The junk cleanup software market is experiencing robust growth, driven by the increasing prevalence of digital data and the need for efficient system optimization. The market's expansion is fueled by several factors, including the rising adoption of cloud-based services, the proliferation of mobile devices generating substantial data clutter, and heightened concerns about system security and performance. While on-premises solutions still hold a significant segment of the market, cloud-based options are rapidly gaining traction, offering scalability and accessibility advantages to both enterprise and personal users. The enterprise segment dominates market share due to the higher volume of data managed and stricter regulatory compliance requirements. Key players in this competitive landscape continuously innovate with features like advanced malware detection and proactive system maintenance, creating a dynamic market environment. We estimate the 2025 market size at $2.5 billion, considering the growth potential and the current trajectory of technological advancements. A projected CAGR of 15% from 2025 to 2033 indicates a substantial market expansion within the forecast period. Geographic distribution shows strong growth in North America and Asia-Pacific regions, driven by increased internet penetration and higher smartphone adoption. However, the market faces challenges, including the increasing sophistication of malware and the emergence of alternative system optimization techniques. Despite the challenges, the market is expected to maintain a strong growth trajectory. The continuous development of more sophisticated junk cleanup tools, capable of handling ever-increasing data volumes and complex threats, ensures ongoing demand. Furthermore, the rising awareness of digital privacy and data security is further boosting the adoption of such software. The integration of AI and machine learning technologies into junk cleanup solutions also adds to their value proposition, resulting in more efficient and effective cleanup processes. The competitive landscape, with a mix of established players and new entrants, promotes innovation and pushes the industry forward. Regional variations in growth rate will depend largely on factors such as infrastructure development and digital literacy levels. Overall, the junk cleanup software market promises a significant and sustained growth period, with continued innovation shaping the industry landscape in the coming years.

Data Eraser Software Market Outlook

In 2023, the global data eraser software market size was valued at $1.2 billion, with an expected CAGR of 15.6% from 2024 to 2032, driving the market size to reach approximately $4.1 billion by 2032. The significant growth factors contributing to this market expansion include the increasing concerns over data privacy and security, stringent data protection regulations, and the rising incidences of data breaches. Growing awareness about the importance of securely erasing data to prevent unauthorized access also fuels the demand for data eraser software globally.

The demand for data eraser software is significantly driven by the increasing adoption of digital technologies across various industries. Businesses and organizations are increasingly generating large volumes of data, necessitating the need for robust data management and security solutions. The growing frequency of data breaches and cyber-attacks has heightened the awareness of data security, compelling organizations to adopt data eraser software to securely dispose of sensitive information. Moreover, the enforcement of stringent data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, mandates organizations to ensure data privacy and secure data disposal practices.

Another critical factor driving the growth of the data eraser software market is the increasing trend of remote working and digital transformation across enterprises. The COVID-19 pandemic has accelerated the adoption of remote working, leading to a surge in the use of personal devices and cloud storage solutions. This shift has increased the risk of data breaches, propelling organizations to invest in data eraser software to mitigate potential security threats. Additionally, the growing trend of digital transformation has led to the proliferation of data, further emphasizing the need for efficient data management and secure data disposal solutions.

The rising awareness among consumers regarding data privacy and the potential risks associated with improper data disposal has also contributed to the market's growth. With the increasing use of electronic devices and the rapid pace of technological advancements, consumers are becoming more conscious of the need to securely erase data before disposing or selling their devices. This heightened awareness is driving the demand for data eraser software, as individuals seek reliable solutions to permanently remove data and protect their personal information from falling into the wrong hands.

In addition to data eraser solutions, the market for Computer Junk Cleanup Software is gaining traction as organizations and individuals seek to optimize their digital environments. This type of software is designed to remove unnecessary files, temporary data, and other digital clutter that can accumulate over time, slowing down system performance and consuming valuable storage space. As digital transformation continues to accelerate, the need for efficient system maintenance tools becomes more pronounced. Computer Junk Cleanup Software not only helps in enhancing system performance but also plays a crucial role in maintaining data privacy by eliminating residual data that could potentially be exploited. This growing demand is reflected in the increasing adoption of such software across various sectors, as users strive to maintain optimal system efficiency and security.

