Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
High throughput experimentation is a growing and evolving field that allows to execute dozens to several thousands of experiments per day with relatively limited resources. Through miniaturization, typically a high degree of automation and the use of digital data tools, many parallel reactions or experiments at a time can be run in such workflows. High throughput experimentation also requires fast analytical techniques capable of generating critically important analytical data in line with the increased rate of experimentation. As traditional techniques usually do not deliver the speed required, some unique approaches are required to enable workflows to function as designed. This review covers the recent developments (2019-2020) in this field and was intended to give a comprehensive overview of the current "state-of-the-art."
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10989611 | PMC |
http://dx.doi.org/10.1002/ansa.202000155 | DOI Listing |
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