About the project
"Blessed be the fruit" is an open data
project regarding the analysis of factors that might influence pregnancy rates in young
women in Italy.
Specifically, we have decided to approach this topic by laying down a few fundamental features:
- Year span: 2017-2019, in order to gather the most frequent data not yet affected by the COVID-19 pandemic
- Gender: female, as source datasets do not go beyond the binary definition of gender, discarding queer identities
- Age range: 15-25
- Factors of interest: religious observance and education
The project, other than answering to the research question, also encompasses in-depth analyses of different aspects of open data regarding quality, legalty, ethics and technicalities.
Different visualizations are possible,
supporting the communication and ensuring a better understanding of the results.
download
Finally, a full detailed documentation is freely downloadable by the user.
RQ: Is there any observable pattern indicating a correlation between religious observance, pregnancy rates and education level in young women throughout Italy?
Find out the results!![](assets/img/pmgfront_new.png)
Datasets, between source ones and mashup ones, used to answer the research question
Pregnancies in the female population aged 15-25 during the year span 2017-2019 in Italy
Thousands religion followers in the year span 2017-2019 in Italy
Early leavers from higher education in the female population aged 18-24 during the year span 2017-2019 in Italy
Results
Click on a year to find out the specific results!
Results for year 2017
Religious observance rates
Highest rate: Puglia 32.9%
Lowest rate: Liguria 16.3%
Pregnancy rates
Highest rate: Sicilia 4.8%
Lowest rate: Basilicata 2.5%
Education leavers rates
Highest rate: Campania 9.1%
Lowest rate: Marche 2.6%
Bar chart
Results for year 2018
Religious observance rates
Highest rate: Calabria 15.7%
Lowest rate: Sardegna 2.8%
Pregnancy rates
Highest rate: Sicilia 2.4%
Lowest rate: Marche 0.4%
Education leavers rates
Highest rate: Sicilia 10.6%
Lowest rate: Marche / Friuli-Venezia Giulia 3.5%
Bar chart
Results for year 2019
Religious observance rates
Highest rate: Campania 15.8%
Lowest rate: Liguria 7.9%
Pregnancy rates
Highest rate: Sicilia 2.5%
Lowest rate: Basilicata 1.3%
Education leavers rates
Highest rate: Sicilia 10.3%
Lowest rate: Abruzzo 2.1%
Bar chart
Our main visualizations:
choropleth maps
We decided to provide choropleth maps as they were the best option to visualize individually the variation
of our data over our geographical area of interest,
as well as to visually highlight similarities in our variables trends.
Please note that:
- Religious observance percentage rates are calculated over the general population without distinction in gender or age class
- Pregnancy percentage rates are calculated over a sample population of women belonging to an age class of 15-24
- Education leavers percentage rates are caluculated over a sample population of women belonging to an age class of 18-24
bar chart
The bar chart visualization was included as well, since it offers us an immediate and clear comparison between values of each of our variables.
conclusions
For a further evaluation of our analysis, we can likely state that we don't have enough data to know if a variable - and in that case which one - causes the other.
For example, we could hypothesize that the early leavers rate is indeed affected by the early pregnancy rates, since being pregnant could be a reason for a young woman to choose to leave education or training. To affirm this, however, we would need to look into other possible correlated factors, such as poverty rates, to make sure they are not strongly affecting our data.
For the same reason, we can't possibly infer causality between religious rates and our other variables: even though they do seem to vary together, there's no mean for us to tell whether religiosity is the reason one is inclined to leave education and start a family early in life, or whether, after this happens, people are inclined to move their sociality from educational contests to religious contests. Just to give some examples of possible reasons of correlation.
Furthermore, to properly assess the connection between religiosity and pregnancies, with specific differentiation amongst induced abortions and live births, for instance, we would need much more data about how religiosity affects women's access to Voluntary Termination of Pregnancy (VTP, in Italian "Interruzione Volontaria di Gravidanza", IVG) in the first place. As we know, Italy has a very high rate of coscientious objectors (in 2012: 69,6% of gynecologists, 47,5% of anesthetists and 45% of non medical staff [source]).
