deliverable d1.4 data management planforensor-project.eu/wp-content/uploads/2015/11/...d1.4 data...

This project has received funding from Horizon 2020, European Union’s

Framework Programme for Research and Innovation, under grant agreement

No. 653355

Deliverable D1.4 Data Management Plan

Work Package 1 Project Management

FORENSOR Project

Grant Agreement No. 653355

Call H2020-FCT-2014-2015 “Fight against Crime and terrorism”

Topic FCT-05-2014 “Law enforcement capabilities topic 1: Develop novel monitoring systems and

miniaturised sensors that improve Law Enforcement Agencies' evidence- gathering abilities”

Start date of the project: 1 September 2015

Duration of the project: 36 months

D1.4 Data Management Plan

FORENSOR Project of 26

Disclaimer This document contains material, which is the copyright of certain FORENSOR contractors, and

may not be reproduced or copied without permission. All FORENSOR consortium partners have

agreed to the full publication of this document. The commercial use of any information contained

in this document may require a license from the proprietor of that information. The reproduction

of this document or of parts of it requires an agreement with the proprietor of that information.

The document must be referenced if used in a publication.

The FORENSOR consortium consists of the following partners.

No. Name Short Name

Coun-try

1 ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS CERTH GR

2 JCP-CONNECT SAS JCP-C FR

3 STMICROELECTRONICS SRL STM IT

4 FONDAZIONE BRUNO KESSLER FBK IT

5 EMZA VISUAL SENSE LTD EMZA IL

6 SYNELIXIS LYSEIS PLIROFORIKIS AUTOMATISMOU & TILEPIKOI-NONION MONOPROSOPI EPE

SYNELIXIS GR

7 VRIJE UNIVERSITEIT BRUSSEL VUB BE

8 ALMAVIVA - THE ITALIAN INNOVATION COMPANY SPA ALMAVIVA IT

9 VISIONWARE-SISTEMAS DE INFORMACAO SA VISION-WARE

PT

10 VALENCIA LOCAL POLICE PLV ES

11 POLÍCIA JUDICIÁRIA (MINISTÉRIO DA JUSTIÇA) MJ PT



Document Information

Project short name and number FORENSOR (653355)

Work package WP1

Number D1.4

Title Data Management Plan

Responsible beneficiary CERTH

Involved beneficiaries JCP-C

Type1 R

Dissemination level2 PU

Contractual date of delivery 30/11/2015

Last update 30/11/2015

1 Types. R: Document, report (excluding the periodic and final reports); DEM: Demonstrator, pilot, proto-type, plan designs; DEC: Websites, patents filing, press & media actions, videos, etc.; OTHER: Software, technical diagram, etc. 2 Dissemination levels. PU: Public, fully open, e.g. web; CO: Confidential, restricted under conditions set out in Model Grant Agreement; CI: Classified, information as referred to in Commission Decision 2001/844/EC.



Document History

Ver-sion

Date Sta-tus

Authors, Reviewers

Description

v 0.01

05/10/2015 Tem-plate

CERTH Project deliverable template.

v 0.02

11/11/2015 Draft CERTH Adjustment of the project deliverable template.

v 0.03

12/11/2015 Draft CERTH Document structure, ToC, sections content in bul-leted form with brief explanation, writing alloca-tion. Note: This version was submitted to the WPL, PTM, PSC, and PMC for internal review.

v 0.04

17/11/2015 Draft WPL, PTM, PSC, PMC,

CERTH

Feedback to v0.03 of the document by WPL, PTM, PSC, and PMC. Provision of a full description of a dataset as template for partner input. Introduction content in bulleted form.

v 0.05

20/11/2015 Final draft

VUB, EMZA, ALMA-VIVA,

CERTH

Inputs and feedback to v0.04 of the document by VUB (Section 2), EMZA (Section 3), and ALMAVIVA (Section 4). Final development of all sections in-cluding Introduction and Executive Summary. Al-phabetical ordering of acronyms/ abbreviations. Note: This version was submitted to the QM, PSC, and PMC. The QM forwarded this version to VIS and STM for internal review.

v 1.00

30/11/2015 Final VIS, STM, VUB,

CERTH

Internal review feedback to v0.05 of the document by VIS, STM, VUB (Sections 2.4, 2.5, 3.4, 3.5, 4.4, 4.5, 5.4, 5.5), and CERTH. Final document for sub-mission.