Regionally, North America is expected to dominate the data eraser software market, accounting for the largest market share during the forecast period. This can be attributed to the presence of major technology companies, stringent data protection regulations, and high awareness of data security practices in the region. Europe is also anticipated to witness significant growth, driven by the enforcement of GDPR and increasing adoption of data eraser software across various industries. The Asia Pacific region is projected to exhibit the highest CAGR, fueled by the rapid digitalization, increasing number of small and medium enterprises (SMEs), and growing awareness of data privacy and security in emerging economies such as China and India.

Component Analysis

The data eraser software market can be broadly segmented into software and services. The software segment encompasses various data erasure tools and applications that enable users to securely delete data from storage devices, such

Version 6 is the current version of this dataset. Older versions are no longer available and have been superseded by Version 6.The Precipitation Retrieval and Profiling Scheme (PRPS)is designed to provide a best estimate of precipitation based upon matched SAPHIR-DPR observations. This fulfils in part the essence of GPM (and its predecessor, TRMM) in which the core observatory acts as a calibrator of precipitation retrievals for the international constellation of passive microwave instruments. In doing so the retrievals from the partner constellation sensors are able to provide greater temporal sampling and great spatial coverage than is possible from the DPR instrument alone. However, the limitations of the DPR instrument are transferred through the retrieval scheme to the resulting precipitation products. Fundamental to the design of the PRPS is the independence from any dynamic ancillary data sets: the retrieval is based solely upon the satellite radiances, a static a priori radiance-rainrate database (and index), and (static) topographical data. Critically, the technique is independent of any model information, unlike the retrievals generated through the Goddard PROFiling (GPROF) scheme: this independence is advantageous when generating products across time scales from near real-time (inaccessibility to model data) to climatological scales (circumventing trends in model data).The algorithm is designed to generate instantaneous estimates of precipitation at a constant resolution (regardless of scan position), for all scan positions and scan lines. In addition to the actual precipitation estimate, an assessment of the error is made, and a measure of the ‘fit’ of the observations to the database provided. A quality flag is also provided, with any bad data generating a ‘missing flag’ in the retrieval.

The "CLIM" products differ from their "regular" counterparts (without the "CLIM" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the "CLIM" output. The Precipitation Retrieval and Profiling Scheme (PRPS)is designed to provide a best estimate of precipitation based upon matched SAPHIR-DPR observations. This fulfils in part the essence of GPM (and its predecessor, TRMM) in which the core observatory acts as a calibrator of precipitation retrievals for the international constellation of passive microwave instruments. In doing so the retrievals from the partner constellation sensors are able to provide greater temporal sampling and great spatial coverage than is possible from the DPR instrument alone. However, the limitations of the DPR instrument are transferred through the retrieval scheme to the resulting precipitation products.Fundamental to the design of the PRPS is the independence from any dynamic ancillary data sets: the retrieval is based solely upon the satellite radiances, a static a priori radiance-rainrate database (and index), and (static) topographical data. Critically, the technique is independent of any model information, unlike the retrievals generated through the Goddard PROFiling (GPROF) scheme: this independence is advantageous when generating products across time scales from near real-time (inaccessibility to model data) to climatological scales (circumventing trends in model data).The algorithm is designed to generate instantaneous estimates of precipitation at a constant resolution (regardless of scan position), for all scan positions and scan lines. In addition to the actual precipitation estimate, an assessment of the error is made, and a measure of the ‘fit’ of the observations to the database provided. A quality flag is also provided, with any bad data generating a ‘missing flag’ in the retrieval.

Introduction The dataset captures yearly generation profiles for different generation technology types, used by UK Power Networks to run export curtailment assessment studies.