This could significantly affect the pregnancy rates per region, as they have been counted including abortions in the place where they happen and not according to the woman's residency. It goes without saying, if a region prevents a woman to access VTP they might be likely to migrate to other region, hence, this phenomenon might also have affected our data. Unfortunately, there are very few and non-institutional data about the phenomenon and this prevented us to futherly investigate the degree of its impact on our study.
Finally, to highlight other features related to our variables and illustrate trends and other interesting information, you can have a look at our in the "Additional Visualizations" section. There, we provided:
- bubble charts for every year to investigate and better visualize how much our variables are correlated together,
- pie charts for pregnancies data, in order to better understand the rates of the different outcomes of pregnancies,
- time series for each reason, in order to better investigate how our values changed over the three year span we analyzed.
Source and mashup datasets
As mentioned, our project comprises the use of 16 different datasets, between source ones and mashup ones.
The 7 source datasets have been downloaded in .csv format from different databases belonging to Istat, the Italian National Institute of Statistics. Specifically, we have used three different databases:
- demo: demographics in figures: on resident population in the Italian municipalities and information on main demographic phenomena
- I.Stat: a datawarehouse organised by theme, presented in multidimensional tables and with a wide range of standard metadata
- IstatData: the new database into which all I.Stat content will be gradually migrated
During the download phase, we have manually filtered out everything that was not of interest for our research, keeping only the data strictly related to our research question (we have, for example, discarded any information related to marital status in the datasets regarding population).
Still, the source datasets went through an additional clean up phase in which we discarded duplicate (e.g., columns with different names and values but referring to the same information) and irrelevant data and, when necessary, added missing "coded data" to allow for an easier management of the datasets.
Finally, we proceeded with the mashup phase,
creating the final three main mashup datasets used to answer our
research question. As with source and clean datasets, we distinguished between the
three years of our time span of interest: in this way, we ended up with 9 final mashup datasets
(three for
each
factor of interest).
The code for the clean up and mashup phases can be found in the appropriate documentation, freely downloadable.
- All
- Source datasets
- Mashup datasets
D1 - Population 2017
ID: D1
Provenience: demo
Format: .csv, .xlsx, .pdf
Metadata: Not provided
URI: 2017Population
License: CC BY 3.0
D2 - Population 2018, 2019
ID: D2
Provenience: demo
Format: .csv, .xlsx, .pdf
Metadata: Not provided
URI: 201819Population
License: CC BY 3.0
D3 - Religious observance
ID: D3
Provenience: I.Stat
Format: .csv, .xlsx, .px, .xml
Metadata: Provided
URI: ReligiousObservance
License: CC BY 3.0
D4 - Live births
ID: D4
Provenience: IstatData
Format: .json, .xml, .xlsx, .csv
Metadata: Provided
URI: LiveBirths
License: CC BY 3.0
D5 - Spontaneous abortions
ID: D5
Provenience: I.Stat
Format: .csv, .xlsx, .px, .xml
Metadata: Provided
URI: SpontAbortions
License: CC BY 3.0
D6 - Induced abortions
ID: D6
Provenience: I.Stat
Format: .csv, .xlsx, .px, .xml
Metadata: Provided
URI: InducedAbortions
License: CC BY 3.0
D7 - Early leavers from education
ID: D7
Provenience: I.Stat
Format: .csv, .xlsx, .px, .xml
Metadata: Provided
URI: EarlyLeavers
License: CC BY 3.0
Analyses
The 7 source datasets have been analysed for four aspects.
Quality analysis
Following the National Guidelines for the Improvement of Public Information Assets and its four factors: accuracy, completeness, coherence and promptness.
Legal Analysis
To assess possible imbalances and risks regarding the long-term viability of the data generation and dissemination regarding the topics of: privacy, intellectual property, release license, limitation on public access, economical conditions, and other temporary aspects.
