.csv – The final German dataset as a CSV file.
• DataSetTrans_en.csv – The final English dataset as a CSV file.
• translation.py – The python code for translating the cleaned dataset.

The original dataset found on Kaggle had fewer columns, some with 2 separate variables grouped together. Furthermore, the numbers in many of the data were string characters instead of int, since they were typed with numbers followed by words, for instance: Condition: 2 Accidents, 3 previous owners This one column was split into two separate columns - Accidents and Owners, and the string characters were removed and then the numbers were converted to integer type. Just like this example, many other columns have been modified, along with other cleaning and organizational techniques using python.

Columns Definition and Information of the data set

The auction dataset is a really small data set ( 19 items) which is being created for the sole purpose of learning pandas library.

The auction data set contains 5 columns :

1. Item :Gives the description of what items are being sold. 2. Bidding Price : Gives the price at which the item will start being sold at. 3. Selling Price : The selling price tells us at which amount the item was sold. 4. Calls :Calls indicate the number of times the items value was raised or decreased by the customer. 5. Bought By : Gives us the idea which customer bought the item.

Note: There are missing values, which we will try to fill. And yes some values might not make sense once we make those imputations, but this notebook is for the sole purpose of learning.

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.

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.

Reddit is a social news, content rating and discussion website. It's one of the most popular sites on the internet. Reddit has 52 million daily active users and approximately 430 million users who use it once a month. Reddit has different subreddits and here We'll use the r/AskScience Subreddit.

The dataset is extracted from the subreddit /r/AskScience from Reddit. The data was collected between 01-01-2016 and 20-05-2022. It contains 612,668 Datapoints and 25 Columns. The database contains a number of information about the questions asked on the subreddit, the description of the submission, the flair of the question, NSFW or SFW status, the year of the submission, and more. The data is extracted using python and Pushshift's API. A little bit of cleaning is done using NumPy and pandas as well. (see the descriptions of individual columns below).

The dataset contains the following columns and descriptions: author - Redditor Name author_fullname - Redditor Full name contest_mode - Contest mode [implement obscured scores and randomized sorting]. created_utc - Time the submission was created, represented in Unix Time. domain - Domain of submission. edited - If the post is edited or not. full_link - Link of the post on the subreddit. id - ID of the submission. is_self - Whether or not the submission is a self post (text-only). link_flair_css_class - CSS Class used to identify the flair. link_flair_text - Flair on the post or The link flair’s text content. locked - Whether or not the submission has been locked. num_comments - The number of comments on the submission. over_18 - Whether or not the submission has been marked as NSFW. permalink - A permalink for the submission. retrieved_on - time ingested. score - The number of upvotes for the submission. description - Description of the Submission. spoiler - Whether or not the submission has been marked as a spoiler. stickied - Whether or not the submission is stickied. thumbnail - Thumbnail of Submission. question - Question Asked in the Submission. url - The URL the submission links to, or the permalink if a self post. year - Year of the Submission. banned - Banned by the moderator or not.

This dataset can be used for Flair Prediction, NSFW Classification, and different Text Mining/NLP tasks. Exploratory Data Analysis can also be done to get the insights and see the trend and patterns over the years.

BackgroundThe Department of Rehabilitation Medicine is key to improving patients’ quality of life. Driven by chronic diseases and an aging population, there is a need to enhance the efficiency and resource allocation of outpatient facilities. This study aims to analyze the treatment preferences of outpatient rehabilitation patients by using data and a grading tool to establish predictive models. The goal is to improve patient visit efficiency and optimize resource allocation through these predictive models.MethodsData were collected from 38 Chinese institutions, including 4,244 patients visiting outpatient rehabilitation clinics. Data processing was conducted using Python software. The pandas library was used for data cleaning and preprocessing, involving 68 categorical and 12 continuous variables. The steps included handling missing values, data normalization, and encoding conversion. The data were divided into 80% training and 20% test sets using the Scikit-learn library to ensure model independence and prevent overfitting. Performance comparisons among XGBoost, random forest, and logistic regression were conducted using metrics, including accuracy and receiver operating characteristic (ROC) curves. The imbalanced learning library’s SMOTE technique was used to address the sample imbalance during model training. The model was optimized using a confusion matrix and feature importance analysis, and partial dependence plots (PDP) were used to analyze the key influencing factors.ResultsXGBoost achieved the highest overall accuracy of 80.21% with high precision and recall in Category 1. random forest showed a similar overall accuracy. Logistic Regression had a significantly lower accuracy, indicating difficulties with nonlinear data. The key influencing factors identified include distance to medical institutions, arrival time, length of hospital stay, and specific diseases, such as cardiovascular, pulmonary, oncological, and orthopedic conditions. The tiered diagnosis and treatment tool effectively helped doctors assess patients’ conditions and recommend suitable medical institutions based on rehabilitation grading.ConclusionThis study confirmed that ensemble learning methods, particularly XGBoost, outperform single models in classification tasks involving complex datasets. Addressing class imbalance and enhancing feature engineering can further improve model performance. Understanding patient preferences and the factors influencing medical institution selection can guide healthcare policies to optimize resource allocation, improve service quality, and enhance patient satisfaction. Tiered diagnosis and treatment tools play a crucial role in helping doctors evaluate patient conditions and make informed recommendations for appropriate medical care.

