To substantially reduce energy costs and consumption by State government, an energy/electricity competition was established in 2011 between the 16 largest energy-using agencies. Each agency's consumption of electricity (kWh) and total energy (MMBTU) from significant facilities is monitored in relation to a baseline year of FY 2008. Significant facilities are those that have been occupied by the State since 2008 and are air-conditioned.

An overall goal is set for State agencies to reduce energy/electricity consumption by 15% by 2015 to Lead By Example.

The Fiscal Year (FY) 2013 runs from July 1, 2012 through June 30, 2013. The Fiscal Year 2014 runs from July 1, 2013 through June 30, 2014.

This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain.

Part1 is available at https://zenodo.org/deposit/7157356 Part3 is available at https://zenodo.org/deposit/7158133 Part4 is available at https://zenodo.org/deposit/7158328

Details of the datasets are given below:

FILENAME FORMAT:

The filenames have the following format:

btc-tx-

where

For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from

block 100000 to block 149999 inclusive.

The files are compressed with bzip2. They can be uncompressed using command bunzip2.

TRANSACTION FORMAT:

Each line in a file corresponds to a transaction. The transaction has the following format:

BLOCK TIME FORMAT:

The block time file has the following format:

IMPORTANT NOTE:

Public Bitcoin Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as https://btcscan.org . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything.

NOTE:

If you use this dataset, please do not forget to add the DOI number to the citation.

If you use our dataset in your research, please also cite our paper: https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14

@incollection{kilicc2022analyzing, title={Analyzing Large-Scale Blockchain Transaction Graphs for Fraudulent Activities}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and {\c{S}}en, Alper}, booktitle={Big Data and Artificial Intelligence in Digital Finance}, pages={253--267}, year={2022}, publisher={Springer, Cham} }

In this project, we aimed to map the visualisation design space of visualisation embedded in right-to-left (RTL) scripts. We aimed to expand our knowledge of visualisation design beyond the dominance of research based on left-to-right (LTR) scripts. Through this project, we identify common design practices regarding the chart structure, the text, and the source. We also identify ambiguity, particularly regarding the axis position and direction, suggesting that the community may benefit from unified standards similar to those found on web design for RTL scripts. To achieve this goal, we curated a dataset that covered 128 visualisations found in Arabic news media and coded these visualisations based on the chart composition (e.g., chart type, x-axis direction, y-axis position, legend position, interaction, embellishment type), text (e.g., availability of text, availability of caption, annotation type), and source (source position, attribution to designer, ownership of the visualisation design). Links are also provided to the articles and the visualisations. This dataset is limited for stand-alone visualisations, whether they were single-panelled or included small multiples. We also did not consider infographics in this project, nor any visualisation that did not have an identifiable chart type (e.g., bar chart, line chart). The attached documents also include some graphs from our analysis of the dataset provided, where we illustrate common design patterns and their popularity within our sample.

To substantially reduce energy costs and consumption by State government, an energy/electricity competition was established in 2011 between the 16 largest energy-using agencies. Each agency's consumption of electricity (kWh) and total energy (MMBTU) from significant facilities is monitored in relation to a baseline year of FY 2008. Significant facilities are those that have been occupied by the State since 2008 and are air-conditioned.

An overall goal is set for State agencies to reduce energy/electricity consumption by 15% by 2015 to Lead By Example.

To view how the State and each of the 16 agencies are performing, please select between energy and electricity, and select an agency using the options to the right.

The Fiscal Year (FY) 2013 runs from July 1, 2012 through June 30, 2013. The Fiscal Year 2014 runs from July 1, 2013 through June 30, 2014.

To substantially reduce energy costs and consumption by State government, an energy/electricity competition was established in 2011 between the 16 largest energy-using agencies. Each agency's consumption of electricity (kWh) and total energy (MMBTU) from significant facilities is monitored in relation to a baseline year of FY 2008. Significant facilities are those that have been occupied by the State since 2008 and are air-conditioned.

An overall goal is set for State agencies to reduce energy/electricity consumption by 15% by 2015 to Lead By Example.