Acronyms and Abbreviations

Acronym/Abbreviation Description

CC0 Creative Commons “No Rights Reserved” license

DaPPECL Data Protection, Privacy, Ethical and Criminal Law

DMP Data Management Plan

DOI Digital Object Identifier

FORENSOR FOREnsic evidence gathering autonomous seNSOR

FT Field Test

GA Grant Agreement

HTTP Hypertext Transfer Protocol

HW Hardware

IPR Intellectual Property Rights

LEA Law Enforcement Authority

OAI-PMH Open Archives Initiative Protocol for Metadata Harvesting

PMC Project Management Committee

PSC Project Steering Committee

PTM Project Technical Manager

QVGA Quarter Video Graphics Array

SW Software

ToC Table of Contents

TX.Y Task X.Y

UC Use Case

VGA Video Graphics Array

WPL Work Package Leader

WPX Work Package X



Contents

1 Introduction ........................................................................................................................... 10

2 Project Datasets..................................................................................................................... 11

2.1 DaPPECL Impact Assessment Survey Data (FORENSOR DaPPECL Impact Assessment

Data) 11

2.1.1 Dataset reference and name ................................................................................. 11

2.1.2 Dataset description ............................................................................................... 11

2.2 Staged Surveillance Videos and FORENSOR Emulated Staged Surveillance Videos

(FORENSOR Development and Testing Benchmark) ................................................................. 12



2.3 Real Life Surveillance Videos (FORENSOR Pilots/ Field Tests Content) ......................... 16



2.4 Automatically Obtained Image and Video Forensic Evidence (FORENSOR Pilots/ Field

Tests Testbed Data) ................................................................................................................... 18



3 Standards and Metadata ....................................................................................................... 20

3.1 General note on dataset structure ................................................................................ 20

3.2 FORENSOR DaPPECL IA SD Dataset ............................................................................... 20

3.3 Other datasets ............................................................................................................... 21

4 Data Sharing .......................................................................................................................... 22


4.2 Other datasets ............................................................................................................... 22

4.2.1 Access and licensing .............................................................................................. 23

4.2.2 Re-use .................................................................................................................... 23

4.2.3 Reasons for not sharing ......................................................................................... 23

5 Archiving and Preservation (Including Storage and Backup) ................................................ 25


5.2 Other datasets ............................................................................................................... 25

6 Appendix I: Relevant ZENODO Archiving and Preservation Policies ..................................... 26



6.1 Retention period ........................................................................................................... 26

6.2 Functional preservation................................................................................................. 26

6.3 File preservation ............................................................................................................ 26

6.4 Fixity and authenticity ................................................................................................... 26

6.5 Succession plans ............................................................................................................ 26

6.6 What does it cost? ......................................................................................................... 26



List of Tables

Table 1: Overview of the research data of the FORENSOR project ............................................... 10

Table 2: DaPPECL Impact Assessment Survey Data (FORENSOR DaPPECL Impact Assessment

Data) .............................................................................................................................................. 11

Table 3: FORENSOR DaPPECL IA SD Dataset description .............................................................. 12

Table 4: Staged Surveillance Videos and FORENSOR Emulated Staged Surveillance Videos

(FORENSOR Development and Testing Benchmark) ..................................................................... 12

Table 5: FORENSOR DTB and FORENSOR DTB-E Datasets description .......................................... 15

Table 6: Real Life Surveillance Videos (FORENSOR Pilots/ Field Tests Content) ........................... 16

Table 7: FORENSOR PFT Dataset description ................................................................................ 17

Table 8: Automatically Obtained Image and Video Forensic Evidence (FORENSOR Pilots/ Field

Tests Testbed Data) ....................................................................................................................... 18

Table 9: FORENSOR Evidence Dataset description........................................................................ 18



Executive Summary

A further new element in Horizon 2020 is the use of Data Management Plans (DMPs) detailing

what data the project will generate, whether and how it will be exploited or made accessible for

verification and re-use, and how it will be curated and preserved.

The present deliverable constitutes the DMP of the FORENSOR project, i.e. a short, general outline

of the project policy for data management. The described policy herein reflects the current state

of consortium agreements regarding data management and is consistent with those referring to

exploitation and protection of results.

This deliverable can be also considered as a checklist for the future. It is a living document that is

expected to mature during the project lifetime and will be updated accordingly. The overall pur-

pose of the DMP is to support the data management life cycle for all data that will be collected,

processed or generated by the project.

Five different datasets have been identified, at this early stage of the project. In sections 2-5 we

provide a detailed presentation of all the data that are expected to be collected, processed or

generated by the FORENSOR project along with respective data management policies.

The first project dataset (FORENSOR DaPPECL IA SD Dataset) will consist of all filled-in question-

naires that will be collected within the context of the project Data Protection, Privacy, Ethical and

Criminal Law (DaPPECL) Impact Assessment.

The second (FORENSOR DTB Dataset) and third (FORENSOR DTB-E Dataset) project datasets will

be the Development and Testing Benchmark of the FORENSOR project.

The fourth project dataset (FORENSOR PFT Dataset) will consist of all real life surveillance videos

that will be captured within the context of the project pilots and field tests.

Finally, the fifth project dataset (FORENSOR Evidence Dataset) will consist of all parts of the FO-

RENSOR PFT Dataset that will be automatically identified by the FORENSOR system as forensic

evidence.

The five datasets mentioned above are specified in this document and details are given regarding

their standards, metadata, sharing, archiving and preservation.



