In the age of digital transformation, scientific and social interest for data and data products is constantly on the rise. The quantity as well as the variety of digital research data is increasing significantly. This raises the question about the governance of this data. For example, how to store the data so that it is presented transparently, freely accessible and subsequently available for re-use in the context of good scientific practice. Research data repositories provide solutions to these issues.

Considering the variety of repository software, it is sometimes difficult to identify a fitting solution for a specific use case. For this purpose a detailed analysis of existing software is needed. Presented table of requirements can serve as a starting point and decision-making guide for choosing the most suitable for your purposes repository software. This table is dealing as a supplementary material for the paper "How to choose a research data repository software? Experience report." (persistent identifier to the paper will be added as soon as paper is published).

Thanks to a variety of software services, it has never been easier to produce, manage and publish Linked Open Data. But until now, there has been a lack of an accessible overview to help researchers make the right choice for their use case. This dataset release will be regularly updated to reflect the latest data published in a comparison table developed in Google Sheets [1]. The comparison table includes the most commonly used LOD management software tools from NFDI4Culture to illustrate what functionalities and features a service should offer for the long-term management of FAIR research data, including:

• ConedaKOR
• LinkedDataHub
• Metaphacts
• Omeka S
• ResearchSpace
• Vitro
• Wikibase
• WissKI

The table presents two views based on a comparison system of categories developed iteratively during workshops with expert users and developers from the respective tool communities. First, a short overview with field values coming from controlled vocabularies and multiple-choice options; and a second sheet allowing for more descriptive free text additions. The table and corresponding dataset releases for each view mode are designed to provide a well-founded basis for evaluation when deciding on a LOD management service. The Google Sheet table will remain open to collaboration and community contribution, as well as updates with new data and potentially new tools, whereas the datasets released here are meant to provide stable reference points with version control.

The research for the comparison table was first presented as a paper at DHd2023, Open Humanities – Open Culture, 13-17.03.2023, Trier and Luxembourg [2].

[1] Non-editing access is available here: docs.google.com/spreadsheets/d/1FNU8857JwUNFXmXAW16lgpjLq5TkgBUuafqZF-yo8_I/edit?usp=share_link To get editing access contact the authors.

[2] Full paper will be made available open access in the conference proceedings.

Link to code

Description:

The **company* that I work for builds iOS & Android mobile applications that are available in the App Store (iOS) and on Google Play (Android). I am a 'data analyst' at this company and am responsible for guiding the software developers in making data-driven decisions in regards to which apps they should build.

**This project was completed as part of a DataQuest course and was not used for a real company.*

Plan:

The criteria that my company has laid out for a successful app can be determined as follows:

• Create a minimal Android version of the application and add it to Google Play.
• The app will be developed further IF it gets a good response from users.
• If app continues to be profitable after 6 months, an iOS version will be built and added to the App store.

The applications my company builds are all free for users to download and install. Our revenue mainly comes from in-app ads, so the number of users for any given app directly influences our profit.

Goal:

The main goal for this project is to analyze data and give our developers more insight on which kind of apps are more likely to attract users.

Conclusion:

Throughout this project, I analyzed data for the mobile apps in the App Store and Google Play in order to understand which apps would be profitable for both markets. I concluded that turning a popular book into an app could become profitable for both Google Play and the App Store. The team might include an audible version of the book, trivia, in-app platform to discuss with other users, daily quotes and more within the app.

The two .csv files for analysis: App Store Google PlayStore

Success.ai’s Technographic Data for the North American IT Industry provides unparalleled visibility into the technology stacks, operational frameworks, and key decision-makers powering 30 million-plus businesses across the region’s tech landscape. From established software giants to emerging SaaS startups, this dataset offers verified contacts, firmographic details, and in-depth insights into each company’s technology adoption, infrastructure choices, and vendor partnerships.

Whether you’re aiming to personalize sales pitches, guide product roadmaps, or streamline account-based marketing efforts, Success.ai’s continuously updated and AI-validated data ensures you make data-driven decisions and achieve strategic growth, all backed by our Best Price Guarantee.

Why Choose Success.ai’s North American IT Technographic Data?

1. Comprehensive Technology Insights

   • Access detailed information on software stacks, cloud platforms, hosting providers, cybersecurity tools, CRM solutions, and more.
   • AI-driven validation ensures 99% accuracy, minimizing guesswork and empowering confident engagement with the right tech-focused audiences.

2. Regionally Tailored Focus

   • Includes profiles of IT businesses from Silicon Valley startups to East Coast analytics firms, covering major tech hubs and underserved markets alike.
   • Understand technology adoption patterns influenced by regional trends, regulatory environments, and innovation ecosystems unique to North America.

