diff --git a/.gitignore b/.gitignore
index 88403f840ff21a129eb31957247cfffc30321506..f42874b5f16cf6450bd707eae94de921465b2c3a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -94,4 +94,7 @@ ENV/
# Intellig
.idea/
# DB-Connection
-inventory/config.py
\ No newline at end of file
+inventory/config.py
+
+# sqllite db
+sample_db.sqlite
\ No newline at end of file
diff --git a/app.py b/app.py
index 82ec4cc13896483b4f854439b72ac9b418a7109c..0f96f7728a58edb9f3bf830c99c44d10bcdea65f 100644
--- a/app.py
+++ b/app.py
@@ -182,7 +182,7 @@ def index():
softwares = Software.query.all()
for software_tool in softwares:
template.stream(software=software_tool).dump(
- '../digitale-Methoden-wiki/Tool_' + software_tool.name.replace(' ', '').replace('/', '') + '.asciidoc', encoding='utf-8')
+ '../digitale_Methoden.wiki/Tool_' + software_tool.name.replace(' ', '').replace('/', '') + '.asciidoc', encoding='utf-8')
softwareincategory = []
software_categorys = SoftwareCategory.query.all()
@@ -193,7 +193,7 @@ def index():
# Generate tools overview page
with open('templates/export/softwares.jinja2', "r", encoding="utf-8") as file_:
template = Template(file_.read())
- template.stream(softwareincategory=softwareincategory).dump('../digitale-Methoden-wiki/SoftwareToolsList.asciidoc', encoding='utf-8')
+ template.stream(softwareincategory=softwareincategory).dump('../digitale_Methoden.wiki/SoftwareToolsList.asciidoc', encoding='utf-8')
# Generate methods overview page
hierarchy = db.session.query(Method, literal(0).label('level')).filter(Method.parent_id == null()) \
@@ -216,7 +216,7 @@ def index():
# Generate sub pages
with open('templates/export/MethodsList.jinja2', "r", encoding="utf-8") as file_:
template = Template(file_.read())
- template.stream(methods=result, references=references).dump('../digitale-Methoden-wiki/MethodsList.asciidoc',
+ template.stream(methods=result, references=references).dump('../digitale_Methoden.wiki/MethodsList.asciidoc',
encoding='utf-8')
base_path = pathlib.Path('../digitale-Methoden-wiki/')
@@ -288,12 +288,12 @@ def build_sample_db():
method = Method(id=1,
name="digital methods",
description="""<<Rogers2013>> distinguishes between digitalized/virtual and digital methods. The former methods import standard methods from the social sciences and humanities into the emerging medium. The latter are completly new methods which emerge following the new structures and their properties. +
-In this project a more inclusive conception of digital methods is assumed: the potential use of digital technology during the research.""",
+In this project a more inclusive conception of digital methods is assumed: the use of digital technology or technique during the research.""",
parent=None)
db.session.add(method)
method1 = Method(id=2,
name="data mining",
- description="""Refers to the complete process of 'knowledge mining from data'.<<Han_etal2012>> Can be applied on various data types and consists of different steps and paradigms.""",
+ description="""Refers to the complete process of 'knowledge mining from data'.<<Han_etal2012>> Can be applied on various data types and consists of different steps and paradigms. For an application in the context of text mining in the social science see the concept "blended-reading" (<<Stulpe_etal2016>>).""",
parent=method)
db.session.add(method1)
@@ -451,7 +451,7 @@ Furthermore the server-client-model is the established communication paradigms f
description="'The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.'(link:https://cran.r-project.org/web/packages/lsa/lsa.pdf[CRAN-R])",
parent=method3)
db.session.add(method4)
- method4 = Method(id=36,name="topic modelling",
+ method4 = Method(id=36,name="topic modeling",
description="Probabilistic models to infer semantic clusters. See especially <<Papilloud_etal2018>>.",
parent=method3)
db.session.add(method4)
@@ -464,7 +464,7 @@ The aim of the LDA algorithm is to model a comprehensive representation of the c
description="Inclusion of non-negative constraint.",
parent=method4)
db.session.add(method5)
- method5 = Method(id=39,name="structural topic modelling",
+ method5 = Method(id=39,name="structural topic modeling",
description="Inclusion of meta-data. Refer especially to <<roberts2013>>.",
parent=method4)
db.session.add(method5)
@@ -476,7 +476,7 @@ The aim of the LDA algorithm is to model a comprehensive representation of the c
description="For automated narrative methapor analysis see (<<Ignatow_etal2017>>, 89-106. For argumentative structures(Task: Retrieving sentential arguments for any given controversial topic) <<Stab_etal2018>> .Refer for a current overview <<Cabrio2018>>.",
