Find research datasets worth reusing

openother-openMay 2022View details →

The allocation of Chinese and Indian development finance in Nepal and its influence on local election results

This realease contains the used datasets and calculations for my Bachelorthesis about Indian and Chinese allocation of Overall Development Assistance in Nepal.

opencc-by-4.0Aug 2018View details →

The Role of the World Bank in Financing Tuberculosis Control- Dataset

The completed dataset for the World Bank's financing of TB control initiatives from 1986-2017 (coded_questionnaire.csv). This is accompanied by the variable description guide (Glossary and Variable Description Guide.pdf).  Co-financiers_database.csv is the data used in the analysis of co-financing of World Bank tuberculosis projects.  IHME_DAH_DATABASE 24-10-17.csv is the data used in the analysis of Development Assitance for Health and Tuberculosis. World Bank and TB.R is the source code used to encode and analyse all of the above data. Data analysis on R version 3.4.3. Extended Data.pdf is the supplementary data for publication.

opencc-by-4.0Mar 2023View details →

Dataset - Analytic Network Process in economics, finance and management

This data set presents the recent use of the Analytic Network Process (ANP) in the decision process in the areas of economics, finance and management. It has 434 ANP studies for a 10-year period (2012-2021) within the Scopus database. They were identified using the keyword “Analytic Network Process” in articles indexed in the following two database categories: "Business, Management and Accounting"; and "Economics, Econometrics and Finance".

opencc-by-4.0Jun 2023View details →

Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access

Description The datasets demonstrate the malware economy and the value chain published in our paper, <a href="https://doi.org/10.1145/3600160.3605047">Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access</a>, at the 12th International Workshop on Cyber Crime (IWCC 2023), part of the ARES Conference, published by the International Conference Proceedings Series of the ACM ICPS. Using the well-documented scripts, it is straightforward to reproduce our findings. It takes an estimated 1 hour of human time and 3 hours of computing time to duplicate our key findings from MalwareInfectionSet; around one hour with VictimAccessSet; and minutes to replicate the price calculations using AccountAccessSet. See the included README.md files and Python scripts. We choose to represent each victim by a single JavaScript Object Notation (JSON) data file. Data sources provide sets of victim JSON data files from which we've extracted the essential information and omitted Personally Identifiable Information (PII). We collected, curated, and modelled three datasets, which we publish under the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. 1. MalwareInfectionSet We discover (and, to the best of our knowledge, document scientifically for the first time) that malware networks appear to dump their data collections online. We collected these infostealer malware logs available for free. We utilise 245 malware log dumps from 2019 and 2020 originating from 14 malware networks. The dataset contains 1.8 million victim files, with a dataset size of 15 GB. 2. VictimAccessSet We demonstrate how Infostealer malware networks sell access to infected victims. Genesis Market focuses on user-friendliness and continuous supply of compromised data. Marketplace listings include everything necessary to gain access to the victim's online accounts, including passwords and usernames, but also detailed collection of information which provides a clone of the victim's browser session. Indeed, Genesis Market simplifies the import of compromised victim authentication data into a web browser session. We measure the prices on Genesis Market and how compromised device prices are determined. We crawled the website between April 2019 and May 2022, collecting the web pages offering the resources for sale. The dataset contains 0.5 million victim files, with a dataset size of 3.5 GB. 3. AccountAccessSet The Database marketplace operates inside the anonymous Tor network. Vendors offer their goods for sale, and customers can purchase them with Bitcoins. The marketplace sells online accounts, such as PayPal and Spotify, as well as private datasets, such as driver's licence photographs and tax forms. We then collect data from Database Market, where vendors sell online credentials, and investigate similarly. To build our dataset, we crawled the website between November 2021 and June 2022, collecting the web pages offering the credentials for sale. The dataset contains 33,896 victim files, with a dataset size of 400 MB. Credits Authors <ul> <li>Billy Bob Brumley (Tampere University, Tampere, Finland)</li> <li>Juha Nurmi (Tampere University, Tampere, Finland)</li> <li>Mikko Niemelä (Cyber Intelligence House, Singapore)</li> </ul> Funding This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under project numbers 804476 (SCARE) and 952622 (SPIRS). Alternative links to download: <a href="https://mega.nz/folder/aJwVyIYJ#9SWh-Z3-TpPfjHZeFxbeew">AccountAccessSet</a>, <a href="https://mega.nz/folder/iUQ3RaKB#48ZkXnFYSR0qXLcbkrZLqw">MalwareInfectionSet</a>, and <a href="https://mega.nz/folder/aNYCFCrK#pbDkJL-PNWjn1ABXbtdR4w">VictimAccessSet</a>.

opencc-by-4.0Dec 2023View details →

A LARGE INTEGRATED KNOWLEDGE GRAPH OF ECNOMICS, FINANCE AND BANKING

Creating the first release to obtain a DOI on Zenodo

opencc-by-4.0Jun 2022View details →

Small Bills and Petty Finance

This dataset makes accessible the overseers’ vouchers transcribed by the project Small Bills and Petty Finance: co-creating the history of the Old Poor Law which was funded by the British Arts and Humanities Research Council 2018-2021.  Vouchers are drawn from parishes in three English counties, namely Cumbria, East Sussex, and Staffordshire.  Modern Cumbria includes parishes formerly allocated to Westmorland, and East Sussex is exclusively represented by East Hoathly, located in the historic county of Sussex.  Additional vouchers beyond this database exist in each county; and in East Sussex the sheer volume of surviving vouchers has necessitated transcribing a single parish - East Hoathly - rather than a wider selection of parishes as in Cumbria and Staffordshire.  The data includes a variety of details about goods and services paid for from poor-rate income 1700-1840 such as date, commodity type and cost, payer, and payee.  Additional features can comprise commodity quantities and quality, the names of pauper beneficiaries, and signatures of traders or their deputies.  Where possible, the archival data has been augmented such as by the addition of occupational categories and geolocations.  Additional information about some of the people and themes occurring in this data, and alternative online access to the dataset, can be found at <a href="http://www.thepoorlaw.org/">www.thepoorlaw.org</a> Each of the 41549 entries has up to sixty columns of data, all of which are described in the additional notes.  The original vouchers were generated by a large cohort of traders, professionals, and local-government officers, each with different levels of literacy and accounting competency.  Spellings have not been standardised.  The dataset was created by project staff and a team of archival volunteers between May 2018 and March 2021.  The results are neither consistent nor error-free but do aim to give a clear and comprehensive account of voucher contents line by line.

opencc-by-4.0Aug 2022View details →

Data: Biodiversity Risks and Safeguards of China's Hydropower Financing in BRI Countries

Data used in: Narain D, Teo HC, Lechner AM, Watson JEM, Maron M. 2021. Biodiversity risks and safeguards of China’s hydropower financing in BRI countries. (In press with One Earth)

Dataset: Fidelity Disruptive Finance ETF (FDFF) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Dataset: WhiteHorse Finance, Inc. 7.875% Notes due 2028 (WHFCL) Stock Performance

Dataset: WhiteHorse Finance, Inc. (WHF) Stock Performance

Dataset: Runway Growth Finance Corp. (RWAY) Stock Performance

Dataset: Runway Growth Finance Corp. 8.00% Notes due 2027 (RWAYZ) Stock Performance

Dataset: Roma Green Finance Limited (ROMA) Stock Performance

Dataset: Chicago Atlantic Real Estate Finance, Inc. (REFI) Stock Performance

Dataset: Portman Ridge Finance Corporation (PTMN) Stock Performance