3. Continuously Updated Datasets

   • Real-time updates reflect emerging vendors, newly adopted tools, infrastructure upgrades, and shifts in IT leadership.
   • Stay aligned with evolving market conditions, competitive landscapes, and customer requirements.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other privacy regulations, ensuring responsible data usage and ethical outreach practices.

Data Highlights:

• 30M+ Verified Business Profiles: Gain insights into software companies, IT consultancies, data analytics providers, cloud integrators, and cybersecurity startups.
• Comprehensive Firmographics: Identify company sizes, revenue ranges, workforce composition, and operational footprints.
• Vendor and Stack Details: Understand which CRMs, ERPs, marketing automation tools, or development frameworks companies rely on.
• Verified Decision-Maker Contacts: Engage with CEOs, CTOs, CIOs, IT directors, DevOps managers, and product leads shaping procurement and integration strategies.

Key Features of the Dataset:

1. Technographic Decision-Maker Profiles

   • Identify and connect with executives, architects, and engineers overseeing vendor selection, digital transformation, and IT investments.
   • Target professionals who influence software procurement, SaaS migrations, and long-term technology roadmaps.

2. Advanced Filters for Precision Targeting

   • Refine outreach by technology categories, usage intensity, company size, region, or industry verticals.
   • Tailor campaigns to align with specific pain points, growth opportunities, or emerging tech trends like AI, IoT, or edge computing.

3. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and boost engagement with IT stakeholders.

Strategic Use Cases:

1. Sales and Account-Based Marketing

   • Present IT solutions, infrastructure services, or software licenses directly to companies with compatible tech stacks.
   • Identify warm leads who already use complementary tools, accelerating deal closures and improving conversion rates.

2. Product Development and Roadmap Planning

   • Analyze common technology adoption patterns, security tools, or workflow integrations to inform product enhancements.
   • Align feature sets with industry standards and emerging stacks, ensuring long-term relevance and customer satisfaction.

3. Competitive Analysis and Market Entry

   • Benchmark against leading IT providers, analyze technology maturity curves, and understand customer preferences for particular platforms.
   • Identify new markets or niches where your offering can fill technology gaps or improve operational efficiency.

4. Partnership and Ecosystem Building

   • Connect with partners offering complementary solutions, integration capabilities, or co-marketing opportunities.
   • Foster alliances with MSPs, VARs, or channel partners who can amplify distribution and support end-to-end solutions.

Why Choose Success.ai?

1. Best Price Guarantee

   • Gain access to premium-quality technographic data at competitive rates, ensuring high ROI for your sales, marketing, and product strategies.

2. Seamless Integration

   • Incorporate verified data into CRM systems, marketing automation platforms, or analytics dashboards via APIs or downloadable formats, streamlining workflows and decision-making.

3....

Data are the foundation of science, and there is an increasing focus on how data can be reused and enhanced to drive scientific discoveries. However, most seemingly “open data” do not provide legal permissions for reuse and redistribution. The inability to integrate and redistribute our collective data resources blocks innovation and stymies the creation of life-improving diagnostic and drug selection tools. To help the biomedical research and research support communities (e.g. libraries, funders, repositories, etc.) understand and navigate the data licensing landscape, the (Re)usable Data Project (RDP) (http://reusabledata.org) assesses the licensing characteristics of data resources and how licensing behaviors impact reuse. We have created a ruleset to determine the reusability of data resources and have applied it to 56 scientific data resources (e.g. databases) to date. The results show significant reuse and interoperability barriers. Inspired by game-changing projects like Creative Commons, the Wikipedia Foundation, and the Free Software movement, we hope to engage the scientific community in the discussion regarding the legal use and reuse of scientific data, including the balance of openness and how to create sustainable data resources in an increasingly competitive environment.

Our dataset is meticulously curated from reputable platforms like G2, Capterra, and Trustpilot, providing you with comprehensive and unbiased information. Collect data such as product names, reviews, discussions, descriptions, pricing, features, and more. Explore the power of our web-scraped software reviews dataset, encompassing millions of software reviews. With our dataset, you can make well-informed decisions based on real user experiences, gaining valuable insights into the pros, cons, and features of various software solutions.

Transparency and trust are at the core of our mission. Our dataset enables you to uncover valuable information about software solutions, empowering you to choose the right option for your specific needs.

This dataset contains a collection of software engineering job listings scraped from LinkedIn. It provides valuable insights into the current job market, job requirements, and company hiring trends.

If you find this dataset useful, don't forget to hit the upvote button! 😊💝

Checkout my top datasets

• Top Spotify Songs in 73 Countries
• Wages by Education in the USA
• Amazon Products Dataset (1.4M Products)
• TMDB 950K Movies
• Amazon Kindle Books (130K Books)

Interesting Task Ideas:

1. Analyze the most in-demand software engineering job titles.
2. Explore the geographical distribution of software engineering job opportunities.
3. Identify the most sought-after programming languages and skills in job descriptions.
4. Determine the average years of experience required for different job levels.