Ethical Analysis
Evaluating the ethical aspects of the datasets using the Data Ethics Principles and Guidelines: human being at the centre, transparency, accountability, and individual data protection.
Technical Analysis
On the metadata provided by Istat and other possible information on the datasets: format, provenance, IRI. Moreover, an RDF assertion and metadata for the mashup datasets and an analysis of the project from the perspective of the FAIR principles.
Quality analysis
Following the Italian National
Guidelines ("Linee guida nazionali per la valorizzazione del patrimonio informativo
pubblico"), developed in the context of the Data & Analytics
Framework project by AgID and the Digital Transformation Team, we have performed a quality analysis of our
source datasets to ensure their good
condition and their suitability for the intended use.
Specifically, there are four main factors to look for when analysing data quality:
- Accuracy (syntactic and semantic): the data and its attributes correctly represent the real value of the concept or event they refer to
- Coherence: the data and its attributes do not present any contradictions with respect to other data in the context of use by the administration owner
- Completeness: the data are exhaustive for what concerns every expected value and with respect to the related entities (sources) that contribute to the definition of the procedure
- Timeliness (or promptness of updating): the data and its attributes refer to the "correct time" (up to date) with respect to the procedure they refer to
ID Dataset | Accuracy | Coherence | Completeness | Timeliness |
---|---|---|---|---|
D1 - Population 2017 | ||||
D2 - Population 2018, 2019 | ||||
D3 - Religious observance | ||||
D4 - Live births | ||||
D5 - Spontaneous abortions | ||||
D6 - Induced abortions | ||||
D7 - Early leavers from education |
Legal analysis
The legal analysis of the source datasets is fundamental to obtain sustainability over time of the production process and of the publication of datasets and to guarantee a balanced service in compliance with the public function and with individual rights.
This analysis was carried out using a reference checklist consisting of a series of binary questions regarding the topics of: privacy issues, IPR policy, licenses, limitations on public access, economical conditions, and temporal aspects.
To check: | D1 | D2 | D3 | D4 | D5 | D6 | D7 |
---|---|---|---|---|---|---|---|
Is the dataset free of any personal data as defined in the Regulation (EU) 2016/679? | |||||||
Is the dataset free of any indirect personal data that could be used for identifying the natural person? | |||||||
Is the dataset free of any particular personal data (art. 9 GDPR)? | |||||||
Is the dataset free of any information that combined with common data available in the web, could identify the person? | |||||||
Is the dataset free of any information related to human rights (e.g., refugees, witness protection, etc.) | |||||||
Did you use a tool for calculating the range of the risk of deanonymization? | Not needed | Not needed | Not needed | Not needed | Not needed | Not needed | Not needed |
Are you using geolocalization capabilities? | |||||||
Did you check that the open data platform respect all the privacy regulations (registration of the end-user, profiling, cookies, analytics, etc.)? | |||||||
Do you know who, in your open data platform, is the Controller and Processor of the privacy data of the system? | |||||||
Have you checked the privacy regulation of the country where the dataset are physically stored? | |||||||
Do you have non-personal data? |
To check: | D1 | D2 | D3 | D4 | D5 | D6 | D7 |
---|---|---|---|---|---|---|---|
Have you created and generated the dataset? | |||||||
Are you the owner of the dataset? | |||||||
Are you sure not to use third party data without the proper authorization and license? | |||||||
Have you checked if there are any limitations in your national legal system for releasing some kind of datasets with open license? |
To check: | D1 | D2 | D3 | D4 | D5 | D6 | D7 |
---|---|---|---|---|---|---|---|
Did you release the dataset with an open data license? | |||||||
Did you include the clause: "In any case the dataset can't be used for re-identifying the person"? | |||||||
Did you release the API (in case you have it) with an open source license? | |||||||
Have you checked that the open data/API platform license regime is in compliance with your IPR policy? |
