About

This dataset lists all cities that received an A score from CDP in 2019. More information about CDP's scoring methodology for Cities is available here: https://www.cdp.net/en/cities/cities-scores/scoring-methodology

Sources

https://data.cdp.net/Governance/2019-A-list-Cities-with-Response-Links/krms-c95k

Blockchain data query: Lista CDP Collateral List

cities_2020-11-10.csv : City adhesion to the Global covenant of Mayors for Climate & Energy

Source

technical_words.csv :

Environment related words scraped on three websites, corresponding to the three main environmental topics of CDP's questionnaires:

• Wikipedia's Climate Change Glossary
• Wikipedia's Environmental science Glossary
• Glossary of Water Management Terms from the Canadian goverment

The list of words have been translated in French, Spanish and Portuguese.

2019EUCitiesIndexResults.xlsx : 2019 Sustainability Developpment Goals Index by US cities

Source

2019USCitiesIndexResults.xlsx : 2019 Sustainability Developpment Goals Index by EU cities

Source

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Context

Copy Detection Patterns (CDP) are noisy, black-and-white, maximum entropy image, generated with a secret key. CDPs are designed to be sensitive to countefeit attempts, and have received significant attention from academia and industry as a practical means to facilitate detection of counterfeits. Their security level against sophisticated attacks has been studied theoretically and practically in different research papers, but it is not clear as of today whether it is possible to counterfeit effectively a CDP.

We therefore created this dataset to 1) stimulate research on the security of CDPs, 2) evaluate the security level against different types of copies, and 3) develop enhanced algorithms to improve detection performance (e.g. over the commonly used bit error rate which is often used in the literature).

It was shown in prior arts that the simple duplication using a copy machine is not an effective way to copy a CDP. Therefore, the most promising solution appears to be the estimation of CDP from printed-and-scanned image either by using image processing techniques, or by doing the CDP estimation using a neural network approach.

The second question is “what is the efficient CDP detector?”. Indeed, depending on the specific processing involved,

Content

The digital binary (template) CDPs have size of 52×52 pixels, with 1 pixel per element which is defined at 600 ppi, printed with 600 dpi and scanned with 2400 dpi using printer Canon IR-ADV C5535i. Therefore, the printed and scanned CDPs have the size of 208 × 208 pixels (that corresponds to 4 pixels per element) and are grayscale images. The estimation methods used in this work are the following: 1) Binarization using Otsu thresholding (called Otsu). 2) Unsharp masking followed by binarization using Otsu thresholding (called unsharp+Otsu). 3) Binarization using fully connected neural network with 2 hidden layers (called FC2). 4) Binarization using fully connected neural network with 3 hidden layers (called FC3). 5) Binarization using fully connected neural network with 4 hidden layers (called FC4). 6) Binarization using bottleneck DNN (called BN DNN). 7) Unsharp masking followed by binarization using bottleneck DNN (called unsharp+BN DNN).

The unique_cdp dataset consists of 5000 unique CDPs printed once and then estimated using six attacks (methods 1-6 from the list). It consists of 5000 digital templates and the corresponding 5000 original prints (authentic CDPs), and 4 folders of copies of the last 1500 original CDPs (the first 3500 were used for training the counterfeiting algorithm).

The batch_cdp dataset consists of CDP printed per batch, i.e. each CDP is printed multiple times. This is representative of the application of CDPs with industrial printers such as offset, flexo and rotogravure. This dataset consists of 50 unique CDPs, and each CDP is printed-and-scanned 50 times. That gives us in total 2500 printed and scanned versions of 50 unique CDP. After that we have applied 4 estimation attacks (methods 1, 2, 6 and 7 in the list) in fusion with averaging attack. The folder “fake batch” consists of 4 sub-folders with fakes obtained using estimation methods (1), (2), (6) and (7).