A collection of datasets and python scripts for extraction and analysis of isograms (and some palindromes and tautonyms) from corpus-based word-lists, specifically Google Ngram and the British National Corpus (BNC).Below follows a brief description, first, of the included datasets and, second, of the included scripts.1. DatasetsThe data from English Google Ngrams and the BNC is available in two formats: as a plain text CSV file and as a SQLite3 database.1.1 CSV formatThe CSV files for each dataset actually come in two parts: one labelled ".csv" and one ".totals". The ".csv" contains the actual extracted data, and the ".totals" file contains some basic summary statistics about the ".csv" dataset with the same name.The CSV files contain one row per data point, with the colums separated by a single tab stop. There are no labels at the top of the files. Each line has the following columns, in this order (the labels below are what I use in the database, which has an identical structure, see section below):

Label Data type Description

isogramy int The order of isogramy, e.g. "2" is a second order isogram

length int The length of the word in letters

word text The actual word/isogram in ASCII

source_pos text The Part of Speech tag from the original corpus

count int Token count (total number of occurences)

vol_count int Volume count (number of different sources which contain the word)

count_per_million int Token count per million words

vol_count_as_percent int Volume count as percentage of the total number of volumes

is_palindrome bool Whether the word is a palindrome (1) or not (0)

is_tautonym bool Whether the word is a tautonym (1) or not (0)

The ".totals" files have a slightly different format, with one row per data point, where the first column is the label and the second column is the associated value. The ".totals" files contain the following data:

Label

Data type

Description

!total_1grams

int

The total number of words in the corpus

!total_volumes

int

The total number of volumes (individual sources) in the corpus

!total_isograms

int

The total number of isograms found in the corpus (before compacting)