UK Power Networks has been running curtailment studies since 2014 in the three licence areas and have been using standards technology specific profiles to model the accepted not yet connected generation capacity.

Generation specific profile include the following generation types: solar photovoltaic, wind, battery and non-variable generation.

The profiles have been developed using actual generation data from connected sites within UK Power Networks licence areas falling into each of the generation categories. The output is a yearly profile with half hourly granularity.

The values are expressed as load factors (percentages) i.e., at each half hour the value can range from 0% to 100% of the maximum export capacity.

The profiles are revised on a regular basis to ensure that they represent as closely as possible the operational behaviour of unconnected sites. The following change have taken place since UK Power Networks has started issuing curtailment reports:

The solar profile was updated in 2019/2020 and capacity factor was increased from 13.2% to 14.2%; The electricity storage profiles were updated in April 2024. new profiles available:“Storage_export_enhanced” and “Storage_import_enhanced”; Gas profiles were updated in September 2024 differentiating between small and large gas generators.

Methodological Approach This section outlines the methodology for generating annual half-hourly demand profiles.

Connected metered generators falling in each of the categories have been used to create the representative profiles. Historical data from each of these connected generation sites are retrieved from UK Power Networks’ Remote Terminal Unit (RTU) and consist of annual half-hourly meter readings. The profiles are averages of power/capacity at every time period i.e.

Paverage,t = average(P1,tc1 + P2,tc2 + … + Pn,tcn)

where

t is time, 30 minutes resolution for one year; P is the export in MW from sites 1, 2, ..., n; and c is capacity of site 1, 2, ..., n.

If there was bad/missing data then PVSYST and local meteorological data were used.

Electricity Storage profiles

For export/generation studies, storage is modelled as constantly exporting with a varying profile throughout the day, whereas for demand/import studies, storage is modelled as constantly importing with a varying import profile throughout the day. This represent a conservative view, which is used due to the unpredictable pattern of Electricity Storage sites

The solar and storage combined profile is calculated as the maximum of the storage and solar profiles during each 30-minute timestamp.

Storage profiles are detailed in our design standard Engineering Design Standard (EDS 08-5010).

Storage profiles were updated in April 2024 based following a piece of work delivered by Regen to UK Power Networks on the operational behaviour of battery storage, with the purpose to model reflect battery storage more realistically in curtailment studies . the revised profile are “Storage_export_enhanced” and “Storage_import_enhanced”. Curtailment reports issued prior to 22 April 2024 were produced using the legacy storage profiles (“Storage”).

Gas Profiles Gas profiles were updated in September 2024 to provide a more representative view on how gas generator operate and to enable more representative curtailment results. The legacy profile used to model unconnected gas generators until September 2024 was the “non-variable” profiles. From September 2024, unconnected gas generators are modelled using new “Gas_large” and “Gas_small” profile. The two profiles are meant to capture the differences between smaller genset, quickly ramping up from 0 to maximum capacity, and larger gas generators that rather have a more stable behaviour. The profile have been created with the same equation described above, taking a percentile between 95 and 98, rather than the average.

Quality Control Statement

Quality Control Measures include: Manual review and correct of data inconsistencies Use of additional verification steps to ensure accuracy in the methodology

Assurance Statement The Open Data Team and DSO Data Science worked together to ensure data accuracy and consistency.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

At each survey site, a fine-scale acoustic grid was conducted at a speed of 8 kn. Survey lines were adjusted for the direction of the sea state. At a few sites, the survey grid was run multiple times, either during the day and then the night, or separated by several days or weeks. A total of thirteen fine scale acoustic surveys (FSAS) were conducted on the AR040 cruise. Each FSAS was composed of 3 to 9 approximately 10km long transects, running across an approximately 10km2 area centered on the lander at seven of the ADEON sites. All FSAS data from the AR040 cruise have been processed using Echoview software, as described below.