1 Introduction The present deliverable constitutes the Data Management Plan (DMP) of the FORENSOR project,

i.e. a short, general outline of the project policy for data management, including the following

issues:

What types of data will the project collect/generate?

What standards will be used?

How will this data be exploited and/or shared/made accessible for verification and re-

use? Reasons why the data cannot be made available in some cases.

How will this data be curated and preserved?

In other words the DMP is a document outlining how research data will be handled during the

FORENSOR project, and after it is completed.

The overall purpose of the DMP is to support the data management life cycle

for all data that will be collected, processed or generated by the project.

The described policy herein reflects the current state of consortium agreements regarding data

management and is consistent with those referring to exploitation and protection of results.

This deliverable can also be considered as a checklist for the future. It is a living document that is

expected to mature during the project lifetime and will be updated accordingly.

Five different datasets have been identified, at this early stage of the project. Table 1 gives an

overview of all the data that are expected to be collected, processed or generated by the FOREN-

SOR project.

Table 1: Overview of the research data of the FORENSOR project

Dataset ID Dataset Name

1. FORENSOR DaPPECL IA SD Da-taset

DaPPECL Impact Assessment Survey Data

2. FORENSOR DTB Dataset Staged Surveillance Videos

3. FORENSOR DTB-E Dataset FORENSOR Emulated Staged Surveillance Videos

4. FORENSOR PFT Dataset Real life surveillance videos

5. FORENSOR Evidence Dataset Automatically Obtained Image and Video Forensic Evi-dence

The five datasets listed in Table 1 are described in Section 2 of this deliverable while extensive

details regarding standards, metadata, sharing, archiving and preservation of the datasets are

provided in Sections 3-6.



2 Project Datasets

2.1 DaPPECL Impact Assessment Survey Data (FORENSOR DaPPECL Impact As-

sessment Data)

2.1.1 Dataset reference and name Dataset and Subset IDs, names and references are shown in Table 2.

Table 2: DaPPECL Impact Assessment Survey Data (FORENSOR DaPPECL Impact Assessment Data)

Dataset ID Dataset Name Dataset Reference

FORENSOR DaPPECL IA SD Dataset

DaPPECL Impact Assessment Sur-vey Data

Link will be provided at a later stage of the FORENSOR project.

1st Level Subset ID 1st Level Subset Name 1st Level Subset Reference

FORENSOR DaPP IA SD Subset

Data Protection and Privacy Im-pact Assessment Survey Data

Same as above.

FORENSOR E IA SD Subset

Ethical Impact Assessment Survey Data

Same as above.

FORENSOR CL IA SD Subset

Criminal Law Impact Assessment Survey Data

Same as above.

2.1.2 Dataset description FORENSOR DaPPECL IA SD Dataset will consist of all filled-in questionnaires that will be collected

within the context of the project Data Protection, Privacy, Ethical and Criminal Law (DaPPECL)

Impact Assessment and specifically within T2.2 “Impact assessment against DaPPECL require-

ments” of the project (M7-M12 or March to August 2016). Analytically, collected data will com-

prise written answers concerning the perceived implication of DaPPECL issues regarding the FO-

RENSOR project and any FORENSOR technology that may be developed.

FORENSOR DaPPECL IA SD Dataset will be processed within the same task (T2.2) in order to facil-

itate the assessment of the FORENSOR system:

upon personal data protection and privacy, and formulate recommendations on how to

improve the observance of data protection and privacy in the FORENSOR system;

against a specially developed ethical framework for the impact of the FORENSOR system

upon the applicable ethical principles, taking into account the specific features of that

system, and formulate recommendations on how to improve the observance of applica-

ble ethical principles in the FORENSOR system.

FORENSOR DaPPECL IA SD Dataset will be also used within T2.3 “Continuous monitoring of the

impacts of the FORENSOR upon personal data protection, privacy, ethical and criminal law

(DaPPECL) requirements” (M7-M36 or March 2016 to August 2018) in order to monitor the im-

pacts of FORENSOR on the requirements identified in DaPPPECL, as the system is integrated,

tested and evaluated in the field trials.

Finally, another purpose of the collection of this information (FORENSOR DaPPECL IA SD Dataset)

is to alert all partners to potential issues concerning DaPPECL matters that may not be immedi-

ately obvious, especially partners who do not have expertise in the area concerned.



The FORENSOR DaPPECL IA SD Dataset will be structured as shown in Table 2.

Key attributes, characteristics and other information regarding FORENSOR DaPPECL IA SD Dataset

are presented in Table 3.

Table 3: FORENSOR DaPPECL IA SD Dataset description

Description of the data that will be generated or col-lected

Quantitative and qualitative survey data from filled in DaPPECL Impact As-sessment questionnaires.

Data origin (in case it is col-lected)

Content acquired in the context of the DaPPECL Impact Assessment of the FORENSOR EU funded project (GA No. 653355).

Data nature Filled-in DaPPECL Impact Assessment questionnaires.

Data scale Tenths of filled-in questionnaires with less than 100 answered questions each.