The Fiscal Year (FY) 2013 runs from July 1, 2012 through June 30, 2013. The Fiscal Year 2014 runs from July 1, 2013 through June 30, 2014.

Long term dataset showing the 30 year fixed rate mortgage average in the United States since 1971.

This dataset contains a range of directed signed networks (signed digraphs) from social domain. The data come from 9 different sources and in total there are 29 network files. There are two temporal networks and one multilayer network in this dataset. Each network is provided in two formats: edgelist (.csv) and .gml format.This dataset is provided under a CC BY-NC-SA Creative Commons v 4.0 license (Attribution-NonCommercial-ShareAlike). This means that other individuals may remix, tweak, and build upon these data non-commercially, as long as they provide citations to this data repository (https://doi.org/10.6084/m9.figshare.12152628) and the reference article listed below (https://doi.org/10.1038/s41598-020-71838-6), and license the new creations under the identical terms.For more information about the data, one may refer to the article below:Samin Aref, Ly Dinh, Rezvaneh Rezapour, and Jana Diesner. "Multilevel Structural Evaluation of Signed Directed Social Networks based on Balance Theory" Scientific Reports (2020) https://doi.org/10.1038/s41598-020-71838-6

This dataset contains ether as well as popular ERC20 token transfer transactions extracted from the Ethereum Mainnet blockchain.

Only send ether, contract function call, contract deployment transactions are present in the dataset. Miner reward transactions are not currently included in the dataset.

Details of the datasets are given below:

FILENAME FORMAT:

The filenames have the following format:

eth-tx-

where

For example file eth-tx-1000000-1099999.txt.bz2 contains transactions from

block 1000000 to block 1099999 inclusive.

The files are compressed with bzip2. They can be uncompressed using command bunzip2.

TRANSACTION FORMAT:

Each line in a file corresponds to a transaction. The transaction has the following format:

units. ERC20 tokens transfers (transfer and transferFrom function calls in ERC20

contract) are indicated by token symbol. For example GUSD is Gemini USD stable

coin. The JSON file erc20tokens.json given below contains the details of ERC20 tokens.

decoder-error.txt FILE:

This file contains transactions (block no, tx no, tx hash) on each line that produced

error while decoding calldata. These transactions are not present in the data files.

er20tokens.json FILE:

This file contains the list of popular ERC20 token contracts whose transfer/transferFrom

transactions appear in the data files.

-------------------------------------------------------------------------------------------

[

{

"address": "0xdac17f958d2ee523a2206206994597c13d831ec7",

"decdigits": 6,

"symbol": "USDT",

"name": "Tether-USD"

},

{

"address": "0xB8c77482e45F1F44dE1745F52C74426C631bDD52",

"decdigits": 18,

"symbol": "BNB",

"name": "Binance"

},

{

"address": "0x2af5d2ad76741191d15dfe7bf6ac92d4bd912ca3",

"decdigits": 18,

"symbol": "LEO",

"name": "Bitfinex-LEO"

},

{

"address": "0x514910771af9ca656af840dff83e8264ecf986ca",

"decdigits": 18,

"symbol": "LNK",

"name": "Chainlink"

},

{

"address": "0x6f259637dcd74c767781e37bc6133cd6a68aa161",

"decdigits": 18,

"symbol": "HT",

"name": "HuobiToken"

},

{

"address": "0xf1290473e210b2108a85237fbcd7b6eb42cc654f",

"decdigits": 18,

"symbol": "HEDG",

"name": "HedgeTrade"

},

{

"address": "0x9f8f72aa9304c8b593d555f12ef6589cc3a579a2",

"decdigits": 18,

"symbol": "MKR",

"name": "Maker"

},

{

"address": "0xa0b73e1ff0b80914ab6fe0444e65848c4c34450b",

"decdigits": 8,

"symbol": "CRO",

"name": "Crypto.com"

},

{

"address": "0xd850942ef8811f2a866692a623011bde52a462c1",

"decdigits": 18,

"symbol": "VEN",

"name": "VeChain"

},

{

"address": "0x0d8775f648430679a709e98d2b0cb6250d2887ef",

"decdigits": 18,

"symbol": "BAT",

"name": "Basic-Attention"