Photo by Hack Capital on Unsplash

Abstract This study discusses a method to determine the root canal anatomic dimension by using e-Vol DX software. The methodology consists in initially establishes the correct positions which will be measured, define the point on the edge of the anatomical structure, and next adjust the intermediate position in the grayscale of CBCT image. Afterward, thin sections (0.10 mm) are obtained from 3D reconstructed slices in the filter for the measurements, in order to determine the edge of the anatomical surface in the axial plane. A replication of positions in 3D mode is done in multiplanar reconstruction (MPR) of CBCT images, where the correct position is established with the aid of a positioning guide. The 3D density is adjusted so that it is in the same dimension as the 2D image, and a dimension calibration occurs to the point where there is a coincidence between 3D and 2D. This calibration is done only at the beginning of the measurement. Next, the intermediate position of the division between the grayscale is verified in the CBCT scan. Once one side has been completed, it is moved to the other side and follows the same guidelines described above. When setting the position of the courses in the other margin, being that 2D mode is used as reference. Thus, one obtains the required measure, being checked in the two points. The creation of this filter in the e-Vol DX software for measurement, and its appropriate management, allows more effective applications when it is desired to obtain diameters of anatomical structures.

The US data processing and hosting services industry is navigating a dynamic environment marked by rising demands and revolutionary trends. As digitalization accelerates, data centers have evolved from simple infrastructure to essential strategic assets. These hubs now power services ranging from cloud computing to advanced data analytics. In 2025, the data processing and hosting service market includes giants like Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). Industry revenue currently sits at $383.8 billion, growing robustly at a CAGR of 9.2% over the past five years, including a 6.2% surge in 2025 alone. Alongside leading tech firms, smaller specialized providers cater to sectors like healthcare, financial services and government agencies with precision-placed data storage solutions. Emerging trends significantly influence the evolution of the US data processing and hosting services industry. Prominent among these is edge computing, a decentralized approach that locates data centers closer to end-user devices. Along with AI and modern data centers, these innovations aim to reduce latency and enhance application performance by minimizing resource usage in data transmission, thereby promoting broader adoption of cloud computing. Despite this transformative growth, the US data processing and hosting services industry faces significant hurdles, including a skill gap, escalating energy costs and escalating cybersecurity threats. This scarcity has heightened the focus on software automation, leading many facilities to implement AI solutions. Though offshoring trends lead to lost business for many participants, this activity is limited and the industry still benefits from strong demand, leading to rising profit. The industry is projected to grow at a CAGR of 2.4% to $431.4 billion by 2030. The future holds a mix of challenges and opportunities for the industry. Strategic investments in human capital and advanced technologies will distinguish industry leaders from laggards. Compliance with evolving data sovereignty and privacy regulations will determine local market competitiveness. Continuous innovation is expected to drive this progress, solidifying data centers' roles as pivotal components shaping the digital landscape ahead.

Business Context

The advent of e-news, or electronic news, portals has offered us a great opportunity to quickly get updates on the day-to-day events occurring globally. The information on these portals is retrieved electronically from online databases, processed using a variety of software, and then transmitted to the users. There are multiple advantages of transmitting new electronically, like faster access to the content and the ability to utilize different technologies such as audio, graphics, video, and other interactive elements that are either not being used or aren’t common yet in traditional newspapers.

E-news Express, an online news portal, aims to expand its business by acquiring new subscribers. With every visitor to the website taking certain actions based on their interest, the company plans to analyze these actions to understand user interests and determine how to drive better engagement. The executives at E-news Express are of the opinion that there has been a decline in new monthly subscribers compared to the past year because the current webpage is not designed well enough in terms of the outline & recommended content to keep customers engaged long enough to make a decision to subscribe.

[Companies often analyze user responses to two variants of a product to decide which of the two variants is more effective. This experimental technique, known as A/B testing, is used to determine whether a new feature attracts users based on a chosen metric.]

Objective

The design team of the company has researched and created a new landing page that has a new outline & more relevant content shown compared to the old page. In order to test the effectiveness of the new landing page in gathering new subscribers, the Data Science team conducted an experiment by randomly selecting 100 users and dividing them equally into two groups. The existing landing page was served to the first group (control group) and the new landing page to the second group (treatment group). Data regarding the interaction of users in both groups with the two versions of the landing page was collected. Being a data scientist in E-news Express, you have been asked to explore the data and perform a statistical analysis (at a significance level of 5%) to determine the effectiveness of the new landing page in gathering new subscribers for the news portal by answering the following questions:

Do the users spend more time on the new landing page than on the existing landing page? Is the conversion rate (the proportion of users who visit the landing page and get converted) for the new page greater than the conversion rate for the old page? Does the converted status depend on the preferred language? Is the time spent on the new page the same for the different language users?