!total_palindromes

int

How many of the isograms found are palindromes

!total_tautonyms

int

How many of the isograms found are tautonyms

The CSV files are mainly useful for further automated data processing. For working with the data set directly (e.g. to do statistics or cross-check entries), I would recommend using the database format described below.1.2 SQLite database formatOn the other hand, the SQLite database combines the data from all four of the plain text files, and adds various useful combinations of the two datasets, namely:• Compacted versions of each dataset, where identical headwords are combined into a single entry.• A combined compacted dataset, combining and compacting the data from both Ngrams and the BNC.• An intersected dataset, which contains only those words which are found in both the Ngrams and the BNC dataset.The intersected dataset is by far the least noisy, but is missing some real isograms, too.The columns/layout of each of the tables in the database is identical to that described for the CSV/.totals files above.To get an idea of the various ways the database can be queried for various bits of data see the R script described below, which computes statistics based on the SQLite database.2. ScriptsThere are three scripts: one for tiding Ngram and BNC word lists and extracting isograms, one to create a neat SQLite database from the output, and one to compute some basic statistics from the data. The first script can be run using Python 3, the second script can be run using SQLite 3 from the command line, and the third script can be run in R/RStudio (R version 3).2.1 Source dataThe scripts were written to work with word lists from Google Ngram and the BNC, which can be obtained from http://storage.googleapis.com/books/ngrams/books/datasetsv2.html and [https://www.kilgarriff.co.uk/bnc-readme.html], (download all.al.gz).For Ngram the script expects the path to the directory containing the various files, for BNC the direct path to the *.gz file.2.2 Data preparationBefore processing proper, the word lists need to be tidied to exclude superfluous material and some of the most obvious noise. This will also bring them into a uniform format.Tidying and reformatting can be done by running one of the following commands:python isograms.py --ngrams --indir=INDIR --outfile=OUTFILEpython isograms.py --bnc --indir=INFILE --outfile=OUTFILEReplace INDIR/INFILE with the input directory or filename and OUTFILE with the filename for the tidied and reformatted output.2.3 Isogram ExtractionAfter preparing the data as above, isograms can be extracted from by running the following command on the reformatted and tidied files:python isograms.py --batch --infile=INFILE --outfile=OUTFILEHere INFILE should refer the the output from the previosu data cleaning process. Please note that the script will actually write two output files, one named OUTFILE with a word list of all the isograms and their associated frequency data, and one named "OUTFILE.totals" with very basic summary statistics.2.4 Creating a SQLite3 databaseThe output data from the above step can be easily collated into a SQLite3 database which allows for easy querying of the data directly for specific properties. The database can be created by following these steps:1. Make sure the files with the Ngrams and BNC data are named “ngrams-isograms.csv” and “bnc-isograms.csv” respectively. (The script assumes you have both of them, if you only want to load one, just create an empty file for the other one).2. Copy the “create-database.sql” script into the same directory as the two data files.3. On the command line, go to the directory where the files and the SQL script are. 4. Type: sqlite3 isograms.db 5. This will create a database called “isograms.db”.See the section 1 for a basic descript of the output data and how to work with the database.2.5 Statistical processingThe repository includes an R script (R version 3) named “statistics.r” that computes a number of statistics about the distribution of isograms by length, frequency, contextual diversity, etc. This can be used as a starting point for running your own stats. It uses RSQLite to access the SQLite database version of the data described above.

This dataset provides annual records of threatened species from 2004 to 2023, focusing on the 25 countries most impacted by biodiversity loss. For direct download of datasets. The data is organized into three categories—Vertebrates, Invertebrates, and Plants—and sourced from UNdata and the IUCN Red List. Each entry includes the country name, year, species count, and biodiversity group. It is designed to support research, education, and public engagement on global conservation priorities. Source and Collection Timeline Original Data Range: 2004–2023 Cleaned and Extracted: November 2024 Primary Sources: UNdata, IUCN Red List (via UN Statistics Division) Data Processing Summary Data Cleaning: Removed incomplete entries and excluded non-country-level data (e.g., continents or regions). Grouping: Categorized into Vertebrates, Invertebrates, and Plants. Top 25 Filter: Selected the top 25 countries per year and per category to improve visual clarity. File Generation: Created three structured CSVs using Python (Pandas). Data Format File Type: CSV (.csv) Columns Include: Country – Name of the country Year – Range from 2004 to 2023 Value – Number of threatened species Group – Vertebrates, Invertebrates, or Plants

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.

Dataset Description: Online Retail Transaction Data

This dataset contains transactional data from an online retail store, including customer purchases, product details, invoice information, and country-specific data. The dataset is structured into four main files:

Invoices.csv – Contains invoice-related details such as date and customer information.

Products.csv – Includes product-specific data like stock codes, descriptions, and unit prices.

Invoice_rel_product.csv – Represents the relationship between invoices and products, detailing quantities purchased.

Customers.csv – Provides customer identifiers and their respective countries.

Column Descriptions:

InvoiceNo: Unique identifier for each order (invoices starting with "C" indicate refunds/cancellations).

InvoiceDate: The date and time when the invoice was issued.

StockCode: Unique code assigned to each product.

Description: Name or description of the product.

UnitPrice: Price per unit of the product (in GBP).

Quantity: Number of units purchased per transaction.

CustomerID: Unique identifier for each customer.

Country: The country from which the order was placed.

Preprocessing Notes:

-Refund Flag: Invoices starting with "C" were marked with an additional feature {is_return: True/False} in the graph database to distinguish refunded transactions.

-Data Cleaning: Rows with negative values in UnitPrice or Quantity were removed using Pandas DataFrame for consistency.