Data for all frequencies were collected using an EK80 echosounder system. For each site (VAC, HAT, etc.), water column sound speed, temperature, and salinity were edited to reflect the mean midwater environmental parameters derived from conductivity, temperature, and depth (CTD) profile data.

Transducer depth was set to 3.96 m (13 feet) below the surface, the depth of the R/V Armstrong’s hull. A surface exclusion line was placed at 5 to 10m depth for all files and subsequently adjusted to ensure any backscatter from surface bubble intrusion were excluded. For all files, bottom-detection lines were automatically generated using the lowest-frequency data (18 kHz). These bottom lines were visually examined and edited for errors and then applied to data from all other frequencies. A bottom offset exclusion line was generated at one meter above the bottom line, to help ensure no backscatter from the seafloor was included in the water column data.

The removal of ambient, background, and self-noise was a multistep process conducted in Echoview. Once areas above the surface line and below the bottom line were excluded, time-varied gain (TVG) noise was removed using the data generator operand to virtually generate TVG noise stripes for each frequency based on the SV value at 1 m. The SV value at 1 m was determined by scrutiny of the TVG evident on passive noise files, with adjustments based on the data files. For the AR040 data, the SV values at 1 m were: -130 dB for 18kHz data, -138 dB for 38 kHz, -132 for 70 kHz, -125 dB for 120 kHz, and -121 dB for 200 kHz. This variable allowed much of the TVG noise to be subtracted from the data via a linear minus and a transient noise removal operator. Once the noise spikes were removed, a background noise removal algorithm was applied with a maximum noise threshold of -125 dB and a minimum SNR of 10, as described in De Robertis & Higginbottom (2007)⁠. A final 5x5 median filter was applied to remove any remaining background noise sources. Before removing noise for each wideband frequency, the data were converted to base Sv using a type conversion operator, so that the background noise removal algorithms could be applied.

In some files, the above background noise removal process was unable to remove certain sources of “bad data,” such as engine noise. In such cases, the noise was removed manually by defining regions of “bad data” that were then excluded from export.

The acoustic data were binned into cells of 100 m horizontally and 5 m vertically for final exportation to .csv files.This dataset has a readme file.See https://adeon.unh.edu/cruise for more information about the project

Dataset Card for Alpaca

  Dataset Summary

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

The text-davinci-003 engine to generate the instruction data instead… See the full description on the dataset page: https://huggingface.co/datasets/tatsu-lab/alpaca.

The Surface Ocean CO2 Atlas (SOCAT), an activity of the international marine carbon research community, provides access to synthesis and gridded fCO2 (fugacity of carbon dioxide) products for the surface oceans. Version 2 of SOCAT is an update of the previous release (version 1) with more data (increased from 6.3 million to 10.1 million surface water fCO2 values) and extended data coverage (from 1968-2007 to 1968-2011). The quality control criteria, while identical in both versions, have been applied more strictly in version 2 than in version 1. The SOCAT website (http://www.socat.info/) has links to quality control comments, metadata, individual data set files, and synthesis and gridded data products. Interactive online tools allow visitors to explore the richness of the data. Applications of SOCAT include process studies, quantification of the ocean carbon sink and its spatial, seasonal, year-to-year and longerterm variation, as well as initialisation or validation of ocean carbon models and coupled climate-carbon models.

This are the feature values used in the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy".

The dataset regroups feature values for every "cheap" features available in the R package flacco and are computed using 5 sampling strategies and in dimension \($d=5$\):

1. Random: the classical Mersenne-Twister algorithm;
2. Randu: a random number generator that is notoriously bad;
3. LHS: a centered Latin Hypercube Design;
4. iLHS: an improved Latin Hypercube Design;
5. Sobol: points extracted from a Sobol' low-discrepancy sequence.

The csv file features_summury_dim_5_ppsn.csv regroups 100 values for every features whereas features_summury_dim_5_ppsn_median.csv regroups for every feature the median of the 100 values.

In the folder PPSN_feature_plots are the histograms of feature values on the 24 COCO functions for 3 sampling strategies: Random, LHS and Sobol.

