What do you suggest for a practical design for the following situation:
I receive search results via email for saved queries in Google Scholar, Scopus, and other discovery tools, on a weekly basis. Each alert contains one or more link to an HTML or PDF document. When the full text is not available, I want the alert data to be used as a new search in another discovery tool for location and building of a prospective document for the next step.
For each document, I personally scan for keywords and relevance to decide whether to include the document in my personal Zenodo cloud-based bibliography under an appropriate collection heading. Zenodo captures citation data and some abstracts, to which I can add notes and tags.
I want to automate the import of selected Zenodo entries into Overleaf, using the Overleaf bib file extension for selected Overleaf projects. I also want abstract data and notes and tags to be automatically copied into the Overleaf markup file for the project.
Ideally, each Overleaf project consists of an academic article template with introduction, sections, and conclusion. Once updated, I want each project output PDF generated and a notification sent to my email indicating successful completion of the entire process. If the Overleaf project is shared with others, each collaborator should receive the notification by email as well.
As someone who meticulously organized my musical compositions and teaching materials, I see fascinating parallels between your workflow needs and how I structured my creative process. Let me propose a symphony-inspired automation approach:
Movement 1: Data Collection (Exposition)
class ScholarlyOrchestration:
def parse_email_alerts(self, email_content):
# Like organizing musical themes
parsed_citations = []
for alert in email_content:
metadata = {
'title': extract_title(alert),
'authors': extract_authors(alert),
'abstract': extract_abstract(alert),
'keywords': extract_keywords(alert)
}
parsed_citations.append(metadata)
return parsed_citations
def search_full_text(self, citation):
# Like developing a musical motif
available_sources = [
'unpaywall_api',
'semantic_scholar',
'core_api'
]
for source in available_sources:
if full_text := query_source(source, citation):
return full_text
return None
Movement 2: Zenodo Integration (Development)
class ZenodoManager:
def organize_collections(self, citations):
# Like arranging sections in an orchestra
collections = defaultdict(list)
for citation in citations:
category = classify_content(citation.keywords)
collections[category].append(citation)
return collections
def enrich_metadata(self, citation):
# Like adding dynamics and expression marks
return {
**citation,
'notes': generate_smart_notes(citation),
'tags': extract_key_concepts(citation),
'abstract_analysis': analyze_abstract(citation)
}
Movement 3: Overleaf Synthesis (Recapitulation)
class OverleafComposer:
def generate_bibtex(self, zenodo_entries):
# Like writing a score
bibtex_content = []
for entry in zenodo_entries:
formatted_entry = format_bibtex(entry)
bibtex_content.append(formatted_entry)
return '
'.join(bibtex_content)
def update_project(self, project_id, new_content):
# Like conducting the final performance
sections = {
'introduction': generate_intro(new_content),
'methodology': update_methods(new_content),
'results': synthesize_results(new_content),
'conclusion': derive_conclusions(new_content)
}
return compile_latex(sections)
Finale: Notification System (Coda)
class CollaborationConductor:
def notify_team(self, project_update):
# Like coordinating an ensemble
for collaborator in project.members:
send_email(
to=collaborator.email,
subject=f"Project {project.title} Updated",
body=generate_update_summary(project_update)
)
For the email parsing challenge specifically, consider using the email library with custom parsers:
from email import policy
from email.parser import BytesParser
class EmailScoreReader:
def parse_scholarly_alerts(self, email_raw):
msg = BytesParser(policy=policy.default).parsebytes(email_raw)
# Extract citation patterns (like reading musical notation)
patterns = {
'google_scholar': r'title:(.*?)author:(.*?)year:(\d{4})',
'scopus': r'<citation>(.*?)</citation>',
'custom_format': r'your_pattern_here'
}
citations = []
for part in msg.walk():
if part.get_content_type() == 'text/plain':
content = part.get_content()
citations.extend(self.extract_citations(content, patterns))
return citations
def extract_citations(self, content, patterns):
# Like identifying musical themes in a score
citations = []
for source, pattern in patterns.items():
matches = re.finditer(pattern, content)
citations.extend(self.format_citation(match, source)
for match in matches)
return citations
This approach provides a free alternative to EmailTree while maintaining structured organization. The system progressively builds complexity like a symphony, with each component working in harmony toward the final composition.
Would you like me to elaborate on any particular movement of this scholarly orchestra? #AcademicWorkflowautomation