Analyzing customers’ characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization and other preprocessing operations are done on 900,000 telecom customer personal characteristics and historical behavior data set based on Python language. Appropriate model parameters were selected to build BPNN (Back Propagation Neural Network). Random Forest (RF) and Adaboost, the two classic ensemble learning models were introduced, and the Adaboost dual-ensemble learning model with RF as the base learner was put forward. The four models and the other four classical machine learning models-decision tree, naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) were utilized respectively to analyze the customer churn data. The results show that the four models have better performance in terms of recall rate, precision rate, F1 score and other indicators, and the RF-Adaboost dual-ensemble model has the best performance. Among them, the recall rates of BPNN, RF, Adaboost and RF-Adaboost dual-ensemble model on positive samples are respectively 79%, 90%, 89%,93%, the precision rates are 97%, 99%, 98%, 99%, and the F1 scores are 87%, 95%, 94%, 96%. The RF-Adaboost dual-ensemble model has the best performance, and the three indicators are 10%, 1%, and 6% higher than the reference. The prediction results of customer churn provide strong data support for telecom companies to adopt appropriate retention strategies for pre-churn customers and reduce customer churn.

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.

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

Species loss is highly scale-dependent, following the species-area relationship. We analysed spatio-temporal patterns of species’ extirpation on a multitaxonomic level using Berlin, the capital city of Germany. Berlin is one of the largest cities in Europe and has experienced a strong urbanisation trend since the late 19th century. We expected species’ extirpation to be exceptionally high due to the long history of urbanisation. Analysing regional Red Lists of Threatened Plants, Animals, and Fungi of Berlin (covering 9498 species), we found that 16 % of species were extirpated, a rate 5.9 times higher than at the German scale, and 47.1 times higher than at the European scale. Species’ extirpation in Berlin is comparable to that of another German city with a similarly broad taxonomic coverage, but much higher than in regional areas with less human impact. The documentation of species’ extirpation started in the 18th century and is well documented for the 19th and 20th centuries. We found an average annual extirpation of 3.6 species in the 19th century, 9.6 species in the 20th century, and the same number of extirpated species as in the 19th century were documented in the 21th century, despite the much shorter time period. Our results showed that species’ extirpation is higher at small than on large spatial scales, and might be negatively influenced by urbanisation, with different effects on different taxonomic groups and habitats. Over time, we found that species’ extirpation is highest during periods of high human alterations and is negatively affected by the number of people living in the city. But, there is still a lack of data to decouple the size of the area and the human impact of urbanisation. However, cities might be suitable systems for studying species’ extirpation processes due to their small scale and human impact. Methods Data extraction: To determine the proportion of extirpated species for Germany, we manually summarised the numbers of species classified in category 0 ‘extinct or extirpated’ and calculated the percentage in relation to the total number of species listed in the Red Lists of Threatened Species for Germany, taken from the website of the Red List Centre of Germany (Rote Liste Zentrum, 2024a). For Berlin, we used the 37 current Red Lists of Threatened Plants, Animals, and Fungi from the city-state of Berlin, covering the years from 2004 to 2023, taken from the official capital city portal of the Berlin Senate Department for Mobility, Transport, Climate Protection and Environment (SenMVKU, 2024a; see overview of Berlin Red Lists used in Table 1). We extracted all species that are listed as extinct/extirpated, i.e. classified in category 0, and additionally, if available, the date of the last record of the species in Berlin. The Red List of macrofungi of the order Boletales by Schmidt (2017) was not included in our study, as this Red List has only been compiled once in the frame of a pilot project and therefore lacks the category 0 ‘extinct or extirpated’. We used Python, version 3.7.9 (Van Rossum and Drake, 2009), the Python libraries Pandas (McKinney et al., 2010), and Camelot-py, version 0.11.0 (Vinayak Meta, 2023) in Jupyter Lab, version 4.0.6 (Project Jupyter, 2016) notebooks. In the first step, we created a metadata table of the Red Lists of Berlin to keep track of the extraction process, maintain the source reference links, and store summarised data from each Red List pdf file. At the extraction of each file, a data row was added to the metadata table which was updated throughout the rest of the process. In the second step, we identified the page range for extraction for each extracted Red List file. The extraction mechanism for each