parent=method3)
db.session.add(method4)
- method3 = Method(id=42,name="network analysis/modelling",
+ method3 = Method(id=42,name="network analysis/modeling",
description="Generate networks out of text/relationships between text.",
parent=method2)
db.session.add(method3)
@@ -504,8 +504,8 @@ The aim of the LDA algorithm is to model a comprehensive representation of the c
description="Visualizations with user interaction or animations.",
parent=method2)
db.session.add(method3)
- method1 = Method(id=49,name="science practice",
- description="General science practice",
+ method1 = Method(id=49,name="research practice",
+ description="",
parent=method)
db.session.add(method1)
method2 = Method(id=50,name="digital research design",
@@ -517,19 +517,19 @@ The aim of the LDA algorithm is to model a comprehensive representation of the c
parent=method2)
db.session.add(method3)
method3 = Method(id=52,name="wiki surveys",
- description="Guide open-answer questions with user feedback.",
+ description="Guide open-answer questions with user feedback. Refer also (<<Salganik2018>>,111)",
parent=method2)
db.session.add(method3)
method3 = Method(id=53,name="survey data linked to big data sources",
description="",
parent=method2)
db.session.add(method3)
- method4 = Method(id=54,name="Enriched asking",
- description="",
+ method4 = Method(id=54,name="enriched asking",
+ description="'In enriched asking, survey data build context around a big data source that contains some important measurements but lacks others.'(<<Salganik2018>>,118)",
parent=method3)
db.session.add(method4)
- method4 = Method(id=55,name="Amplified asking",
- description="",
+ method4 = Method(id=55,name="amplified asking",
+ description="'Amplified asking using a predictive model to combine survey data from few people with a big data source from many people.'(<<Salganik2018>>,122)",
parent=method3)
db.session.add(method4)
method2 = Method(id=56,name="collaborative work",
@@ -548,27 +548,31 @@ The aim of the LDA algorithm is to model a comprehensive representation of the c
description="",
parent=method1)
db.session.add(method2)
- method1 = Method(id=60,name="statistical modeling",
+ method2 = Method(id=60,name="digital data/phenomena as reasearch-objective",
+ description="",
+ parent=method1)
+ db.session.add(method2)
+ method1 = Method(id=61,name="statistical modeling",
description="",
parent=method)
db.session.add(method1)
- method2 = Method(id=61,name="regression analysis",
+ method2 = Method(id=62,name="regression analysis",
description="",
parent=method1)
db.session.add(method2)
- method2 = Method(id=62,name="time-series analysis",
+ method2 = Method(id=63,name="time-series analysis",
description="",
parent=method1)
db.session.add(method2)
- method2 = Method(id=63,name="agent-based modeling",
+ method2 = Method(id=64,name="agent-based modeling",
description="",
parent=method1)
db.session.add(method2)
- method1 = Method(id=64,name="social complexity modeling/ social simulation",
+ method1 = Method(id=65,name="social complexity modeling/ social simulation",
description="",
parent=method)
db.session.add(method1)
- method2 = Method(id=65,name="nowcasting",
+ method2 = Method(id=66,name="nowcasting",
description="Using methods to predict the future for estimation of current values. (Example: predict influenza epidemiology combining CDC Data and Google Trends(<<Salganik2018>>,46–50)).",
parent=method1)
db.session.add(method2)
@@ -609,6 +613,12 @@ The aim of the LDA algorithm is to model a comprehensive representation of the c
reference = Reference(name="Niekler_etal2018",
cited="Niekler, A., Bleier, A., Kahmann, C., Posch, L., Wiedemann, G., Erdogan, K., . . . Strohmaier, M. (2018). ILCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). European Language Resource Association. Retrieved from http://aclweb.org/anthology/L18-1209")
db.session.add(reference)
+ reference = Reference(name="Lemke_etal2016",
+ cited="Lemke, M., & Wiedemann, G. (Eds.). (2016). Text Mining in den Sozialwissenschaften: Grundlagen und Anwendungen zwischen qualitativer und quantitativer Diskursanalyse. Wiesbaden: Springer VS.")
+ db.session.add(reference)
+ reference = Reference(name="Stulpe_etal2016",
+ cited="Stulpe, A., & Lemke, M. (2016). Blended Reading. In Text Mining in den Sozialwissenschaften (pp. 17–61). Springer.")
+ db.session.add(reference)
lic_unknown = License(name="Unknown")
lic_bsd = License(name="BSD")
diff --git a/sample_db.sqlite b/sample_db.sqlite
deleted file mode 100644
index 8cd791705c5b26174d75e40e411ddc71e54c423e..0000000000000000000000000000000000000000
Binary files a/sample_db.sqlite and /dev/null differ