To check: | D1 | D2 | D3 | D4 | D5 | D6 | D7 |
---|---|---|---|---|---|---|---|
Did you check that the dataset concerns your institutional competences, scope and finality? | |||||||
Did you check the limitations for the publication stated by your national legislation or by the EU directives? | |||||||
Did you check if there are some limitations connected to the international relations, public security or national defence? | |||||||
Did you check if there are some limitations concerning the public interest? | |||||||
Did you check the international law limitations? | |||||||
Did you check the INSPIRE law limitations for the spatial data? |
To check: | D1 | D2 | D3 | D4 | D5 | D6 | D7 |
---|---|---|---|---|---|---|---|
Did you check that the dataset could be released for free? | |||||||
Did you check if there are some agreements with some other partners in order to release the dataset with a reasonable price? | Not needed | Not needed | Not needed | Not needed | Not needed | Not needed | Not needed |
Did you check if the open data platform terms of service include a clause of “non liability agreement” regarding the dataset and API provided? | |||||||
In case you decide to release the dataset to a reasonable price did you check if the limitation imposed by the new directive 2019/1024/EU are respected? | Not needed | Not needed | Not needed | Not needed | Not needed | Not needed | Not needed |
In case you decide to release the dataset to a reasonable price did you check the e-Commerce directive and regulation? | Not needed | Not needed | Not needed | Not needed | Not needed | Not needed | Not needed |
To check: | D1 | D2 | D3 | D4 | D5 | D6 | D7 |
---|---|---|---|---|---|---|---|
Do you have a temporary policy for updating the dataset? | |||||||
Do you have some mechanism for informing the end-user that the dataset is updated at a given time to avoid mis-usage and so potential risk of damage? | |||||||
Did you check if the dataset for some reason cannot be indexed by the research engines (e.g., Google, Yahoo, etc.)? | |||||||
In case of personal data, do you have a reasonable technical mechanism for collecting request of deletion (e.g., right to be forgotten)? | Not needed | Not needed | Not needed | Not needed | Not needed | Not needed | Not needed |
Publication license
Finally, a fundamental step of the legal analysis is the choice of the publication license for the
new mashup datasets.
Of course, this choice must consider the original licenses of the source datasets, which, in our case,
were all published under CC BY
3.0.
To better navigate the alternatives we made use of the Licensing Assistant
tool
provided by the European Commission, and we decided to publish all nine mashup datasets under a CC BY 4.0
license.
The table below showcases the original licenses of the original datasets and the final publication license of the mashup datasets:
ID | Dataset | Original licenses | Final license |
---|---|---|---|
MD1 | General religious observance in each region | CC BY 3.0, CC BY 3.0, CC BY 3.0 | CC BY 4.0 |
MD2 | Pregnancy rates in young women | CC BY 3.0, CC BY 3.0, CC BY 3.0 | CC BY 4.0 |
MD3 | Education leavers' rates in young women | CC BY 3.0, CC BY 3.0, CC BY 3.0 | CC BY 4.0 |
Ethical analysis
![](assets/img/ethics-icon.jpg)
For the ethical analysis of our project's data, we considered the Data Ethics Principles and
Guidelines and the Odi Project's detailed canvas for evaluating the ethical aspects of our data processing.
Since both our source and mashup datasets contain Information provided exclusively by the Italian National Institute of Statistics, we initially focused on analysing the fairness of data collection and management by ISTAT, and then established guidelines for addressing ethical concerns when processing the source datasets for our project.
Data Ethics Principles
- Human being at the center As stated on the institution's website, ISTAT's policy is aligned with Ethical and legislative principles, with the primary aim to publish and communicate effectively statistical information and results of analyses conducted in order to foster awareness of Italy's conditions and to improve decision-making processes on the part of private subjects and public institutions. Furthermore, ISTAT is committed to conducting methodological, applied research with the aim of improving statistical production processes and enhancing Italy's statistical literacy.