To whom could the data be useful

Researchers involved in projects similar to FORENSOR, i.e. concerning the use of surveillance technologies in the area of criminal investigations.

Do the data un-derpin a scientific publication

To be determined in due time.3

Information on the existence (or not) of similar data

Data protection and privacy Impact Assessment questionnaires are used often for a variety of reasons (social sciences research, internal or external evaluation of security systems etc.). However proper public archiving of the respective datasets is much less common. To the best of our knowledge ethical and criminal law Impact Assessment questionnaires have not been used by other researchers in the past.

Information on the possibilities for integration and reuse of the data

The data could be reused for comparison purposes or being subject to different analyses in projects concerning the potential use of surveillance technologies for investigation of criminal activity.

2.2 Staged Surveillance Videos and FORENSOR Emulated Staged Surveillance Vid-

eos (FORENSOR Development and Testing Benchmark)


Table 4: Staged Surveillance Videos and FORENSOR Emulated Staged Surveillance Videos (FORENSOR Development and Testing Benchmark)


FORENSOR DTB Dataset

Staged Surveillance Videos Link will be provided at a later stage of the FORENSOR project.

3 Any developments will be duly reported in subsequent versions of the present deliverable.



FORENSOR DTB-E Dataset

FORENSOR Emulated Staged Surveillance Videos


1st Level Sub-set ID

1st Level Subset Name 1st Level Subset Reference

FORENSOR DTB1 Subset

Unfrequented provincial road surveillance Same as above.


Illegal trafficking in desolate coastal area Same as above.


Burglary at private house located in hamlet/ village

Same as above.


Pedestrian and bicycle path surveillance Same as above.

FORENSOR DTB-E1 Subset






Same as above.



2nd Level Sub-set ID

2nd Level Subset Name 2nd Level Subset Reference

FORENSOR DTB1.1 Subset

Car crash in unfrequented provincial road Same as above.


Illegal garbage disposal in unfrequented pro-vincial road adjacent to a protected/ conser-vation area

Same as above.


Zigzagging/ Driving under the influence of substances in unfrequented provincial road

Same as above.


Vehicles passing by through desolate coast access road

Same as above.


People passing by through desolate coast access road

Same as above.


Vessel approaching desolate coast Same as above.


People walking in desolate beach Same as above.


Vehicle stationary (suspicious activity) nearby private house located in hamlet/ vil-lage

Same as above.


Person stationary (suspicious activity) nearby private house located in hamlet/ vil-lage

Same as above.


People trespassing gate or fence of private house located in hamlet/ village

Same as above.




Vehicle that is not a bicycle enters pedes-trian and bicycle path

Same as above.


People entering pedestrian and bicycle path Same as above.

FORENSOR DTB-E1.1 Sub-set




Same as above.



Same as above.



Same as above.


People passing by through desolate coast access road

Same as above.







Same as above.


Person stationary (suspicious activity) nearby private house located in hamlet/ vil-lage

Same as above.



Same as above.



Same as above.



2.2.2 Dataset description FORENSOR DTB Dataset will consist of all staged surveillance videos that will be captured within

the context of project development and testing and specifically (but not exclusively) within T5.1

“Embedded low-level algorithms” of the project (M1-M20 or September 2015 to April 2017). Due

to the fact that the FORENSOR project foresees extensive hardware development towards the



creation of the homonymous sensor and given that the project benchmark is required as early as

possible for developing algorithms and designing hardware and software, the FORENSOR DTB Da-

taset will be initially captured using a vision system that has characteristics (e.g. analysis, sensitiv-

ity, etc.) as similar as possible to those of the novel Low-Power Imager, FORENSOR, i.e. a generic,

low resolution, grey-level consumer camera.

Subsequently, the FORENSOR DTB Dataset will be processed (within T5.1) by a software emulator

that will be developed specifically for this purpose, which will be emulating the behaviour of the

Low-Power Imager, i.e. the operations of the FORENSOR chip. The full set of videos that will be

generated in this way, i.e. the output of the emulator (low resolution, binary or grey-level videos),

will be the FORENSOR DTB-E Dataset.

The two datasets (original and emulated one) will be the Development and Testing Benchmark of

the FORENSOR project and will be subsequently used within WP5 “Ultra-low-power vision algo-

rithms” – and potentially other technical WPs, e.g. WP6 and WP7 – of the project in order to test

the developed low- and high-level algorithms and compare them to the performance of current

state-of-the-art algorithms.

The two datasets will include both training and testing videos while training videos will include

videos depicting both ‘positive’ as well as negative ‘events’.

The FORENSOR DTB and FORENSOR DTB-E Datasets will be structured as shown in Table 4.

Key attributes, characteristics and other information regarding the FORENSOR DTB and FOREN-

SOR DTB-E Datasets are presented in Table 5.

Table 5: FORENSOR DTB and FORENSOR DTB-E Datasets description

Description of the data that will be gen-erated or collected

FORENSOR DTB Da-taset

Low resolution (VGAi), grey-level (256 grey levels) vid-eos captured by a generic consumer camera from a sta-ble viewpoint.

