Compare OpenAlex Research Scraper Side by Side

Need to compare openalex research scraper across targets? Thirdwatch's OpenAlex Research Scraper turns it into structured rows — the fields you need, ready to export.

Skip the setup: Run this as a ready-to-go task on Apify — pre-loaded with the configuration from this guide.

Why Compare side by side works

Put the same fields side by side for every target — the deltas are where decisions happen. This guide shows the configuration that pulls openalex research scraper data from OpenAlex Research Scraper.

How does this compare to the alternatives?

Manual copy-paste works for a handful of rows; dedicated per-site scrapers on other platforms either gate the same data behind sign-in or charge flat monthly fees regardless of usage.

Why this Actor

• Purpose-built for OpenAlex Research Scraper — no generic crawling config to tune
• Pay-per-result pricing — you pay only for rows that come back
• Structured output ready for JSON/CSV export or API consumption
• No login, cookies, or API keys needed for the public surfaces it reads

How to do it in 3 steps

Step 1: Configure the input

Set the inputs as shown below — queries takes the targets, and the optional fields bound the run size.

Step 2: Run the Actor

Run it from the console, the API, or the linked saved task. One dataset row is written per record.

Step 3: Use the output

Every row carries the fields shown in the sample — export as JSON, CSV, or feed it straight into your pipeline.

{
  "queries": [
    "artificial intelligence"
  ],
  "maxResultsPerQuery": 20
}

Each dataset row looks like:

{
  "openalex_id": "https://openalex.org/W1748592323",
  "doi": "https://doi.org/10.2196/mhealth.4328",
  "title": "Review and Evaluation of Mindfulness-Based iPhone Apps",
  "publication_year": 2015,
  "publication_date": "2015-08-19",
  "work_type": "article",
  "language": "en",
  "authors": [
    {
      "id": "https://openalex.org/A5039826148",
      "name": "Madhavan Mani",
      "position": "first"
    },
    {
      "id": "https://openalex.org/A5063079719",
      "name": "David J. Kavanagh",
      "position": "middle"
    },
    {
      "id": "https://openalex.org/A5023673789",
      "name": "Leanne Hides",
      "position": "middle"
    },
    {
      "id": "https://openalex.org/A5032951181",
      "name": "Stoyan Stoyanov",
      "position": "last"
    }
  ],
  "institutions": [
    "Queensland University of Technology"
  ],
  "abstract": "BACKGROUND: There is growing evidence for the positive impact of mindfulness on wellbeing. Mindfulness-based mobile apps may have potential as an alternative delivery medium for training. While there are hundreds of such apps, there is little information on their quality. OBJECTIVE: This study aimed to conduct a systematic review of mindfulness-based iPhone mobile apps and to evaluate their quality using a recently-developed expert rating scale, the Mobile Application Rating Scale (MARS). It also aimed to describe features of selected high-quality mindfulness apps. METHODS: A search for \"mindfulness\" was conducted in iTunes and Google Apps Marketplace. Apps that provided mindfulness training and education were included. Those containing only reminders, timers or guided meditation tracks were excluded. An expert rater reviewed and rated app quality using the MARS engagement, functionality, visual aesthetics, information quality and subjective quality subscales. A second rater provided MARS ratings on 30% of the apps for inter-rater reliability purposes. RESULTS: The \"mindfulness\" search identified 700 apps. However, 94 were duplicates, 6 were not accessible and 40 were not in English. Of the remaining 560, 23 apps met inclusion criteria and were reviewed. The median MARS score was 3.2 (out of 5.0), which exceeded the minimum acceptable score (3.0). The Headspace app had the highest average score (4.0), followed by Smiling Mind (3.7), iMindfulness (3.5) and Mindfulness Daily (3.5). There was a high level of inter-rater reliability between the two MARS raters. CONCLUSIONS: Though many apps claim to be mindfulness-related, most were guided meditation apps, timers, or reminders. Very few had high ratings on the MARS subscales of visual aesthetics, engagement, functionality or information quality. Little evidence is available on the efficacy of the apps in developing mindfulness.",
  "cited_by_count": 547,
  "is_retracted": false
}

What to watch for

Rate the targets reasonably — large batches benefit from modest concurrency. Source sites may throttle heavy bursts; retry once and widen filters if a target surface is thin.

Related use cases

• Feed OpenAlex Research Scraper Data Into Your Pipeline
• Extract OpenAlex Research Scraper at Scale
• Monitor OpenAlex Research Scraper on a Schedule
• Export OpenAlex Research Scraper to CSV
• All Thirdwatch use-case guides

Run the OpenAlex Research Scraper on Apify Store — pay per result, free to try, no credit card to test.