Red List file depended on the printed table layout. We extracted tables with lined rows with the Lattice parsing method (Camelot-py, 2024a), and tables with alternating-coloured rows with the Stream method (Camelot-py, 2024b). For proofing the consistency of extraction, we used the Camelot-py accuracy report along with the Pandas data frame shape property (Pandas, 2024). After initial data cleaning for consistent column counts and missing data, we filtered the data for species in category 0 only. We collated data frames together and exported them as a CSV file. In a further step, we proofread whether the filtered data was tallied with the summary tables, given in each Red List. Finally, we cleaned each Red List table to contain the species, the current hazard level (category 0), the date of the species’ last detection in Berlin, and the reference (codes and data available at: Github, 2023). When no date of last detection was given for a species, we contacted the authors of the respective Red Lists and/or used former Red Lists to find information on species’ last detections (Burger et al., 1998; Saure et al., 1998; 1999; Braasch et al., 2000; Saure, 2000). Determination of the recording time windows of the Berlin Red Lists We determined the time windows, the Berlin Red Lists look back on, from their methodologies. If the information was missing in the current Red Lists, we consulted the previous version (see all detailed time windows of the earliest assessments with references in Table B2 in Appendix B). Data classification: For the analyses of the percentage of species in the different hazard levels, we used the German Red List categories as described in detail by Saure and Schwarz (2005) and Ludwig et al. (2009). These are: Prewarning list, endangered (category 3), highly endangered (category 2), threatened by extinction or extirpation (category 1), and extinct or extirpated (category 0). To determine the number of indigenous unthreatened species in each Red List, we subtracted the number of species in the five categories and the number of non-indigenous species (neobiota) from the total number of species in each Red List. For further analyses, we pooled the taxonomic groups of the 37 Red Lists into more broadly defined taxonomic groups: Plants, lichens, fungi, algae, mammals, birds, amphibians, reptiles, fish and lampreys, molluscs, and arthropods (see categorisation in Table 1). We categorised slime fungi (Myxomycetes including Ceratiomyxomycetes) as ‘fungi’, even though they are more closely related to animals because slime fungi are traditionally studied by mycologists (Schmidt and Täglich, 2023). We classified ‘lichens’ in a separate category, rather than in ‘fungi’, as they are a symbiotic community of fungi and algae (Krause et al., 2017). For analyses of the percentage of extirpated species of each pooled taxonomic group, we set the number of extirpated species in relation to the sum of the number of unthreatened species, species in the prewarning list, and species in the categories one to three. We further categorised the extirpated species according to the habitats in which they occurred. We therefore categorised terrestrial species as ‘terrestrial’ and aquatic species as ‘aquatic’. Amphibians and dragonflies have life stages in both, terrestrial and aquatic habitats, and were categorised as ‘terrestrial/aquatic’. We also categorised plants and mosses as ‘terrestrial/aquatic’ if they depend on wetlands (see all habitat categories for each species in Table C1 in Appendix C). The available data considering the species’ last detection in Berlin ranked from a specific year, over a period of time up to a century. If a year of last detection was given with the auxiliary ‘around’ or ‘circa’, we used for further analyses the given year for temporal classification. If a year of last detection was given with the auxiliary ‘before’ or ‘after’, we assumed that the nearest year of last detection was given and categorised the species in the respective century. In this case, we used the species for temporal analyses by centuries only, not across years. If only a timeframe was given as the date of last detection, we used the respective species for temporal analyses between centuries, only. We further classified all of the extirpated species in centuries, in which species were lastly detected: 17th century (1601-1700); 18th century (1701-1800); 19th century (1801-1900); 20th century (1901-2000); 21th century (2001-now) (see all data on species’ last detection in Table C1 in Appendix C). For analyses of the effects of the number of inhabitants on species’ extirpation in Berlin, we used species that went extirpated between the years 1920 and 2012, because of Berlin’s was expanded to ‘Groß-Berlin’ in 1920 (Buesch and Haus, 1987), roughly corresponding to the cities’ current area. Therefore, we included the number of Berlin’s inhabitants for every year a species was last detected (Statistische Jahrbücher der Stadt Berlin, 1920, 1924-1998, 2000; see all data on the number of inhabitants for each year of species’ last detection in Table C1 in Appendix C). Materials and Methods from Keinath et al. (2024): 'High levels of species’ extirpation in an urban environment – A case study from Berlin, Germany, covering 1700-2023'.