FORENSOR DTB-E Da-taset

Low resolution (VGAi or QVGAii), binary or grey-level (256 grey levels) videos produced by a SW emulator of the FORENSOR chip.

Data origin (in case it is collected)

FORENSOR DTB Da-taset

Content acquired in the context of project develop-ment and testing of the FORENSOR EU funded project (GA No. 653355).


Content generated in the context of project develop-ment and testing of the FORENSOR EU funded project (GA No. 653355).

Data nature Videos.

Data scale FORENSOR DTB Da-taset

Several hours of video (30 mins to 10 hours estimate) or video data in the order of tenths or hundreds of gi-gabytes (> 15 GB, < 350 GB estimate).


Several hours of video (30 mins to 10 hours estimate) or video data in the order of gigabytes (> 1 GB, < 350 GB estimate).

To whom could the data be useful Image and video processing community.



Do the data underpin a scientific publication



Similar datasets or even databases of scientific or other origin do exist.

Information on the possibilities for integration and reuse of the data

The data could be merged with existing and/ or un-der development staged surveillance video da-tasets/ databases.

The data could be easily reused for scientific re-search or for testing and validation of commercial products, in the field of image and video pro-cessing.

2.3 Real Life Surveillance Videos (FORENSOR Pilots/ Field Tests Content)


Table 6: Real Life Surveillance Videos (FORENSOR Pilots/ Field Tests Content)


FORENSOR PFT Dataset

Real life surveillance videos Link will be provided at a later stage of the FORENSOR project.

1st Level Sub-set ID

1st Level Subset Name 1st Level Subset Reference

FORENSOR PFT1 Subset






Same as above.



2nd Level Sub-set ID

2nd Level Subset Name 2nd Level Subset Reference

FORENSOR PFT1.1 Subset


FORENSOR PFT1.2 Sub-set5


Same as above.



Same as above.



Same as above.

4 Any developments will be duly reported in subsequent versions of the present deliverable. 5 FORENSOR PFT1.2 Subset is under consideration.




People passing by through desolate coast ac-cess road

Same as above.







Same as above.


Person stationary (suspicious activity) nearby private house located in hamlet/ village

Same as above.



Same as above.



Same as above.

FORENSOR PFT4.2 Sub-set6


2.3.2 Dataset description FORENSOR PFT Dataset will consist of all real life surveillance videos that will be captured within

the context of the project pilots and field tests and specifically within T8.1 “Content acquisition”

of the project (M7-M18 or March to February 2016); it will be the full content of the project pilots

and field tests. FORENSOR PFT Dataset will be then processed within T8.2 “Pilots” (M16-M32 or

December 2016 to April 2018) in order to:

a. drive the demonstration and highlight the innovative functionalities of FORENSOR, and

b. validate the project into real applications in three European countries: Spain, Portugal

and Italy.

The FORENSOR PFT Dataset will be structured as shown in Table 6.

Key attributes, characteristics and other information regarding FORENSOR PFT Dataset are pre-

sented in Table 7.

Table 7: FORENSOR PFT Dataset description

Description of the data that will be generated or col-lected

Low resolution (VGAi), binary or grey-level (256 grey levels) vid-eos captured by a low power static rigged visual sensor.


Content acquired in the context of the pilots and field tests of the FORENSOR EU funded project (GA No. 653355).

Data nature Videos.

Data scale Several hours of video (30 mins to 10 hours estimate) or video data in the order of gigabytes (> 4 GB, < 350 GB estimate).

6 FORENSOR PFT4.2 Subset is under consideration.




Image and video processing community.

Do the data underpin a scien-tific publication



Similar datasets or even databases of scientific or other origin do exist.

Information on the possibili-ties for integration and reuse of the data

The data could be merged with existing and/ or under de-velopment real life surveillance video datasets/ databases.

The data could be easily reused for scientific research or for testing and validation of commercial products, in the field of image and video processing.

2.4 Automatically Obtained Image and Video Forensic Evidence (FORENSOR Pi-

lots/ Field Tests Testbed Data)


Table 8: Automatically Obtained Image and Video Forensic Evidence (FORENSOR Pilots/ Field Tests Testbed Data)


FORENSOR Evidence Dataset

Automatically Obtained Image and Video Forensic Evidence


1st Level Subset ID 1st Level Subset Name 1st Level Subset Reference

FORENSOR Image Evidence Dataset

Automatically Obtained Image Forensic Evidence

Same as above.

FORENSOR Video Ev-idence Dataset

Automatically Obtained Video Forensic Evidence

Same as above.

2.4.2 Dataset description FORENSOR Evidence Dataset will consist of all parts of the FORENSOR PFT Dataset that will be

automatically identified by the FORENSOR system as forensic evidence, i.e. as evidence regarding

a specific event that has been, again, automatically identified by the system. The FORENSOR Evi-

dence Dataset will consist of still images (video frames) and limited length 6videos (video seg-

ments).