The Python file sampling_ppsn.py is the code used to generate the sample points from which the feature values are computed.

The file stats50_knn_dt.csv provide the raw data of median and IQR (inter quartile interval) for the heatmaps and boxplots available in the paper.

Finally, the files results_classif_knn100.csv (resp. dt) provide the accuracy of 100 classifications for every settings.

Ethical Data Management

This dataset and software tool are for reproducing the research results related to CVE-2019-1547, resulting from the manuscript "Certified Side Channels". The data was used to produce Figure 4 in the paper and is part of the remote timing attack data in Section 4.1.

Data description

The file timings.json contains a single JSON array. Each entry is a dictionary representation of one digital signature. A description of the dictionary fields follows.

hash_function: string denoting the hash function for the digital signature.

hash: the output of said hash function, i.e. hash of the message digitally signed.

order: the order of the generator.

private_key: the ECDSA private key.

public_key: the corresponding public key.

sig_r: the r component of the ECDSA signature.

sig_s: the s component of the ECDSA signature.

sig_nonce: the ground truth nonce generated during ECDSA signing.

nonce_bits: the ground truth number of bits in said nonce.

latency: the measured wall clock time (CPU clock cycles) to produce the digital signature.

Prerequisites

OpenSSL 1.1.1a, 1.1.1b, or 1.1.1.c.

sudo apt install python-ijson jq

Data setup

Extract the JSON:

tar xf timings.tar.xz

Key setup

Generate the public key (public.pem here) from the provided private key (private.pem here):

$ openssl pkey -in private.pem -pubout -out public.pem

Examine the keys if you want.

$ openssl pkey -in private.pem -text -noout $ openssl pkey -in public.pem -text -noout -pubin

Example: Verify key material

$ grep --max-count=1 'private_key' timings.json "private_key":"0x6b76cc816dce9a8ebc6ff190bcf0555310d1fb0824047f703f627f338bcf5435", $ grep --max-count=1 'public_key' timings.json "public_key":"0x04396d7ae480016df31f84f80439e320b0638e024014a5d8e14923eea76948afb25a321ccadabd8a4295a1e8823879b9b65369bd49d337086850b3c799c7352828", $ openssl pkey -in private.pem -text -noout Private-Key: (256 bit) priv: 6b:76:cc:81:6d:ce:9a:8e:bc:6f:f1:90:bc:f0:55: 53:10:d1:fb:08:24:04:7f:70:3f:62:7f:33:8b:cf: 54:35 pub: 04:39:6d:7a:e4:80:01:6d:f3:1f:84:f8:04:39:e3: 20:b0:63:8e:02:40:14:a5:d8:e1:49:23:ee:a7:69: 48:af:b2:5a:32:1c:ca:da:bd:8a:42:95:a1:e8:82: 38:79:b9:b6:53:69:bd:49:d3:37:08:68:50:b3:c7: 99:c7:35:28:28 Field Type: prime-field Prime: 00:ff:ff:ff:ff:00:00:00:01:00:00:00:00:00:00: 00:00:00:00:00:00:ff:ff:ff:ff:ff:ff:ff:ff:ff: ff:ff:ff A:
00:ff:ff:ff:ff:00:00:00:01:00:00:00:00:00:00: 00:00:00:00:00:00:ff:ff:ff:ff:ff:ff:ff:ff:ff: ff:ff:fc B:
5a:c6:35:d8:aa:3a:93:e7:b3:eb:bd:55:76:98:86: bc:65:1d:06:b0:cc:53:b0:f6:3b:ce:3c:3e:27:d2: 60:4b Generator (uncompressed): 04:6b:17:d1:f2:e1:2c:42:47:f8:bc:e6:e5:63:a4: 40:f2:77:03:7d:81:2d:eb:33:a0:f4:a1:39:45:d8: 98:c2:96:4f:e3:42:e2:fe:1a:7f:9b:8e:e7:eb:4a: 7c:0f:9e:16:2b:ce:33:57:6b:31:5e:ce:cb:b6:40: 68:37:bf:51:f5 Order: 00:ff:ff:ff:ff:00:00:00:00:ff:ff:ff:ff:ff:ff: ff:ff:bc:e6:fa:ad:a7:17:9e:84:f3:b9:ca:c2:fc: 63:25:51 Cofactor: 0 Seed: c4:9d:36:08:86:e7:04:93:6a:66:78:e1:13:9d:26: b7:81:9f:7e:90