Data Dictionary

The data contains information regarding the interaction of users in both groups with the two versions of the landing page.

user_id - Unique user ID of the person visiting the website group - Whether the user belongs to the first group (control) or the second group (treatment) landing_page - Whether the landing page is new or old time_spent_on_the_page - Time (in minutes) spent by the user on the landing page converted - Whether the user gets converted to a subscriber of the news portal or not language_preferred - Language chosen by the user to view the landing page

An industrial questionnaire survey where a total of 33 practitioners, of varying roles, from 18 companies are tasked to compare two decision models for asset selection.

The objective of the study was to evaluate what characteristics of decision models for asset selection that determine industrial practitioner preference of a model when given the choice of a decision-model of high precision or a model with high speed.

The dataset was originally published in DiVA and moved to SND in 2024.

1. Introduction

This data corpus was produced during the RoBivaL project, by robotics and agriculture researchers from DFKI (German Research Center for Artificial Intelligence, Robotics Innovation Center) and HSO (Hochschule Osnabrück, University of Applied Sciences, Agro-Technicum), between August 2021 and October 2023.

The RoBivaL project compared different robot locomotion concepts from both space research and agricultural applications on the basis of experiments conducted under agricultural conditions. Four robot systems were used, two of which (ARTEMIS & SherpaTT) have their origin in futuristic space applications, while the other two (Naio Oz & BoniRob) were developed specifically for agriculture.

The robots were subjected to six experiments, addressing different challenges and requirements for agricultural applications. Since real-world soil conditions usually change with the seasons and can be expected to have a crucial impact on robot performance, the experimental soil conditions were controlled and varied on the two dimensions moisture (dry, moist, wet) and density (tilled, compacted), resulting in six soil condition options. Depending on the specific objectives, each experiment was conducted either on a subset or on all available soil conditions. The experiments were:

Straight travel: Determine variations of travel speed and directional stability under different soil moisture and densitiy levels, and determine the soil deformation and compaction caused by a traverse under given initial soil conditions.

Turn around: Examine the effect of steering on soil deformation with moist and tilled soil.

Repeated rollover: Investigate the effects of repeated axle rollovers on soil compaction, determined by measuring the soil penetration resistance.

Tensile force: Compare the maximum exerted tractive force under different soil moisture and densitiy levels, and gain insights how varying soil conditions affect the performance of each system during traction.

Sill crossing: Determine the ability to overcome different types of obstacles, and compare relevant system characteristics, e.g. ground clearance, or center of mass.

Obstacle avoidance: Demonstrate SherpaTT's ability to step over an obstacle without contact, thanks to its actively controlled suspension.

Field conditions and robot behavior were monitored with various sensors and measuring devices, partly on the robots and partly in the field, in order to document the experiment execution, and to determine the robot performance. The data capturing devices, their roles and deployments are summarized in Table 1.

Table 1: Overview of data capturing devices

Device on System Device on System and in Field Device in Field

System Monitoring

IMU

Force logger

RTK-GPS

Stopwatch

Compass

System and Field Monitoring

Video camera

Ruler

Field Monitoring

Tilt laser scanner

Penetrometer

Moisture meter

1. File tree

The data corpus is stored in a file tree, which is divided into three main sections:

Logbook

Data

Specification

Each section is described in detail in the following chapters.

Here is a complete overview of the file tree:

logbook/ csv/ experiment.csv parameter.csv possible_robot.csv possible_value.csv robot.csv run_${experiment}.csv database/ logbook.sqlite schema/ logbook_entities.png logbook_schema.sqlite src/ create_sqlite_database.sh data/ ${experiment}/ ${robot}/ ${run}/ ${datafile} specification/ experiment/ experiment.md ${experiment}/ img/ ${experiment}.png ${experiment}-description.md ${experiment}.json parameter/ parameter.json datafile/ ${datafile_stem}.json robot/ ${robot}/ system_properties.json robots.png sensor/ ${sensor}.json software/ ${software}.json

The variables ${experiment}, ${robot}, ${run}, ${datafile}, ${datafile_stem}, ${sensor}, and ${software} are explained in the context of each respective section.

2.1. Logbook

The Logbook is a small relational database. Primarily, it contains one table for every experiment, where each row represents an experiment run. These tables capture all facts and measurements about a run that can be expressed as scalar values, including

start and end times,

independent variables, (e.g. run track length, soil moisture and density level, name of the tested robot, commanded speed, etc.),

dependent variables, (e.g. wheel track depth and width, heading and offset of the robot after a run, etc.),

comments about unforeseen events.