The FORENSOR Evidence Dataset will be structured as shown in Table 8.

Key attributes, characteristics and other information regarding FORENSOR Evidence Dataset are

presented in Table 9.

Table 9: FORENSOR Evidence Dataset description

Description of the data that will be generated or collected

Low resolution (VGAi), grey-level (256 grey levels) images or videos captured by a low power static rigged visual sensor.





Content acquired in the context of the pilots and field tests of the FORENSOR EU funded project (GA No. 653355).

Data nature Images and videos.

Data scale Several hundred images (10 to 1,000 estimate) or image data in the order of megabytes (> 3 MB, < 300 MB esti-mate).

Several minutes of video (10 to 120 minutes estimate) or video data in the order of gigabytes (> 5 GB, < 65 GB esti-mate).


Social sciences researchers, judicial/legal experts, digital fo-rensic experts, LEAs or LEA related organisations, Image and video processing community.

Do the data underpin a scien-tific publication



Similar datasets or even databases of scientific or other origin do exist but are rarely shared publicly.

Information on the possibili-ties for integration and reuse of the data

The data could be merged with existing and/ or under de-velopment similar datasets/ databases.

The data could be easily reused for scientific research in the fields of social sciences and image and video pro-cessing.




3 Standards and Metadata

3.1 General note on dataset structure The structure of the FORENSOR DTB and FORENSOR DTB-E Datasets and the FORENSOR PFT Da-

taset that is shown in Table 4 and Table 6 respectively, corresponds to the three project UCs and

one project FT and the respective events to be detected (as they are currently identified):

UC1: Unfrequented provincial road surveillance

o Car crash in unfrequented provincial road

o Illegal garbage disposal in unfrequented provincial road adjacent to a protected/

conservation area

o Zigzagging/ Driving under the influence of substances in unfrequented provincial

road

UC2: Illegal trafficking in desolate coastal area

o Vehicles passing by through desolate coast access road

o People passing by through desolate coast access road

o Vessel approaching desolate coast

o People walking in desolate beach

UC3: Burglary at private house located in hamlet/ village

o Vehicle stationary (suspicious activity) nearby private house located in hamlet/

village

o Person stationary (suspicious activity) nearby private house located in hamlet/

village

o People trespassing gate or fence of private house located in hamlet/ village

FT1: Pedestrian and bicycle path surveillance

o Vehicle that is not a bicycle enters pedestrian and bicycle path

o People entering pedestrian and bicycle path

Note: The final project UCs and FTs will be defined in D3.1 “Use case analysis and user scenarios”

(M6, February 2015), as contractually agreed. Any needed changes to the description of the FO-

RENSOR DTB and the FORENSOR DTB-E Datasets will be reported in subsequent versions of the

present document as it will be a “living document”, updated accordingly until the end of the pro-

ject.

3.2 FORENSOR DaPPECL IA SD Dataset FORENSOR DaPPECL IA SD Dataset data will concern potential DaPPECL issues that partners see

in the FORENSOR project. This will include opinions based from varying perspectives including

both technical and those based in the law enforcement sector. Answers will be given in the light

of the varying expertise of each concerned partner. The surveys will be conducted in a way that

allows for the information generated to be evaluated objectively. This will occur through the use

of questions that are designed to elaborate issues that exist but which may not be obvious given

the particular background of the concerned interviewee. The resulting data will be stored in the

form of a set of PDF files.



3.3 Other datasets The FORENSOR DTB, FORENSOR DTB-E, and FORENSOR PFT Datasets will consist of uncompressed

low resolution, binary or grey-level videos while the FORENSOR Evidence Dataset will consist of

uncompressed low resolution, grey-level images and videos.

The metadata of all four aforementioned datasets will consist of selected object, event or other

system attributes. The metadata will be generated either automatically by the system or through

manual content annotation. The metadata will be associated with specific frames, sequences or

whole videos.

Metadata structure is expected to be the same for all four aforementioned datasets and common

for all project UCs and the FT (as those are currently defined); each UC and the FT will use a proper

database to store metadata. Metadata will be framed using XML and the respective structure will

include at least the following:

Metadata that describe objects/events

o Existing/detected object type (from a predefined list of object types)

o Occurring/detected event type (from a predefined list of event types)

o Identification of frames where an event of a certain type occurs

o Identification of locations within a frame where an object or a certain type ap-

pears

o Text-based descriptions of actions in video and/or image sequences (e.g. a car

passing, a person running, two people walking etc.)

Metadata that trace the path/process (workflow) of gathering metadata for an ob-

ject/event, i.e. chain of custody of evidence, in the system.

o Time information (e.g. timestamps)

o Location information (e.g. imaging device georeferenced coordinates)

o Identification of the algorithms that have been used

o Identification of algorithmic input and output

Metadata to monitor/ control the use of the metadata

o DaPPECL requirements

o Logs

More detailed descriptions of the metadata will become available as part of the work in various

project WPs – i.e. DaPPECL constraints (WP2), system specification (WP3), algorithm development

(WP5), secure communications infrastructure (WP6), system integration (WP7), project pilots

(WP8) – and will be duly reported in subsequent versions of the present deliverable.