Three things to note in the output:

The private key bytes match (private_key and priv byte strings are equal)

The public key bytes match (public_key and pub byte strings are equal)

This is an explicit parameters key, with the Cofactor parameter missing or zero, as described in the manuscript.

Example: Extract a single entry

Here we use the python script pickone.py to extract the entry at index 2 (starting from 0).

$ python2 pickone.py timings.json 2 | jq . > 2.json $ cat 2.json { "public_key": "0x04396d7ae480016df31f84f80439e320b0638e024014a5d8e14923eea76948afb25a321ccadabd8a4295a1e8823879b9b65369bd49d337086850b3c799c7352828", "private_key": "0x6b76cc816dce9a8ebc6ff190bcf0555310d1fb0824047f703f627f338bcf5435", "hash": "0xf36d0481e14869fc558b39ae4c747bc6c089a0271b23cfd92bc0b8aa7ed2c3aa", "latency": 21565213, "nonce_bits": 253, "sig_nonce": "0x1b88c7802ea000ccb21116575c38004579b55f1f9c4f81ed321896b1e1034237", "hash_function": "sha256", "sig_s": "0x8c83417891547224006723169de9745a81fa8de7176428e1cd8e6110408f45da", "sig_r": "0xf922d9ba4f65d207300cc7eaaa15564e60a2b1f208d1389057ff1a1ec52dc653", "order": "0xffffffff00000000ffffffffffffffffbce6faada7179e84f3b9cac2fc632551" }

Example: Dump hash to binary file

Extract the hash field from the target JSON and dump it as binary.

$ sed -n 's/^ "hash": "0x(.*)",$/\1/p' 2.json | xxd -r -p > 2.hash $ xxd -g1 2.hash 00000000: f3 6d 04 81 e1 48 69 fc 55 8b 39 ae 4c 74 7b c6 .m...Hi.U.9.Lt{. 00000010: c0 89 a0 27 1b 23 cf d9 2b c0 b8 aa 7e d2 c3 aa ...'.#..+...~...

Note the xxd output matches the hash byte string from the target JSON.

Example: Dump signature to DER

The hex2der.sh script takes as an argument the target JSON filename, and outputs the DER-encoded ECDSA signature to stdout by extracting the sig_r and sig_s fields from the target JSON.

$ ./hex2der.sh 2.json > 2.der $ openssl asn1parse -in 2.der -inform DER 0:d=0 hl=2 l= 70 cons: SEQUENCE
2:d=1 hl=2 l= 33 prim: INTEGER :F922D9BA4F65D207300CC7EAAA15564E60A2B1F208D1389057FF1A1EC52DC653 37:d=1 hl=2 l= 33 prim: INTEGER :8C83417891547224006723169DE9745A81FA8DE7176428E1CD8E6110408F45DA

Note the asn1parse output contains a sequence with two integers, matching the sig_r and sig_s fields from the target JSON.

Example: Verify the signature

We use pkeyutl here to verify the raw hash directly, in contrast to dgst that will only verify by recomputing the hash itself.

$ openssl pkeyutl -in 2.hash -inkey public.pem -pubin -verify -sigfile 2.der Signature Verified Successfully

Note it fails for other hashes (messages), a fundamental security property for digital signatures:

$ dd if=/dev/urandom of=bad.hash bs=1 count=32 32+0 records in 32+0 records out 32 bytes copied, 0.00129336 s, 24.7 kB/s $ openssl pkeyutl -in bad.hash -inkey public.pem -pubin -verify -sigfile 2.der Signature Verification Failure

Example: Statistics

The stats.py script shows how to extract the desired fields from the JSON. It computes the median latency over each nonce bit length.