},

{

"address": "0xc9859fccc876e6b4b3c749c5d29ea04f48acb74f",

"decdigits": 0,

"symbol": "INO",

"name": "INO-Coin"

},

{

"address": "0x8e870d67f660d95d5be530380d0ec0bd388289e1",

"decdigits": 18,

"symbol": "PAX",

"name": "Paxos-Standard"

},

{

"address": "0x17aa18a4b64a55abed7fa543f2ba4e91f2dce482",

"decdigits": 18,

"symbol": "INB",

"name": "Insight-Chain"

},

{

"address": "0xc011a72400e58ecd99ee497cf89e3775d4bd732f",

"decdigits": 18,

"symbol": "SNX",

"name": "Synthetix-Network"

},

{

"address": "0x1985365e9f78359a9B6AD760e32412f4a445E862",

"decdigits": 18,

"symbol": "REP",

"name": "Reputation"

},

{

"address": "0x653430560be843c4a3d143d0110e896c2ab8ac0d",

"decdigits": 16,

"symbol": "MOF",

"name": "Molecular-Future"

},

{

"address": "0x0000000000085d4780B73119b644AE5ecd22b376",

"decdigits": 18,

"symbol": "TUSD",

"name": "True-USD"

},

{

"address": "0xe41d2489571d322189246dafa5ebde1f4699f498",

"decdigits": 18,

"symbol": "ZRX",

"name": "ZRX"

},

{

"address": "0x8ce9137d39326ad0cd6491fb5cc0cba0e089b6a9",

"decdigits": 18,

"symbol": "SXP",

"name": "Swipe"

},

{

"address": "0x75231f58b43240c9718dd58b4967c5114342a86c",

"decdigits": 18,

"symbol": "OKB",

"name": "Okex"

},

{

"address": "0xa974c709cfb4566686553a20790685a47aceaa33",

"decdigits": 18,

"symbol": "XIN",

"name": "Mixin"

},

{

"address": "0xd26114cd6EE289AccF82350c8d8487fedB8A0C07",

"decdigits": 18,

"symbol": "OMG",

"name": "OmiseGO"

},

{

"address": "0x89d24a6b4ccb1b6faa2625fe562bdd9a23260359",

"decdigits": 18,

"symbol": "SAI",

"name": "Sai Stablecoin v1.0"

},

{

"address": "0x6c6ee5e31d828de241282b9606c8e98ea48526e2",

"decdigits": 18,

"symbol": "HOT",

"name": "HoloToken"

},

{

"address": "0x6b175474e89094c44da98b954eedeac495271d0f",

"decdigits": 18,

"symbol": "DAI",

"name": "Dai Stablecoin"

},

{

"address": "0xdb25f211ab05b1c97d595516f45794528a807ad8",

"decdigits": 2,

"symbol": "EURS",

"name": "Statis-EURS"

},

{

"address": "0xa66daa57432024023db65477ba87d4e7f5f95213",

"decdigits": 18,

"symbol": "HPT",

"name": "HuobiPoolToken"

},

{

"address": "0x4fabb145d64652a948d72533023f6e7a623c7c53",

"decdigits": 18,

"symbol": "BUSD",

"name": "Binance-USD"

},

{

"address": "0x056fd409e1d7a124bd7017459dfea2f387b6d5cd",

"decdigits": 2,

"symbol": "GUSD",

"name": "Gemini-USD"

},

{

"address": "0x2c537e5624e4af88a7ae4060c022609376c8d0eb",

"decdigits": 6,

"symbol": "TRYB",

"name": "BiLira"

},

{

"address": "0x4922a015c4407f87432b179bb209e125432e4a2a",

"decdigits": 6,

"symbol": "XAUT",

"name": "Tether-Gold"

},

{

"address": "0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48",

"decdigits": 6,

"symbol": "USDC",

"name": "USD-Coin"

},

{

"address": "0xa5b55e6448197db434b92a0595389562513336ff",

"decdigits": 16,

"symbol": "SUSD",

"name": "Santender"

},

{

"address": "0xffe8196bc259e8dedc544d935786aa4709ec3e64",

"decdigits": 18,

"symbol": "HDG",

"name": "HedgeTrade"

},

{

"address": "0x4a16baf414b8e637ed12019fad5dd705735db2e0",

"decdigits": 2,

"symbol": "QCAD",

"name": "QCAD"

}

]

-------------------------------------------------------------------------------------------

This is the set of graphs used in the PACE 2022 challenge for computing the Directed Feedback Vertex Set, from the Heuristic track. It consists of 200 labelled directed graphs. The graphs are mostly not symmetric (an edge form u->v does not imply an edge from v->u), although some are symmetric. The graph labels are integers ranging from 1 to N.