Additional measurements that are better managed in separate data files are stored in the Data section of the corpus, which is discussed in Chapter 2.2.

Besides the run tables, the Logbook contains tables to specify the experiments and the available robots, as well as the parameters that are present in the run tables. These additional tables have some overlap with the Specification section of the data corpus, which is discussed in Chapter 2.3. In the Logbook, the specifying tables were used during the run data acquisition in the field, in order to facilitate and live-validate the data entry.

The entire Logbook is stored in the SQLite file logbook/database/logbook.sqlite. Users who prefer other tools than SQLite can find the constituting tables as CSV files in the directory logbook/csv/. The Logbook schema and entity-relationship-diagram are in the logbook/schema/ directory. The database can be recreated from the schema and CSV files with the Bash script logbook/src/create_sqlite_database.sh

The full Logbook file tree is as follows:

logbook/ csv/ experiment.csv parameter.csv possible_robot.csv possible_value.csv robot.csv run_${experiment}.csv database/ logbook.sqlite schema/ logbook_entities.png logbook_schema.sqlite src/ create_sqlite_database.sh

The ${experiment} variable refers to the keys at the top level of the data tree, which is discussed in Chapter 2.2.1.

2.2. Data

The Data section of the corpus contains all measurements that would be impractical to store directly in a run table of the Logbook database, but are better managed as separate data files. In most cases, these are time series issued by a particular sensor, and/or by a software running on one of the robots.

In addition to the data strictly necessary for evaluation purposes in RoBivaL, there are some extra data streams that were routinely captured on the ARTEMIS robot which were not available for the other systems, as well as data from experiment runs that were considered invalid or performed for testing.

For data recording on the robots, two different approaches were used, due to different sensor availabilites. In the case of SherpaTT, Naio Oz, and BoniRob, a custom-built, stand-alone embedded PC in a battery-equipped box for autonomous operation (aka Sensor Box) was attached to the given robot. The Sensor Box includes IMU and GNSS sensors, which are of primary relevance for the experiments. In the case of ARTEMIS, built-in sensors and data logging functionality could be used that relies on similar sensors as the Sensor Box, and employs the same software infrastructure for data recording, based on the Rock software framework.

Table 2 gives an overview of all available data files with a short description and possible sources, including sources outside of the robots (i.e. Force logger, Penetrometer, Tilt scanner, and Video camera). A thorough specification of the data files and their respective hard- and software resources is in the Specification section of the corpus, which is discussed in Chapter 2.3.

Table 2: Overview of file types in the Data section

Data file name Description Possible sources

bogie_dispatcher.motion_status.csv

Time series of status of the joint of the mobile base ARTEMIS

force.csv

Time series of momentary tractive force exerted by a robot, measured at regular intervals Force logger

gnss.nwu_position_samples.csv

Time series of cartesian positions measured by GPS in North-West-Up coordinate system ARTEMIS, Sensor Box

gnss.position_samples.csv

Time series of cartesian positions measured by GPS in robot coordiante system ARTEMIS, Sensor Box

gnss.solution.csv

Time series of raw values from the GPS sensor ARTEMIS, Sensor Box

joystick_converter.motion_command.csv

Time series of joystick commands interpreted as motion commands ARTEMIS

motion_controller.actuators_command.csv

Time series of commands for joints of mobile base ARTEMIS

odometry.odometry_samples.csv

Time series of aggregated pose of the odometry component ARTEMIS

penetrometer-after.json

penetrometer-before.json

penetrometer.json

Penetrometer measurements of the soil penetration resistance at multiple depth levels, before or after the experiment run Penetrometer

tiltscan-before-front.asc

tiltscan-before-rear.asc

tiltscan-before-left.asc

tiltscan-before-right.asc

tiltscan-before.png

tiltscan-before.txt

tiltscan-after-front.asc

tiltscan-after-rear.asc

tiltscan-after-left.asc

tiltscan-after-right.asc

tiltscan-after.png

tiltscan-after.txt

Tilt scanner measurements of the track surface, before or after the experiment run, on the front, rear, left, or right side of the robot, in raw pointcloud (.asc) or rasterized and consolidated (.png, .txt) form Tilt scanner

video.mp4.defaced.mp4

Video recordings of the robot performing the experiment run. Postprocessed to remove faces for privacy protection. Video camera

xsens.calibrated_sensors.csv

Raw readings of inertial unit ARTEMIS, Sensor Box

xsens.orientation_samples.csv

Integrated Cartesian pose measured by

Snapshot img

Assisted Living Software Market Size 2024-2028

The Assisted Living Software Market size is forecast to increase by USD 682.51 million, at a CAGR of 15.9% between 2023 and 2028. The market's growth rate is influenced by various factors, notably the ageing baby boomer population requiring more comprehensive healthcare services. Additionally, there's an increased need for electronic medication administration record (eMAR) integration, driven by the rising complexity of medication management. Furthermore, the growing demand for improved quality of care, especially in long-term care facilities and hospitals, is fostering the adoption of advanced healthcare solutions. These trends indicate a significant shift towards digitalization and technology-enabled healthcare services to meet the evolving needs of patients and healthcare providers alike. The assisted living software market industry report includes key drivers, trends, and challenges of the market during the forecasted period.

