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.

Research datasets about top signals for covid 19 (coronavirus) for study into Google Trends (GT) and with SEO metrics

Website

The study is currently published on https://covidgilance.org website (in french)

Datasets description

covid signals -> |selection| -> 4 dataset -> |serp.py| -> 4 serp datasets -> |aggregate_serp.pl| -> 4 aggregated dataset of serp -> |prepare datasets| -> 4 ranked top seo dataset

Original lists of signals (mainly covid symptoms) - dataset

Description: contain the original relevant list of signals for covid19 (here list of queries where you can see, in GT, a relevant signal during the covid 19 period of time)
Name: covid_signal_list.tsv

List of content:

- id: unique id for the topic
- topic-fr: name of the topic in French
- topic-en: name of the topic in English
- topic-id: GT topic id
- keyword fr: one or several keywords in French for GT
- keyword en: one or several keywords in English for GT
- fr-topic-url-12M: link to 12-months French query topic in GT in France
- en-topic-url-12M: link to 12-months English query topic in GT in US
- fr-url-12M: link to 12-months French queries in GT in France
- en-url-12M: link to 12-months English queries topic in GT in US
- fr-topic-url-5M: link to 5-months French query topic in GT in France
- en-topic-url-5M: link to 5-months English query topic in GT in US
- fr-url-5M: link to 5-months French queries in GT in France
- en-url-5M: link to 5-months English queries topic in GT in US

Tool to get SERP of covid signals - tool

Description: query google with a list of covid signals and obtain a list of serps in csv (tsv in fact) file format
Name: serper.py

python serper.py

SERP files - datasets

Description Serp results for 4 datesets of queries Names: simple version of covid signals from google.ch in French: serp_signals_20_ch_fr.csv
simple version of covid signals from google.com in English: serp_signals_20_en.csv
amplified version of covid signals from google.ch in French: serp_signals_covid_20_ch_fr.csv
amplified version of covid signals from google.com in English: serp_signals_covid_20_en.csv

amplified version means that for each query we create two queries one with the keywords "covid" and one with "coronavirus"

Tool to aggregate SERP results - tool

Description: load csv serp data and aggregate the data to create a new csv file where each line is a website and each column is a query. Name: aggregate_serp.pl

`perl aggregate_serp.pl> aggregated_signals_20_en.csv

datasets of top website from the SERP results - dataset

Description a aggregated version of the SERP where each line is a website and each column a query
Names:
aggregated_signals_20_ch_fr.csv
aggregated_signals_20_en.csv
aggregated_signals_covid_20_ch_fr.csv
aggregated_signals_covid_20_en.csv

List of content:

- domain: domain name of the website
- signal 1: Position of the query 1 (signal 1) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- signal ...: Position of the query (signal) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- signal n: Position of the query n (signal n) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- total: average position (total of all position /divided by the number of queries)
- missing: Total number of missing results in the SERP for this website

datasets ranked top seo - dataset

Description a ranked (by weighted average position) version of the aggregated version of the SERP where each line is a website and each column a query. TOP 20 have more information about the type and HONcode validity (from the date of collect: September 2020)

Names:
ranked_signals_20_ch_fr.csv
ranked_signals_20_en.csv
ranked_signals_covid_20_ch_fr.csv
ranked_signals_covid_20_en.csv

List of content:

- domain: domain name of the website
- signal 1: Position of the query 1 (signal 1) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- signal ...: Position of the query (signal) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- signal n: Position of the query n (signal n) in the SERP where 30 indicates arbitrary that this website is not present in the SERP
- avg position: average position (total of all position /divided by the number of queries)
- nb missing: Total number of missing results in the SERP for this website
- % presence: % of presence
- weighted avg postion: combination of avg position and % of presence for final ranking
- honcode: status of the Honcode certificate for this website (none/valid/expired)
- type: type of the website (health, gov, edu or media)

Academic journals indicators developed from the information contained in the Scopus database (Elsevier B.V.). These indicators can be used to assess and analyze scientific domains.

The size of the Workforce Analytics Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 15.64% during the forecast period.Workforce analytics is the collection, analysis, and interpretation of data regarding an organization's workforce in order to make better decisions and optimize human capital. Advanced analytics techniques can be used by organizations to provide valuable insights into employee performance, engagement, productivity, and other key metrics.Workforce analytics helps the organization make fact-based decisions while acquiring, retaining, developing, and compensating talent. Then the patterns that could be applied to predict future workforce needs would help solve potential problems before they arise and optimize usage from historical data analysis. Workforce analytics further allows an organization to find potential talent, measure the ROI of training programs, and assess the effectiveness of the organizational change initiatives.Using the power of workforce analytics, organizations can make their workforce much more connected, productive, and effective in conducting businesses successfully. Recent developments include: September 2022: ActivTrak partnered with Google Workspace to provide personal work insights that enable employees to improve their digital work habits and wellness. Customers can embed individual work metrics into their Google Workspace applications with ActivTrak for Google Workspace, giving employees immediate visibility to help them redesign their workday, protect focus time, and improve well-being., August 2022: ADP has launched Intelligent Self-Service, which assists employees with common issues before they need to contact their HR department for assistance. Based on an analysis of data from across ADP's ecosystem, the product employs predictive analytics and machine learning to predict which issues may arise.. Key drivers for this market are: Increasing Need to Make a Smarter a Decision About the Talent, Increasing Data in HR Departments related to Pay rolls, Recruitment. Potential restraints include: Lack of Awareness About Workforce Analytics. Notable trends are: Performance Monitoring Offers Potential Growth.

Industry research found that the click-through rate of search ads worldwide stood at 1.63 percent in the second quarter of 2024. Click-through rate is the share of clicks an ad receives in the number of users that view it.

Background High-quality data are vital for informed decision-making, enhancing population health, and achieving comprehensive insights. However, there is limited understanding of the consistency and reliability of routine Health Management Information System (HMIS) including nutrition data across diverse regions in Ethiopia. This study systematically reviewed the existing literature to address these knowledge gaps. Methods We systematically searched PubMed, HINARI, and Google Scholar for studies published from 2015 onwards to assess HMIS, including nutrition data quality in Ethiopia. The evaluations focused on completeness, consistency, and timeliness metrics defined by the WHO. We included diverse regional studies without indicator restrictions, prioritized data quality metrics as primary outcomes, and explored qualitative reasons for poor data quality as secondary outcomes. Results Of the 1790 papers screened, 25 met the inclusion criteria. The completeness of reporting varied widely among studies (50%–100%), with only 21% (4 out of 19) exceeding 90%. The consistency ranged from 38.9% to 90.5%, with only 6% of studies reporting internal consistency above 90%. Other consistency issues included lack of external consistency, indicator discrepancies, and outliers. Timeliness ranged from 41.9% to 93.7%, with 54% of studies reporting below 80%. In addition to the lack of studies addressing nutrition data, the quality was no better than other components of HMIS. The major factors contributing to poor data quality were human resource shortages, insufficient capacity building, behavioural influences, and infrastructural deficits. Conclusion The HMIS including nutrition data in Ethiopia, exhibited deficiencies in completeness, consistency, and timeliness, which were largely, attributed to capacity and resource constraints. Interventions should prioritize resource allocation, staff training, supervision, and feedback mechanisms to enhance data quality, thereby improving decision-making processes and population health outcomes.