- Equality In line with what is mentioned above, ISTAT uses statistical methodologies to publish data on important equality issues in various domains, such as diversity management in enterprises in Italy, gender discrimination, labour discrimination and poverty, and many more, that can be browsed in their dedicated archive. We do note however, that there is room for improvement especially regarding the representation of ethnic and gender and sexuality minority groups in their data.
- Transparency The data gathered is transparently managed, with ample documentation made available to final users. This documentation describes the data collection methods, and the significance of specific vocabulary in the dataset, as well as related licenses and policies to avoid misinterpretations.
- Accountability Istat quality policy is coherent with the European framework developed by Eurostat, adhering to the principles of the European Statistics Code of Practice, that ensures and strengthens both accountability and governance of the European Statistical System and the National Statistical Systems inside it.
-
Individual data protection
Istat's datasets are anonymised, and as stated in their their
regulations
and privacy page, the institute respects the privacy of respondents, protects the
confidentiality of the data gathered,
and
carries
out its activities in a transparent and independent manner.
To be more specific, the information collected is protected by statistical confidentiality (Article 9 of Legislative Decree No. 322/1989) and subject to the legislation on the protection of personal data (Regulation (EU) 2016/679, Legislative Decree n. 196/2003, Legislative Decree n. 101/2018).
Ethical concerns and their management
Despite ISTAT's compliance with the ethics principles of data collection and management, the team placed special importance
to the ethical handling of the source information given the great sensitivity its contents.Data related to age, residence, religious observance and reproductive health are indeed sensible and the ethical aspect of their handling was carefully considered through the following steps:
- Data integrity and privacy are furtherly respected through the process of aggregating source dataset values and providing them in percentage values, in order to avoid any correlation with real individuals.
- To ensure equality and protection of more vulnerable groups, clear boundaries were set by intentionally omitting available data by ISTAT, such as the citizenship distinction in all of our data, which could lead to possible discriminatory behavior.
- The team's purpose was to discover the existence of possible patterns and not make any inferences or interpretations. Throughout our results and conclusion documentation, we also stress that any observed patterns in our data do not call for generalisations regarding the parameters studied, given the inconsistencies in our data and the absence of other, possibly relevant socioeconomic factors.
- All relevant documentation regarding the data processing for creating the mashup datasets and visualisations is provided in our github repository.
Technical analysis
Source datasets
All source dataset have been evaluated based on the metadata model provided by AGID that classifies metadata quality on a range of 4 levels according to two factors: data-metadata bond and detail level
N.B. Additional information and reconstructed metadata about source datasets can be found in the metadata analysis table below or in our general documentation.
ID | Provenience | Format | Metadata | URI | License |
---|---|---|---|---|---|
D1 | demo | .csv, .xlsx, .pdf | Level 2: A weak data-metadata bond since an external pdf with additional information and methodology reports is accessible; Dataset detail level, information are shared by all dataset data | 2017Population | CC BY 3.0 |
D2 | demo | .csv, .xlsx, .pdf | Level 1: Not provided | 201819Population | CC BY 3.0 |
D3 | I.Stat | .csv, .xlsx, .px, .xml | Level 4: An SDMX structured file is downloadable with a strong data-metadata bond and a
datum-level detail of description. They are machine readable. Level 2: Additional metadata to provide transparent information about sources and methodologies are available in a separated >webpage, accessible through a sidebar menu |
ReligiousObservance | CC BY 3.0 |
D4 | IstatData | .csv, .xlsx, .json, .xml | Level 4: An SDMX structured file is downloadable with a strong data-metadata bond and a datum-level detail of description. They are machine readable. | LiveBirths | CC BY 3.0 |
D5 | I.Stat | .csv, .xlsx, .px, .xml | Level 4: An SDMX structured file is downloadable with a strong data-metadata bond and a
datum-level detail of description. They are machine readable. Level 2: Additional metadata to provide transparent information about sources and methodologies are available in a separated >webpage, accessible through a sidebar menu |
SpontAbortions | CC BY 3.0 |
D6 | I.Stat | .csv, .xlsx, .px, .xml | Level 4: An SDMX structured file is downloadable with a strong data-metadata bond and a
datum-level detail of description. They are machine readable. Level 2: Additional metadata to provide transparent information about sources and methodologies are available in a separated webpage, accessible through a sidebar menu |
InducedAbortions | CC BY 3.0 |
D7 | I.Stat | .csv, .xlsx, .px, .xml | Level 4: An SDMX structured file is downloadable with a strong data-metadata bond and a
datum-level detail of description. They are machine readable. Level 2: Additional metadata to provide transparent information about sources and methodologies are available in a separated >webpage, accessible through a sidebar menu |
EarlyLeavers | CC BY 3.0 |
RDF Assertion and Metadata for the mashup datasets
All produced mashup dataset have been thoroughly described with metadata, following the specification
of DCAT-AP_IT standard as recommended by AGID's public information heritage valorization
guidelines.