Assisted Living Software Market Overview

For More Highlights About the Assisted Living Software Market Research Report, Download Free Sample in a Minute

Market Dynamics and Customer Landscape

The market is driven by the increasing demand for real-time services in the healthcare sector. Patient care, especially in long-term healthcare, is being revolutionized through automation and advanced analytical tools. Integration of various systems, including eMAR and workflow automation, enhances efficiency and patient details. Governments' advances in reimbursements support the adoption of these technologies. However, concerns about data breaches highlight the importance of cloud security and open source solutions. Companies like BEK Medical, an Israel-based company, offer a wide range of home medical equipment, including compression stockings and diabetic testing equipment, at affordable prices, meeting the rising assisted living software market demand for patient healthcare and prescription processing. Further, the market advancing with solutions integrating advanced analytics for enhanced management of mobility products and incontinence supplies. These technologies streamline operations, ensuring efficient care delivery and inventory management in assisted living facilities. By leveraging data-driven insights, providers optimize resident care and operational workflows, catering to the growing demand for integrated solutions in the healthcare sector.

Key Assisted Living Software Market Driver

One of the key factors driving the market development is the aging baby boomer population. The most significant demographic trends across the world include the growing population of individuals aged 65 years and older. In addition, baby boomers are individuals born between 1946 and 1964. Furthermore, as the number of retired baby boomers who are aging increases, there will be an increase in the need for nursing care.

Moreover, the obesity rate among elderly people is on the rise, which has resulted in more people facing the risk of disability and chronic diseases. Therefore, in such cases, it becomes challenging for family members to take care of their older family members. Thus, old age communities are suitable options for a comfortable and healthy life as they offer options along with certified nursing care. Hence, such factors are positively impacting the market which in turn drives the market expansion during the forecast period.

Significant Assisted Living Software Market Trends

A key factor shaping the market development is the increased adoption of analytics in software. Big data and analytics are gaining traction in the industry as various analytical and statistical modeling tools are being used in facilities to get structured and meaningful insights about operations. In addition, assisted living care provider organizations are generating a massive amount of data, such as resident health information records, and non-clinical data, such as administrative and financial data.

Moreover, the increasing volume, variety, and velocity of clinical and non-clinical data have compelled organizations to implement statistical tools, data science, and deal mining technology. Furthermore, the software integrated with analytics tools is helping care providers to determine the resident data, insurance data, prescription transaction data, prescription processing, and refill inventories for a specific period. Hence, such factors are driving assisted living software market growth during the forecast period.

Major Assisted Living Software Market Restrain

Data privacy and security concerns regarding software are one of the key challenges hindering market development. The shift to assisted living in the digital age is exciting, but the challenges associated with protecting data are getting more complex. In addition, assisted living care providers must ensure the safety of data and malware-free computers, lap

Description

The datasets demonstrate the malware economy and the value chain published in our paper, Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access, 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 Creative Commons Attribution 4.0 International License.

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

Billy Bob Brumley (Tampere University, Tampere, Finland)

Juha Nurmi (Tampere University, Tampere, Finland)

Mikko Niemelä (Cyber Intelligence House, Singapore)

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: AccountAccessSet, MalwareInfectionSet, and VictimAccessSet.

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software’s capabilities to achieve the most accurate and credible results.