4 Data Sharing

4.1 FORENSOR DaPPECL IA SD Dataset The decision on publicly sharing parts or the entirety of the FORENSOR DaPPECL IA SD Dataset will

be taken in due time – when the dataset is available – and will be duly reported in subsequent

versions of present deliverable. Besides their scientific value, the data contained in this dataset

will also give away the full profile of the FORENSOR system in terms of privacy protection, ethics

and the criminal law (e.g. preservation of the chain of custody of evidence). Therefore, the FO-

RENSOR consortium will carefully examine to what extent the FORENSOR DaPPECL IA SD Dataset

can be publicly shared.

In case it is decided to publicly share parts of the FORENSOR DaPPECL IA SD Dataset this will be

realised through their inclusion as an annex to the public version of D2.3 “The impact assessment

report” (which will be available for download from the project website) and/or separately, as PDF

files, through the project website.

4.2 Other datasets Due to the large estimated size of the FORENSOR DTB, FORENSOR DTB-E, and FORENSOR PFT

Datasets (currently estimated in the order of gigabytes), the FORENSOR consortium decided that

the only appropriate way for sharing them would be through a well organised open access repos-

itory. Since, no consortium partner maintains an appropriate institutional repository and no rele-

vant subject repository was found, it was decided that if the three aforementioned datasets were

to be shared they would be deposited in ZENODO9, a repository hosted by CERN and available to

all.

The FORENSOR consortium will carefully consider if any part of the FORENSOR Evidence Dataset

can be shared with specific groups of individuals (e.g. LEAs) or with the public and for what reason;

all ethical and legal aspects will be thoroughly examined by the Project Ethical Manager, Data

Controller and Security Officer. In case of a positive decision, careful anonymization will take place

in order to remove any personal data depicted in the images or videos (e.g. blurring of faces). In

such case, the FORENSOR Evidence Dataset will be also deposited in ZENODO.

ZENODO supports Closed, Open and Embargoed10 Access. However, only Open Access uploads

are displayed on the front-page of the ZENODO website. Closed Access uploads are still discover-

able through search queries, their DOI11, and any community collections where they are included.

Metadata is licensed under CC012, except for email addresses. All metadata is exported via OAI-

9 https://zenodo.org/. 10 Users may deposit content under an embargo status and provide and end date for the embargo. The repository will restrict access to the data until the end of the embargo period; at which time, the content will become publically available automatically. 11 A Digital Object Identifier (DOI) is a serial code used to uniquely identify objects. Further information can be found at https://en.wikipedia.org/wiki/Digital_object_identifier. 12 Creative Commons “No Rights Reserved” license (CC0) enables scientists, educators, artists and other creators and owners of copyright- or database-protected content to waive those interests in their works and thereby place them as completely as possible in the public domain, so that others may freely build upon, enhance and reuse the works for any purposes without restriction under copyright or database law. Further information can be found at https://creativecommons.org/about/cc0.

https://zenodo.org/

https://en.wikipedia.org/wiki/Digital_object_identifier

https://creativecommons.org/about/cc0



PMH13 and can be harvested. Access to metadata and data files is provided over standard proto-

cols such as HTTP and OAI-PMH.

ZENODO accepts data under a variety of licenses, but extra benefits, in terms of visibility and

credit, and additional services and upload quotas are offered to data deposited under the most

open licenses.

Note: The full set of ZENODO “Terms of Use” can be found online at https://zenodo.org/terms

while the full set of ZENODO Policies can be found online at https://zenodo.org/policies.

4.2.1 Access and licensing The FORENSOR consortium will take its final decisions regarding the sharing, access, and licensing

strategies for FORENSOR DTB, FORENSOR DTB-E, FORENSOR PFT, and FORENSOR Evidence Da-

tasets in due time, when the datasets are available. All relevant details will be duly reported in

subsequent versions of the present deliverable.

4.2.2 Re-use FORENSOR DTB, FORENSOR DTB-E, and FORENSOR PFT Datasets will consist of videos in standard

video formats (easily readable by any standard video player), thus enabling easy re-use. FOREN-

SOR Evidence Dataset will consist of images and videos in standard formats, thus easily readable.

In all four aforementioned cases, no specific SW or HW will be needed in order to view or re-use

the datasets.

Note: FORENSOR will not provide open access to the software emulator that will be developed

specifically for emulating the behaviour of the Low-Power Imager, i.e. the operations of the FO-

RENSOR chip, and which will be used for processing the FORENSOR DTB Dataset in order to acquire

the FORENSOR DTB-E Dataset. It will neither provide open access to any other algorithm developed

during the project, which will not be needed in order to access or re-use any of the datasets de-

scribed in this document.