Since all our datasets contain data of specific national interest and are derived from Istat datasets,
which is an italian public research institution we decided to adopt DCAT-AP_IT (2016), the national
standard.
Although it is based on the first version of the european standard DCAT and has more
constraints, compared to the more flexible and recent european standard DCAT-AP 2.0, we considered
DCAT-AP_IT the more suitable standard for our mashuo datasets: on the one hand, because in the italian
public sector an increasing number of Public
Administrations are adopting DCAT-AP_IT; on the other hand,
because this allowed us to follow more detailed national guidelines and therfore ensure interoperability
and harmonization with other data on a national level.
Moreover:
- To describe in full transparency the sources and the activities underlying the creation of our mashup datasets we adopted PROV-O - the provenance ontology as strongly recommended on a european level and also allowed by DCAT-AP_IT metadata model
- Since all our mashup datasets are series containing individual datasets for each year (2017, 2018,
2019) and only DCAT-AP 3.0 currently provides a
dcat:DatasetSeries
with related properties, again, we followed AGID's metadata model instruction about how to handle relationships between datasets.
We emphasized individual elements of the serie and created, inside each mashup dataset RDF assertion, a triple with a Serie Dataset subject, connected through the Dublin Core propertydct:type
to the value <http://inspire.ec.europa.eu/metadata-codelist/ResourceType/series> .
Then we specified which dataset belonged to the Serie by means ofdct:hasPart
property.
Finally, every individual yearly Mashup dataset, it's connected in its turn with the related Serie by means ofdct:isPartOf
Additional Metadata for source datasets
In providing the metadata for the source datasets, information have been retreived from original sources. When missing, they have been induced and integrated according to the same guidelines adopted for mashup datasets (ex. assignining a theme to source dataset has been done according to the same european authority).
It should be mentioned that, in case of D1 and D2, our source datasets were downloaded from demo database Endpoint where very few and non structured metadata were available. Nonetheless, the same datasets are also published in the IstatDati database Endpoint where the Standard protocol Statistical Data and Metadata eXchange (SDMX) is adopted. For more information about Istat Data and SDMX metadata incorporation carried out by Istat see SDMX web service for Istat data.
FAIR principles
During the development of the project, we strived to adhere to the FAIR principles, as published
by the GO FAIR Initiative.
These recommendations are a set of guiding
principles proposed by a consortium of scientists and organizations to support the
Findability, Accessibility, Interoperability, Reusability of digital assets,
emphasizing, in particular, machine-actionability.
In the following table we have employed the overview of FAIR Principles provided by GO Fair as a checklist to thoroughly examine our project.