The size of the Data Privacy Software Market was valued at USD 2.76 USD Billion in 2023 and is projected to reach USD 25.26 USD Billion by 2032, with an expected CAGR of 37.2% during the forecast period. Recent developments include: November 2023 – Protiviti India entered a partnership with Riskconnect to help companies in India bring all aspects of risk under one roof through an integrated risk management technology., July 2023 – Trust Arc introduced a new Truste EU-U.S. data privacy framework verification to help businesses transfer personal data from the EU to the U.S. in compliance with the EU and GDPR laws., April 2023 – Avepoint and Tech Data expanded their partnership for providing Microsoft 365 data management solutions in Japan and Asia Pacific. The extended partnership will cover Indonesia, India, Vietnam, Malaysia, Singapore, and Hong Kong., January 2023 - Sourcepoint launched a solution, Vendor Trace, to offer enterprises with a flexible evaluation of vendor behavior on their websites. With the help of Vendor Trace, users can isolate susceptibilities in third-party advertising and marketing technologies and determine the responsible parties., September 2022 - BigID launched data deletion abilities to minimize risk and accelerate compliance. The new advancement permits enterprises to effectively and quickly delete sensitive and personal data across various data stores such as Google Drive, AWS, Teradata, and others., October 2022 - Securiti launched the first Data Control cloud that facilitates enterprises with key obligations over data privacy, security, compliance, and governance. The new offerings developed a combined layer of data intelligence and controls across various clouds, such as public cloud, private cloud, data clouds, and SaaS. , March 2022 - AvePoint announced the addition of ransomware detection to its data protection proficiencies. The new addition proactively identifies apprehensive behavior within Microsoft’s OneDrive while reducing disruption to collaboration and productivity. Other features included in ransomware detection are faster investigation, early event detection, and quicker restoration of backup data.. Key drivers for this market are: Rising Adoption of IoT Devices to Aid Global Data Privacy Software Market Growth. Potential restraints include: Low Awareness and Insufficient Knowledge About Software Impede Industry Growth. Notable trends are: Integration of AI and ML to Surge Demand for Data Privacy Solutions.

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Software Consulting Market Size 2024-2028

The software consulting market size is forecast to increase by USD 312.4 billion at a CAGR of 15.2% between 2023 and 2028.

The market is experiencing significant growth, driven by several key trends. The increasing adoption of cloud-based services is one such trend, as businesses seek to reduce IT infrastructure costs and improve scalability. Another trend is the rise of IT as a service, where organizations outsource their IT needs to consulting firms to focus on their core business functions. Additionally, the number of cyberattacks has increased, leading to a higher demand for cybersecurity services from consulting firms. These trends present both opportunities and challenges for market participants. On the one hand, they offer potential for growth and innovation. On the other hand, they require consulting firms to stay abreast of the latest technologies and threats to meet client needs effectively.
Overall, the market is poised for continued expansion, with a focus on delivering value-added services to clients in a rapidly evolving technological landscape.

What will be the Size of the Software Consulting Market During the Forecast Period?

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Technological innovation continues to shape the market, with digital solutions gaining traction in areas such as digital payments, blockchain technology, and digital wallets. Large enterprises are significant players In the market, leveraging the expertise of software consulting firms to implement complex IT infrastructure and stay competitive in a rapidly evolving technological landscape. The market is expected to grow further as businesses continue to prioritize technological innovation, technical skills, and data security to drive growth and efficiency.

How is this Software Consulting Industry segmented and which is the largest segment?

The software consulting industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  BFSI
  IT and Telecom
  Manufacturing
  Healthcare
  Others


Type

  Large enterprise
  Small and medium enterprises


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By End-user Insights

The bfsi segment is estimated to witness significant growth during the forecast period. Software consulting services play a crucial role In the banking, financial services, and insurance (BFSI) sector as companies seek to optimize operations, enhance security, and improve customer support. The global BFSI industry's digital transformation initiatives create a significant opportunity for software consulting providers. Moreover, the increasing adoption of cloud computing In the BFSI sector, driven by the need to securely store customer data, fuels demand for software security consulting services. Given the vast amount of sensitive data In the cloud, software consulting firms offering security solutions to BFSI clients are poised for substantial growth. Key areas of focus include cybersecurity, cloud migration, integration, and management.

Additionally, emerging technologies such as Software-as-a-Service (SaaS), Artificial Intelligence (AI), Machine Learning (ML), Blockchain technology, and Digital payments are transforming the BFSI landscape, further expanding the market for software consulting services.

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The BFSI segment was valued at USD 33.60 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 32% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North America the market is a substantial and expanding segment of the global software consulting industry. Driven by business digitization and the increasing demand for enterprise software solutions, the North America region is expected to lead the market. The United States, as the largest economy In the region, contributes significantly to this market. U.S. Companies invest heavily in enterprise software and seek software consulting services to optimally choose and implement these solutions. This trend is driven by the need to boost business efficiency, streamline operations, and enhance customer-focused technologies. Key areas of focus include IT setup, business processes, cloud computing, data analytics, software adoption, social software, delivery speed, virtual consulting platforms, automation, softwa

Budgeting and Forecasting Software Market Outlook

According to our latest research, the global budgeting and forecasting software market size reached USD 5.2 billion in 2024, with a robust compound annual growth rate (CAGR) of 10.8% from 2025 to 2033. The market is expected to grow significantly, reaching a forecasted value of USD 13.1 billion by 2033, driven by increasing digital transformation initiatives and the growing demand for advanced financial analytics across industries. This growth is primarily attributed to the need for real-time financial insights, improved accuracy in forecasting, and the rising adoption of cloud-based solutions worldwide.