Acknowledgements

This research was presented in article “Can Copy Detection Patterns be copied? Evaluating the performance of attacks and highlighting the role of the detector” published in WIFS 2021. Please cite the corresponding reference while using one of these databases in your academic work. E. Khermaza, I. Tkachenko, J. Picard, “Can Copy Detection Patterns be copied? Evaluating the performance of attacks and highlighting the role of the detector”, WIFS 2021, December 2021, Montpellier, France

Inspiration

These datasets are provided for academic use only with the objective of improving our understanding on the security aspects of CDPs, and can be used to address these questions: • How can we improve the detection performance? How to most efficiently separate the fake (estimated) CDPs from the original CDPs? • Id it possible to generate CDP copies that can be undetectable ?

If you would like to test your own copies, you can try on your printer by printing them at 600ppi. You may also reach out to us so we can print and scan them in comparable conditions as used for this dataset.

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The data provided is a list of approved micro projects by Edo CSDP and SEEFOR. It states the type, cost of the project, its IDA, the location of the project in terms of its GPS, the community, the local government area and the senatorial district.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of most incorporated places in this shapefile are as of January 1, 2019, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census.

Coups d'état are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup D'état Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e. realized or successful coups, unrealized coup attempts, or thwarted conspiracies) the type of actor(s) who initiated the coup (i.e. military, rebels, etc.), as well as the fate of the deposed leader. This is version 2.0.0 of this dataset. The first version, v.1.0.0, was released in 2013. Since then, the Cline Center has taken several steps to improve on the previously-released data. These changes include:

1. Filling in missing event data values
2. Removing events with no identifiable dates
3. Reconciling event dates from sources that have conflicting information
4. Removing events with insufficient sourcing (each event now has at least two sources)
5. Removing events that were inaccurately coded and did not meet our definition of a coup event
6. Extending the time period covered from 1945-2005 to 1945-2019
7. Removing certain variables that fell below the threshold of inter-coder reliability required by the project
8. The spreadsheet ‘CoupInventory.xls’ was removed because of inadequate attribution and citation in the event summaries

Items in this Dataset 1. CDP v.2.0.1 Codebook.pdf

• This 14-page document provides a description of the Cline Center Coup D’état Project Dataset. The first section of this codebook provides a succinct definition of a coup d’état used by the project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The second section describes the methodology used to produce the data. Created November 2020. Revised January 2021 to correct a typo in the definition of "Realized Coup"

2. Coup_Data_v2.0.0.csv

• This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup D’etat Project. It contains 29 variables and 943 observations. Created November 2020

3. Source Document v2.0.0.pdf

• This 305-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify each particular event. Created November 2020

4. README.md

• This file contains useful information for the user about the dataset. It is a text file written in mark down language. Created November 2020

Citation Guidelines 1) To cite this codebook please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, and Jonathan Bonaguro. 2021. “Cline Center Coup D’état Project Dataset Codebook”. Cline Center Coup D’état Project Dataset. Cline Center for Advanced Social Research. V.2.0.1. January 08. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V2 2) To cite the data please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, and Jonathan Bonaguro. 2020. Cline Center Coup D’état Project Dataset. Cline Center for Advanced Social Research. V.2.0.0. November 16. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V2

The strategy and policies for retailing set out in this plan have been prepared having regard to the guidance set out in the ‘Retail Planning Guidelines for planning authorities’ (DoECLG, 2012). This development plan addresses the list of matters to be considered in a plan, as required by ‘Section 3.3 Development Plans and Retailing’ of the Guidelines.

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The data set provides unprocessed and processed seismic reflection data collected during Expedition PS87 (ARK-XXVIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 2014. Detailed data acquisition parameters for the profile are documented in the expedition report (Stein, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. 2. Primary data consist of CDP-sorted data in seg-y format. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector that parallels the sea floor at depths of about 190 ms two-way travewltime (~150 m) is an artifact which has not been removed.