To check: | |
---|---|
(Meta)data are assigned a globally unique and persistent identifier | |
Data are described with rich metadata (defined by R1 below) | |
Metadata clearly and explicitly include the identifier of the data they describe | |
(Meta)data are registered or indexed in a searchable resource |
To check: | |
---|---|
(Meta)data are retrievable by their identifier using a standardised communications protocol | |
The communication protocol is open, free, and universally implementable | |
The communication protocol allows for an authentication and authorisation procedure, where necessary | |
Metadata are accessible, even when the data are no longer available |
To check: | |
---|---|
(Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. | |
(Meta)data use vocabularies that follow FAIR principles | |
(Meta)data include qualified references to other (meta)data |
To check: | |
---|---|
(Meta)data are richly described with a plurality of accurate and relevant attributes | |
(Meta)data are released with a clear and accessible data usage license | |
(Meta)data are associated with detailed provenance | |
(Meta)data meet domain-relevant community standards |
Additional visualizations
Different graphic representations of the data allow for more in-depth research on the topic of our project, as well as permitting the analysis of possible correlations between the factors of interest (through bubble charts), different outcomes of the pregnancies (through pie charts), and chronological trends (through time series) in each region.
The visualizations have been developed using a variety of libraries, namely: leaflet (for the maps), plotly.js (for the bar charts, time series, and bubble charts), and amCharts (for the pie charts).
The data has been processed further to build the different visualizations.
Results for year 2017
Results for year 2018
Results for year 2019
TRENDS OVER TIME
Finally, we can observe how our chosen parameters have evolved over the timespan of our study in each specific region.
Overall, our research has provided many insightful results. Regarding the
relationship
between religious observance with pregnancies and early leavers of education, the data highlights the
presence
of relations in some regions, but is in some cases too inconsistent for establishing fair patterns.
The strongest correlation can be observed between a region's pregnancies and early
leavers of education in the female population, which are parameters that are often linked but should be
subject
to further research.
As stated previously, we must consider that even in instances of higher correlations between our observed parameters, we should be careful in our assumptions for two reasons:
- Firstly, given the absence of appropriate data and the limitations of this project's research, some significant socioeconomic parameters, which could provide more context regarding the situation of italian regions, as well individual cases, were not taken into account.
- Secondly, the presence of outliers in many of our data discourages us to make generalizations, but would be definitely interesting for further research.
Sustainability of the project
The source datasets developed for "Blessed be the fruit" are provided exclusively by the Italian National Institute of Statistics (Istat), which maintains them in its various databases. Given the current situation of I.Stat, which content will be soon moved to IstatData, the URIs provided in this project for the source datasets D3, D5, D6, and D7 will eventually become obsolete .
However, "Blessed be the fruit" is the final project developed for the "Open Access and Digital Ethics" course (a.a. 2022/2023) within the Digital Humanities and Digital Knowledge Masters Degree (University of Bologna), and, as such is not actively maintained and will not be updated in the future.
Team & Statement of responsibility
![](assets/img/team/madda_1.jpg)
Maddalena Ghiotto
(holding herself)Project ideation — Data retrieval — Mashup datasets — Technical analysis — RDF assertion of the metadata
![](assets/img/team/chloe.jpeg)
Chloe Papadopoulou
(holding a coffee machine)Project ideation — Data retrieval — Ethical analysis — Visualizations
![](assets/img/team/orsola.jpg)
Orsola Maria Borrini
(holding tzatziki)Project ideation — Data retrieval — Mashup datasets — Quality and legal analyses — Sustainability of the update — Website development
Licenses and credits
Images and icons
Pomegranate icons created by Freepik - Flaticon
"Bead with pomegranate" image, from the MET museum and available for unrestricted commercial and noncommercial use without permission or fee (CC0)
Pomegranate images in "Hero" and "Clients" sections by Image by rawpixel.com on Freepik
Web template
This website is built on the HTML5 template "Vesperr" by BootstrapMade and released under MIT
Source Datasets
Creative Commons Attribution 3.0 Unported (CC BY 3.0)
Mashup Datasets
Creative Commons Attribution 4.0 International (CC BY 4.0)
Softwares used
Leaflet: Copyright (c) 2010-2023, Volodymyr Agafonkin Copyright (c) 2010-2011, CloudMade - All rights reserved. (BSD 2-Clause "Simplified" License)
Plotly.js: Copyright (c) 2021 Plotly, Inc - All rights reserved ( MIT License)
amCharts: linkware license