4.2.3 Reasons for not sharing

Note: FORENSOR consortium declares that it reserves the right to exclude any video(s) belonging

to the datasets described in the present deliverable (or later versions of it) from the dataset they

belong to and/or from Open Access in general in case it interferes with ethical rules, or contains

personal data, or raises IPR, commercial, privacy-related or security issues.

4.2.3.1 FORENSOR DTB and FORENSOR DTB-E Datasets

FORENSOR DTB and FORENSOR DTB-E Datasets might contain videos where personal data (such

as the faces for example) of the actors, which voluntarily participated in the creation of the videos,

are shown. However, all participants to the research will be requested, prior to their participation,

to sign an informed consent form in which they will: (a) acknowledge the right of the FORESNSOR

consortium to provide Open Access to the project datasets (as these are described and under the

13 The Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) is a low-barrier mechanism for repository interoperability. Further information can be found at: https://www.openar-chives.org/pmh/.

https://zenodo.org/terms

https://zenodo.org/policies

https://www.openarchives.org/pmh/

https://www.openarchives.org/pmh/



terms described in the present deliverable or in later versions of this deliverable), and (b) author-

ise the FORENSOR consortium to provide Open Access to all the videos depicting them. These

actions reflect the FORENSOR Consortium’s commitment to conduct responsible research and

fulfill respective legal and regulatory requirements in all the countries where videos will be shot.

4.2.3.2 FORENSOR PFT Dataset

FORENSOR PFT Dataset might contain videos where personal data (such as the faces for example)

of bypassers are shown. For this reason, we have foreseen the placement of appropriate informa-

tive signs in all pilot and field test locations where the various videos of the FORENSOR PFT Da-

taset will be captured. The signs will inform people – prior their entering the area of the pilot/

field test – about the experimental sensor trial, the fact that they may be captured in video, and

that their personal data may be shared amongst the research partners of the FORENSOR consor-

tium for research purposes. Individual data subjects may object to this if they wish. These actions

reflect the FORENSOR Consortium’s commitment to conduct responsible research and fulfill re-

spective legal and regulatory requirements in all the countries where videos will be shot.

4.2.3.3 FORENSOR Evidence Dataset

FORENSOR Evidence Dataset will contain videos where personal data (such as the faces for exam-

ple) of people are shown. The FORENSOR consortium will carefully consider if any part of the

FORENSOR Evidence Dataset can be shared. Such data will only be shared where consent has been

secured from the relevant data subject or where the data in question has been anonymized.



5 Archiving and Preservation (Including Storage and Backup)

5.1 FORENSOR DaPPECL IA SD Dataset The FORENSOR DaPPECL IA SD Dataset will be preserved for an additional two years after the

end of the project. The cost for the preservation of the project website for the whole lifetime of

the FORENSOR project plus an additional two years has already been included in the project

budget. The approximated end volume of the dataset is tenths of filled-in questionnaires with

less than 100 answered questions each.

5.2 Other datasets We believe that the FORENSOR DTB, FORENSOR DTB-E, and FORENSOR PFT Datasets should be

preserved for at least seven years after the end of the FORENSOR project. This would be ensured

through their deposit in the ZENODO repository. Archiving and long-time preservation of the FO-

RENSOR Evidence Dataset could be also ensured (in case it is deemed necessary) through its de-

posit in the ZENODO repository.

Approximated end volume is in the order of gigabytes for FORENSOR DTB, FORENSOR DTB-E, and

FORENSOR PFT Datasets (more details are provided in Table 5 and Table 7 respectively) and in the

order of megabytes for the images and gigabytes for the videos in the case of the FORENSOR

Evidence Dataset (more details are provided in Table 9).

For further details regarding archiving and preservation of all four aforementioned datasets

please refer to ‘Appendix I: Relevant ZENODO Archiving and Preservation Policies’.



6 Appendix I: Relevant ZENODO Archiving and Preservation Poli-

cies

6.1 Retention period Items will be retained for the lifetime of the repository. This is currently the lifetime of the host

laboratory CERN, which currently has an experimental programme defined for the next 20 years

at least.

6.2 Functional preservation ZENODO makes no promises of usability and understandability of deposited objects over time.

6.3 File preservation Data files and metadata are backed up nightly and replicated into multiple copies in the online

system.

6.4 Fixity and authenticity All data files are stored along with a MD5 checksum of the file content. Files are regularly checked

against their checksums to assure that file content remains constant.

6.5 Succession plans In case of closure of the repository, best efforts will be made to integrate all content into suitable

alternative institutional and/or subject based repositories.

6.6 What does it cost? ZENODO is free for the long tail of Science. In order to offer services to the more resource hungry

research, we will introduce a ceiling to the free slice and offer paid for slices above, according to

the business model developed within the sustainability plan.

i Video Graphics Array (VGA) refers to a 640x480 pixels resolution. ii Quarter Video Graphics Array (QVGA) refers to a 320x240 pixels resolution.

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