Cooperation involves the challenge of jointly selecting one from multiple goals while maintaining the team’s joint commitment to it. We test joint commitment in a multi-player hunting game, combining psychophysics and computational modeling. Joint commitment is modeled through an "Imagined We" (IW) approach, where each agent uses Bayesian inference to infer the intention of “We”, an imagined supraindividual agent controlling all agents as its body parts. This is compared against a Reward Sharing (RS) model, which frames cooperation through reward sharing via multi-agent reinforcement learning (MARL). Both humans and IW, but not RS, maintained high performance by jointly committing to a single prey, regardless of prey quantity or speed. Human observers rated all hunters in both human and IW teams as making high contributions to the catch, regardless of their proximity to the prey, suggesting that high-quality hunting stemmed from sophisticated cooperation rather than individual strategie...

Dataset Creation and Processing Overview

This dataset underwent a comprehensive process of loading, cleaning, processing, and preparing, incorporating a range of data manipulation and NLP techniques to optimize its utility for machine learning models, particularly in natural language processing.

  Data Loading and Initial Cleaning

Source: Loaded from the Hugging Face dataset repository bprateek/amazon_product_description. Conversion to Pandas DataFrame: For ease of data… See the full description on the dataset page: https://huggingface.co/datasets/ckandemir/amazon-products.

This dataset contains GPS tracking data and performance metrics for motorcycle taxis (boda bodas) in Nairobi, Kenya, comparing traditional internal combustion engine (ICE) motorcycles with electric motorcycles. The study was conducted in two phases:Baseline Phase: 118 ICE motorcycles tracked over 14 days (2023-11-13 to 2023-11-26)Transition Phase: 108 ICE motorcycles (control) and 9 electric motorcycles (treatment) tracked over 12 days (2023-12-10 to 2023-12-21)The dataset is organised into two main categories:Trip Data: Individual trip-level records containing timing, distance, duration, location, and speed metricsDaily Data: Daily aggregated summaries containing usage metrics, economic data, and energy consumptionThis dataset enables comparative analysis of electric vs. ICE motorcycle performance, economic modelling of transportation costs, environmental impact assessment, urban mobility pattern analysis, and energy efficiency studies in emerging markets.Institutions:EED AdvisoryClean Air TaskforceStellenbosch UniversitySteps to reproduce:Raw Data CollectionGPS tracking devices installed on motorcycles, collecting location data at 10-second intervalsRider-reported information on revenue, maintenance costs, and fuel/electricity usageProcessing StepsGPS data cleaning: Filtered invalid coordinates, removed duplicates, interpolated missing pointsTrip identification: Defined by >1 minute stationary periods or ignition cyclesTrip metrics calculation: Distance, duration, idle time, average/max speedsDaily data aggregation: Summed by user_id and date with self-reported economic dataValidation: Cross-checked with rider logs and known routesAnonymisation: Removed start and end coordinates for first and last trips of each day to protect rider privacy and home locationsTechnical InformationGeographic coverage: Nairobi, KenyaTime period: November-December 2023Time zone: UTC+3 (East Africa Time)Currency: Kenyan Shillings (KES)Data format: CSV filesSoftware used: Python 3.8 (pandas, numpy, geopy)Notes: Some location data points are intentionally missing to protect rider privacy. Self-reported economic and energy consumption data has some missing values where riders did not report.CategoriesMotorcycle, Transportation in Africa, Electric Vehicles

Operations such as time and coordinate conversions and data cleaning are routine tasks in geodesy and geophysics. Nevertheless, simple and efficient high-level functions to help those kinds of jobs are barely available, and has to be developed, again and again, by each student, engineer for each new project, and even by senior scientists. On another hand, Python became little by little within the last decade a well-used programming language in the academic world. Despite the fact that countless toolboxes already exist in Python for scientific purposes, none really exists for geodetic-oriented purposes. The geodeZYX toolbox aims to fill this gap. The objective of this toolbox, written in Python 3, is to provide a simple but useful and efficient set of functions to help geodesists and geophysicists to spend less time on the pre-processing steps and focus faster on their research, according to the KISS Principle. A static version of the geodeZYX toolbox is available via the "Files" section on this DOI Landing Page and via github (https://github.com/GeodeZYX/GeodeZYX-Toolbox_v4).