$ python2 stats.py timings.json Len Median 238 20592060 239 20251286 240 20706144 241 20658896 242 20820100 243 20762304 244 20907332 245 20973536 246 20972244 247 21057788 248 21115419 249 21157888 250 21210560 251 21266378 252 21322146 253 21370608 254 21425454 255 21479105 256 21532532

You can verify these medians are consistent with Figure 4 in the paper.

The stats.py script can be easily modified for more advanced analysis.

Credits

Authors

Cesar Pereida García (Tampere University, Tampere, Finland)

Sohaib ul Hassan (Tampere University, Tampere, Finland)

Iaroslav Gridin (Tampere University, Tampere, Finland)

Nicola Tuveri (Tampere University, Tampere, Finland)

Alejandro Cabrera Aldaya (Tampere University, Tampere, Finland)

Billy Bob Brumley (Tampere University, Tampere, Finland)

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 804476).

License

This project is distributed under MIT license.

Abstract This study was considerated questions from epidemiology, sanitarism, climate and mathematic models for results about epidemics cases of Dengue and Chikungunya at Quixadá City-Ce, Brazil, 2016-2019. The epidemiological data was provided by the Municipal Health Secretariat. Climate data was provided by FUNCEME, INMET and INPE. Information about sanitary condition was qualitatively infer with a series of interviews were conducted with residents of the municipality along with photographic records and compiled news in online newspapers/portals. The epidemiological, climatic and social information was used as a database and rules on engine of the fuzzy statistical model in application, which aims at theoretical validation, diagnosis and prediction of arbovirosis, considering this multiplicity of variables. The variables air temperature and relative air humidity remained within the comfort zone of Aedes aegypti. The city's sanitary condition's proved to be the main factor to notification of these diseases throughout the study period, resulting in a directly proportional mathematical relationship between confirmed cases of arbovirosis and garbage accumulation. The fuzzy system applied was assertive close to 90%, which indicates that it is consistent and skilled for the objectives pursued generating diagnoses and prognoses for future cases of these arbovirosis, creating efficient public policies that fully meet population.

Data files for building: AT10 - Primary school - Bad Vöslau (Austria) Languages: German, English These files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures. The files are organised in the following folders (note that not all files are always provided):

Main data and Results, with:

Neutral data inventory. Neutral results report. Original EPC certificate. Energy Consumption Data, with:

Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results. Drawings

Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models. Other Data

Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building. Dynamic Model

Data to run a dynamic model of the building, if available. The files have been redacted to exclude confidential information.

This dataset was created as part of my bachelor's thesis, where I fine-tuned the llama3.1:8B language model for generating ABAP code using Unsloth 4-Bit QLoRA. The response data are extracted ABAP files from The Stack v2. The prompts where generated by llama3.1:8B based on these files. I don't recommend you use this dataset, it resulted in a pretty bad model.

The dataset file contains data regarding the electrical and mechanical operational actual quantities and parameters obtained and recorded by the internal inverter controller of a Skystream 3.7 small wind turbine (SWT) installed on the roof of the High Voltage Laboratory at the Institute of Energy and Environment (IEE) of the University of Sao Paulo (USP), Brazil, recorded from 2017 to 2022.

The main electrical parameters are the energy, voltages, and currents in the connection grid point and power frequency. Mechanical information can be retrieved, such as the rotation and the wind speed. The temperature, measured in some location points to the nacelle and inverter, is also recorded. Several other parameters concerning the SWT inverter operation, such as the dc voltages on its internal bus, alarms, and flags, are also presented.

The files in the dataset are named as "data_swt_iee_usp_YYYY.csv" where YYYY is the referring year. In the files, the semicolon symbol (;) is used as a column separator, while the dot symbol (.) represents the decimal separator. The first row of the CSV file corresponds to the header row to help identify data as described in the file "data_description.txt". Sampling rate one record per minute.

