This repository contains the datasets and evaluation results of our study. For a detailed overview regarding the provided materials, please refer to README.md.

Notice: You can check the new version 0.9.6 at the official page of Information Management Lab and at the Google Data Studio as well.

Description of the Report and Topic Justification

Now that the ICTs have matured, Information Organizations such as Libraries, Archives and Museums, also known as LAMs, proceed into the utilization of web technologies that are capable to expand the visibility and findability of their content. Within the current flourishing era of the semantic web, LAMs have voluminous amounts of web-based collections that are presented and digitally preserved through their websites. However, prior efforts indicate that LAMs suffer from fragmentation regarding the determination of well-informed strategies for improving the visibility and findability of their content on the Web (Vállez and Ventura, 2020; Krstić and Masliković, 2019; Voorbij, 2010). Several reasons related to this drawback. As such, administrators’ lack of data analytics competency in extracting and utilizing technical and behavioral datasets for improving visibility and awareness from analytics platforms; the difficulties in understanding web metrics that integrated into performance measurement systems; and hence the reduced capabilities in defining key performance indicators for greater usability, visibility, and awareness.

In this enriched and updated technical report, the authors proceed into an examination of 504 unique websites of Libraries, Archives and Museums from all over the world. It is noted that the current report has been expanded by up to 14,81% of the prior one Version 0.9.5 of 439 domains examinations. The report aims to visualize the performance of the websites in terms of technical aspects such as their adequacy to metadata description of their content and collections, their loading speed, and security. This constitutes an important stepping-stone for optimization, as the higher the alignment with the technical compliencies, the greater the users’ behavior and usability within the examined websites, and thus their findability and visibility level in search engines (Drivas et al. 2020; Mavridis and Symeonidis 2015; Agarwal et al. 2012).

One step further, within this version, we include behavioral analytics about users engagement with the content of the LAMs websites. More specifically, web analytics metrics are included such as Visit Duration, Pages per Visit, and Bounce Rates for 121 domains. We also include web analytics regarding the channels that these websites acquire their users, such as Direct traffic, Search Engines, Referral, Social Media, Email, and Display Advertising. SimilarWeb API was used to gather web data about the involved metrics.

In the first pages of this report, general information is presented regarding the names of the examined organizations. This also includes their type, their geographical location, information about the adopted Content Management Systems (CMSs), and web server software types of integration per website. Furthermore, several other data are visualized related to the size of the examined Information Organizations in terms of the number of unique webpages within a website, the number of images, internal and external links and so on.

Moreover, as a team, we proceed into the development of several factors that are capable to quantify the performance of websites. Reliability analysis takes place for measuring the internal consistency and discriminant validity of the proposed factors and their included variables. For testing the reliability, cohesion, and consistency of the included metrics, Cronbach’s Alpha (a), McDonald’s ω and Guttman λ-2 and λ-6 are used.
- For Cronbach’s, a range of .550 up to .750 indicates an acceptable level of reliability and .800 or higher a very good level (Ursachi, Horodnic, and Zait, 2015). - McDonald’s ω indicator has the advantage to measure the strength of the association between the proposed variables. More specifically, the closer to .999 the higher the strength association between the variables and vice versa (Şimşek and Noyan, 2013). - Gutman’s λ-2 and λ-6 work verifiably to Cronbach’s a as they estimate the trustworthiness of variance of the gathered web analytics metrics. Low values less than .450 indicate high bias among the harvested web metrics, while values higher than .600 and above increase the trustworthiness of the sample (Callender and Osburn, 1979). -Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test of Sphericity indicators are used for measuring the cohesion of the involved metrics. KMO and Bartlett’s test indicates that the closer the value is to .999 amongst the involved items, the higher the cohesion and consistency of them for potential categorization (Dziuban and S...

A system is identified by constructing logical boundaries around a set of processes, communications, storage, and related resources. The elements within these boundaries constitute a single system requiring a security plan. Each element of the system must (1) be under the same direct management control, (2) have the same function or mission objective, (3) have essentially the same operating characteristics and security needs, and (4) reside in the same general operating environment. This service produces a list of FISMA Systems that belong to DHS Components.

A web API service for validating and converting unit expressions from the Unified Code for Units of Measure (UCUM). http://unitsofmeasure.org/trac

Preliminary research efforts regarding Social Media Platforms and their contribution to website traffic in LAMs. Through the Similar Web API, the leading social networks (Facebook, Twitter, Youtube, Instagram, Reddit, Pinterest, LinkedIn) that drove traffic to each one of the 220 cases in our dataset were identified and analyzed in the first sheet. Aggregated results proved that Facebook platform was responsible for 46.1% of social traffic (second sheet).

The NSF Award Search web API provides a web API interface to the Research.gov's Research Spending and Results data, which provides NSF research award information from 2007.

DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

You will find well-rounded ways to scout the competitors:

• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

Context

Spotify for Developers offers a wide range of possibilities to utilize the extensive catalog of Spotify data. One of them are the audio features calculated for each song and made available via the official Spotify Web API.

There are several datasets on Kaggle published in the past containing similar data. However, as of December 2018 this dataset is the most recent one (latest data retrieval was on 3rd December 2018) as well as the biggest one (more than 116k unique songs).

More information about the collection process as well as some additional information can be found in this github repo.

Content

Each song (row) has values for artist name, track name, track id and the audio features itself (for more information about the audio features check out this doc from Spotify).

Additionally, there is also a popularity feature included in this dataset. Please note that Spotify recalculates this value based on the number of plays the track receives so it might not be correct value anymore when you access the data.

Acknowledgements

Credit goes entirely to Spotify for providing this data via their Web API.

➡️ DOCS With just the School LinkedIn Profile URL, you can get the structured data of a school/educational institute with tons of data points. Check out our API Docs at ➡ nubela.co/proxycurl/docs

➡️ PRICING MODEL Get the data using our API at just $0.01/credit, with each successful request using up only 1 credit. If you need more advanced data points, use more credits for each API request.

➡️ COVERAGE Our School Profile API covers profiles globally.

➡️ FRESHNESS 88% of our data is fetched in real time, and the API takes 2-3 seconds to complete. If freshness is not a priority, you can choose cached results, which returns immediately.

➡️ LEGAL COMPLIANCE All our data and procedures are in place that meet major legal compliance requirements such as GDPR, CCPA. We help you be compliant too.

CIMIS data is available to the public free of charge via a web Application Programming Interface (API). The CIMIS Web API delivers data over the REST protocol from an enterprise production platform. The system provides reference evapotranspiration (ETo) and weather data from the CIMIS Weather Station Network and the Spatial CIMIS System. Spatial CIMIS provides daily maps of ETo and solar radiation (Rs) data at 2-km grid by coupling remotely sensed satellite data with point measurements from the CIMIS weather stations. In summary, the data provided through the CIMIS Web API is comprised by a) Weather and ETo data registered at the CIMIS Weather Station Network (more than 150 stations located throughout the state of California and b) Spatial CIMIS System data that provides statewide ETo and solar radiation (Rs) data as well as aeraged ETo by zip-codes. The RESTful HTTP services reach a broader range of clients; including Wi-Fi aware irrigation smart controllers as well as browser and mobile applications, all while expanding the delivery options by providing data in either JSON or XML formats.

Web API responses for the MOBILESoft 2015 paper.

PO.DAAC provides several ways to discover and access physical oceanography data, from the PO.DAAC Web Portal to FTP access to front-end user interfaces (see http://podaac.jpl.nasa.gov). That same data can also be discovered and accessed through PO.DAAC Web Services, enabling efficient machine-to-machine communication and data transfers.

The MODIS Web service provides users with subsets of MODIS Land Products through standards based SOAP (Simple Object Access Protocol) Web service.

MODAPS Web Services API for LANCE-MODIS. This Application Programming Interface (API) encompasses a library of functions that provides programmatic data access to MODIS near-real-time data products. These routines enable both SOAP and REST based web service calls against the LANCE-MODIS near-real-time data archives. These routines mirror existing LAADS Web services.

🇺🇸 United States

The Satellite Situation Center (SSC) web services allow a software developer to use portions of the SSC software in their own applications. SSC is a system to cast geocentric spacecraft location information into a framework of (empirical) geophysical regions and mappings of spacecraft locations along lines of the Earth’s magnetic field. This capability is one key to mission science planning (both single missions and coordinated observations of multiple spacecraft with ground-based investigations) and to subsequent multi-mission data analysis. The Space Physics Data Facility (SPDF) is the archive of non-solar data for the Heliospheric Science Division (HSD) at NASA's Goddard Space Flight Center.

This web API contains FY 2001-FY 2016 NSF funding rates for competitive research proposals by organizational unit. The funding rate is calculated by dividing the number of awards by the number of awards and declines.

This Choose Work webpage provides access to a tool for doing a direct search for Ticket to Work service providers by zip code. It functions similarly to a web based API.

The API Management Tools market is booming, projected to reach $15 billion by 2033 with a 15% CAGR. This in-depth analysis explores market drivers, trends, restraints, and key players like IBM, Microsoft, and Postman, covering segments like web-based and on-premise solutions across North America, Europe, and Asia-Pacific.

The Early Detection Research Network (EDRN) runs a distributed service known as the EDRN Resource Network Exchange (ERNE). ERNE is a virtual specimen bank. It unifies participating sites specimen databases using both classic OODT and Agile OODT.