The data set provides unprocessed and processed seismic reflection data collected during Expedition PS72 (ARK-XXIII/3) onboard RV Polarstern in the southeastern Arctic Ocean in 2008. Detailed data acquisition parameters for the profile are documented in the expedition report (Jokat, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) navigation data with date, time, and coordinates, (b) a shot list of date, time and shot-number, and (c) demultiplexed seismic data in seg-y format. Please note: for some very short consecutive profiles the demultiplexed raw data still are merged in one file. 2. Primary data consist of (a) CDP-sorted data in seg-y format, and (b) navigational data in a conventional UKOOA- file. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector running parallel to the seafloor at a depth of about 190 ms (~150 m) does not appear to be a ghost or bubble signal. It is likely an artifact created during acquisition that was not removed.

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The data set provides unprocessed and processed seismic reflection data collected during Expedition PS72 (ARK-XXIII/3) onboard RV Polarstern in the southeastern Arctic Ocean in 2008. Detailed data acquisition parameters for the profile are documented in the expedition report (Jokat, 2015). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) navigation data with date, time, and coordinates, (b) a shot list of date, time and shot-number, and (c) demultiplexed seismic data in seg-y format. Please note: for some very short consecutive profiles the demultiplexed raw data still are merged in one file. 2. Primary data consist of (a) CDP-sorted data in seg-y format, and (b) navigational data in a conventional UKOOA- file. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates. Please note that the low-frequency reflector running parallel to the seafloor at a depth of about 190 ms (~150 m) does not appear to be a ghost or bubble signal. It is likely an artifact created during acquisition that was not removed.

(A) Coomassie‐stained gels of liposome co‐sedimentation assays using either Atg5-Atg12/Atg16 (upper panel) or Atg5/Atg16 (lower panel) and liposomes with the indicated lipid composition (Folch: Folch lipids, PE/PI3P: 40% POPC, 35% POPS, 20% POPE, 5% PI3P, PE/PI: 40% POPC, 35% POPS, 20% POPE, 5% PI, DAG/PI3P: 40% POPC, 35% POPS, 20% DAG, 5% PI3P, DAG/PI: 40% POPC, 35% POPS, 20% DAG, 5% PI). Note that the Atg5-Atg12/Atg16 and the Atg5/Atg16 complexes show almost identical binding behaviours.. List of tagged entities: ATG12 (uniprot:P38316), ATG16 (uniprot:A6ZML8), ATG5 (uniprot:Q12380), CDP-diacylglycerol (CHEBI:17962), phosphatidylethanolamine (CHEBI:16038), phosphatidylinositol (CHEBI:28874), phosphatidylinositol 3-phosphate (CHEBI:26034), Coomassie dye protein staining (bao:BAO_0002464)

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The data set provides unprocessed and processed seismic reflection data collected during Expedition PS19 (ARK-VIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 1991. Detailed data acquisition parameters for the profile are documented in the expedition report (Fütterer, 1992). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. Please note: for some very short consecutive profiles the demultiplexed raw data still are merged in one file. 2. Primary data consist of (a) CDP-sorted data in seg-y format, and (b) navigational data in a conventional UKOOA- file. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates.

The data set provides unprocessed and processed seismic reflection data collected during Expedition PS19 (ARK-VIII/3) onboard RV Polarstern in the central and Arctic Ocean and Eurasia Basin in 1991. Detailed data acquisition parameters for the profile are documented in the expedition report (Fütterer, 1992). Metadata, recording parameters, and the seismic data processing sequence are documented in the EBCDIC-Headers enclosed in the seg-y files. Navigation information is included in the seg-y headers, and in the CDP-SHOT-latitude-longitude file (see data product). A ReadMe file additionally lists the metadata and recording parameters for the seismic profile. The data set is grouped into three categories: 1. Raw data comprise (a) demultiplexed seismic data in seg-y format, (b) a shot list of date, time and shot-number, and (c) navigation data with date, time, and coordinates. Please note: for some very short consecutive profiles the demultiplexed raw data still are merged in one file. 2. Primary data consist of (a) CDP-sorted data in seg-y format, and (b) navigational data in a conventional UKOOA- file. 3. The data product is provided as (a) stacked or time-migrated version of the seismic profile in seg-y format and (b) a list with CDP and SHOT numbers as well as geographical coordinates.