There is the related PACE 2022 Exact dataset, which was for exact computation; those graphs are generally smaller and sparser, as only exact solutions were accepted.

The data format begins with one line N E 0, where N is the number of vertices, E is the number of edges, and 0 is the literal integer zero. The N subsequent lines are each a space-separated list of integers, such as 2 5 11 19. If that appeared on line number 1 (the first after N E 0), it would indicate that there are edges from vertex 1 to each of the vertices 2, 5, 11, and 19. Some lines are blank, and these indicates vertices with outdegree zero. An example graph would be ``` 4 4 0 2 3 3

1 ```

The dataset can be downloaded here. The 100 instances that were available for public testing are precisely the odd-numbered ones in that link; the public instances can be downloaded on their own here.

U.S. collision regulation boundaries are lines of demarcation delineating those waters upon which mariners shall comply with the International Regulations for Preventing Collisions at Sea, 1972 (72 COLREGS) and those waters upon which mariners shall comply with the Inland Navigation Rules. The waters inland of these lines are subject to the Inland Navigation Rules Act of 1980. The waters outside these lines are subject to the International Navigation Rules of the International Regulations for Preventing Collisions at Sea, 1972 (COLREGS). The Coast Guard has the legal authority to effect regulatory changes to COLREGS. Creation of features was interpreted from descriptions published in the Code of Federal Regulations Title 33, Part 80.

© MarineCadastre.gov This layer is a component of Navigation and Marine Transportation.

Marine Cadastre themed service for public consumption featuring layers associated with navigation and marine transportation.

This map service presents spatial information about MarineCadastre.gov services across the United States and Territories in the Web Mercator projection. The service was developed by the National Oceanic and Atmospheric Administration (NOAA), but may contain data and information from a variety of data sources, including non-NOAA data. NOAA provides the information “as-is” and shall incur no responsibility or liability as to the completeness or accuracy of this information. NOAA assumes no responsibility arising from the use of this information. The NOAA Office for Coastal Management will make every effort to provide continual access to this service but it may need to be taken down during routine IT maintenance or in case of an emergency. If you plan to ingest this service into your own application and would like to be informed about planned and unplanned service outages or changes to existing services, please register for our Data Services Newsletter (http://coast.noaa.gov/digitalcoast/publications/subscribe). For additional information, please contact the NOAA Office for Coastal Management (coastal.info@noaa.gov).

© MarineCadastre.gov

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-03-27 to 2025-03-26 about stock market, average, industry, and USA.

The Observatory Knowledge Graph (OKG) is a knowledge graph with tweets on inequality in terms of the OBIO ontology (https://w3id.org/okg/obio-ontology/), which integrates social media metadata with various types of linguistic knowledge. The OKG can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse on inequality.

We retrieved tweets and retweets published from the end (30th) of May 2020 to the beginning (1st) of May 2023.

In this version of the OKG, we use a sample of 85,247 tweets, published from May 30th to August 27th, 2020. To be compliant with Twitter's policies, we remove usernames and id's, as well as the tweet texts and sentences. We also replace user IRIs with skolem IRIs through skolemization.

Access to the OKG as well as the SPARQL endpoint can be requested by sending a mail to the contact person (l.stork@uva.nl) with the following information:

1. A description of the use case
2. Affiliation of the researchers involved
3. How their work is in line with Twitter's policies: https://developer.twitter.com/en/developer-terms/policy#4-d

The main stock market index in Australia (ASX200) decreased 241 points or 2.95% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Australia. Australia Stock Market Index - values, historical data, forecasts and news - updated on March of 2025.