A critical growth factor for the budgeting and forecasting software market is the rising complexity of business operations and the need for accurate, data-driven financial decision-making. As organizations expand globally, managing multi-currency transactions, diverse regulatory requirements, and complex supply chains becomes increasingly challenging. Budgeting and forecasting software provides powerful tools to consolidate financial data, automate repetitive tasks, and generate actionable insights, enabling finance teams to make informed decisions swiftly. The integration of artificial intelligence (AI) and machine learning (ML) in these platforms further enhances predictive capabilities, allowing companies to anticipate market shifts, optimize resource allocation, and minimize financial risks. This technological advancement is particularly crucial in volatile economic environments, where agility and precision in financial planning can determine organizational success.

Another significant driver for market expansion is the surge in cloud adoption across all industry verticals. Cloud-based budgeting and forecasting software offers unparalleled scalability, cost-effectiveness, and accessibility, making it especially attractive to small and medium enterprises (SMEs) seeking to modernize their financial processes without heavy upfront investments. The shift towards remote and hybrid work models has further accelerated the demand for cloud solutions, as they facilitate seamless collaboration among geographically dispersed teams. Additionally, cloud platforms provide robust security features, automatic updates, and easy integration with other enterprise applications, ensuring business continuity and data integrity. As organizations prioritize digital transformation, cloud-based financial planning tools are becoming integral to their IT strategies, propelling market growth.

The increasing regulatory scrutiny and compliance requirements in sectors like banking, healthcare, and government are also fueling the adoption of budgeting and forecasting software. These industries face stringent reporting standards and must ensure transparency, accuracy, and auditability in their financial operations. Advanced software solutions help organizations automate compliance processes, generate real-time reports, and maintain comprehensive audit trails, thereby reducing the risk of errors and penalties. Furthermore, the ability to simulate various financial scenarios and assess the impact of regulatory changes empowers organizations to proactively manage compliance risks. As regulatory landscapes evolve, the demand for sophisticated budgeting and forecasting tools is expected to rise, reinforcing the market’s upward trajectory.

Regionally, North America continues to dominate the budgeting and forecasting software market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of advanced technologies, presence of leading software vendors, and strong focus on financial transparency drive market growth in these regions. Meanwhile, Asia Pacific is witnessing the fastest growth, fueled by rapid digitalization, expanding SME sector, and increasing investments in cloud infrastructure. Latin America and the Middle East & Africa are also emerging as lucrative markets, supported by growing awareness of the benefits of financial automation and government initiatives to promote digital transformation. The global landscape is characterized by intense competition, continuous innovation, and a strong emphasis on customer-centric solutions.

Discover the booming Management Decision market! Explore a $6.55B industry projected to reach $18.65B by 2033, driven by AI, cloud solutions & data analytics. Learn about key players, growth drivers, and regional trends. Recent developments include: November 2022 - IBM introduced Business Analytics Enterprise, a more advanced version of the program allowing companies to acquire a thorough perspective of the data sources across their entire business. The program will assist in business intelligence planning, budgeting, reporting, forecasting, and dashboard capabilities., January 2022 - The cloud platform LambdaTest, introduced Test Analytics, a solution to enable better decision-making. With the help of highly customized dashboards provided by LambdaTest Test Analytics, DevOps teams can monitor the status and effectiveness of testing across numerous LambdaTest product lines in a single view, enabling businesses to make better decisions., The Pennsylvania ed-tech company Frontline Education launched HR Capital Analytics Tool. The program will examine absenteeism trends and patterns, comprehend information about teacher candidates and open positions, determine staffing needs by position, quickly fill openings for substitutes, plan recruitment, and hiring strategies, and share professional development opportunities. The tool will increase administrators' ability to expedite decision-making and strategically plan.. Key drivers for this market are: Increasing need for business agility which requires faster and efficient decision making, Increasing demand for Decision Analytics in BFSI sector to drive the market. Potential restraints include: Increasing need for business agility which requires faster and efficient decision making, Increasing demand for Decision Analytics in BFSI sector to drive the market. Notable trends are: BSFI Sector is Expected to Hold Significant Share.

This dataset contains raw data and processed data from the Dataverse Community Survey 2022. The main goal of the survey was to help the Global Dataverse Community Consortium (GDCC; https://dataversecommunity.global/) and the Dataverse Project (https://dataverse.org/) decide on what actions to take to improve the Dataverse software and the larger ecosystem of integrated tools and services as well as better support community members. The results from the survey may also be of interest to other communities working on software and services for managing research data. The survey was designed to map out the current status as well as the roadmaps and priorities of Dataverse installations around the world. The main target group for participating in the survey were the people/teams responsible for operating Dataverse installations around the world. A secondary target group were people/teams at organizations that are planning to deploy or considering deploying a Dataverse installation. There were 34 existing and planned Dataverse installations participating in the survey.