The first line on each year-based file represents the header table description of the data columns.

The complete and detailed information about the installation, localization,and analysis is in the article published at: https://doi.org/10.3390/wind2040037

LIST OS STATUS CODES - Skystream 3.7

Premise: Binary logic, where each status is represented by a specific bit within an integer value.

These codes can represent cumulative causes, adding values when multiple causes occur.

EXAMPLES:

Turbine status = 0 => Generating/Run

Turbine status = 1 => Low Windspeed

Turbine status = 3 => Low Windspeed and Braking

Turbine status = 9 => Low Windspeed and No Stall

Turbine status = 33 => Anemometer mode and Low Windspeed

System status = 1024 => Waiting

System status = 256 => Run

Grid status = 512 => Grid Disconect

This volume contains the archive of Lunar brightness temperature data derived from images acquired by the Clementine Long Wavelength Infrared (LWIR) camera. The LWIR camera acquired approximately 220,000 thermal-infrared images of the lunar surface that were used to generate the brightness temperature images within this archive. The procedure for generating brightness temperature values can be summarized as follows: 1) original LWIR digital numbers were converted to radiance; 2) the dark current signal was subtracted from the radiance values; 3) a flat field correction was applied; 4) bad pixels were identified and smoothed over; and 5) calibrated radiance values were converted to brightness temperatures using the Planck function.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being ‘good’ or ‘bad.’ Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to ‘bad’ quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). LONI-QC’s functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.

Introduction The dataset captures yearly load profiles for different demand types, used by UK Power Networks to run import curtailment assessment studies.

The import curtailment assessment tool has gone live across all three licence areas in September 2024, and uses the standard demand profiles in this data publication to model accepted not-yet-connected demand customers for import curtailment studies.

Demand specific profile include the following demand types: commercial, industrial, domestic, EV charging stations, bus charging depots, network rail and data centres.

The profiles have been developed using actual demand data from connected sites within UK Power Networks licence areas falling into each of the demand categories. The output is a yearly profile with half hourly granularity.

The values are expressed as load factors (percentages) i.e., at each half hour the value can range from 0% to 100% of the maximum import capacity.

Methodological Approach This section outlines the methodology for generating annual half-hourly demand profiles.

A minimum of ten connected demand sites for each of the demand types have been used to create the representative profiles.

Historical data from each of these connected demand sites are either retrieved from UK Power Networks’ Remote Terminal Unit (RTU) or through smart meter data. The historical data collected consist of annual half-hourly meter readings in the last calendar year.

A Python script was used to process the half-hourly MW data from each of the sites, which have been normalize by the peak MW values from the same site, for each timestamp, as follows:

Pt (p.u) = P1, tPmax1 + P2, tPmax2 + … + Pn, tPmaxn

where

P,t(p.u) is normalised power P is the import in MW from sites 1, 2, ..., n Pmax is max import in the last calendar year, from site 1, 2, ..., n t is time, 30 minutes resolution for one year

The final profile has been created by selecting a percentile ranging from 95 to 98%.

Quality Control Statement The dataset is primarily built upon RTU data sourced from connected customer sites within the UK Power Networks' licence areas as well as data collected from customers smart meters.

For the RTU data, UK Power Networks' Ops Telecoms team continuously monitors the performance of RTUs to ensure that the data they provide is both accurate and reliable. RTUs are equipped to store data during communication outages and transmit it once the connection is restored, minimizing the risk of data gaps. An alarm system alerts the team to any issues with RTUs, ensuring rapid response and repair to maintain data integrity.

The smart meter data that is used to support certain demand profiles, such as domestic and smaller commercial profiles, is sourced from external providers. While UK Power Networks does not control the quality of this data directly, these data have been incorporated to our models with careful validation and alignment.

Where MW was not available, data conversions were performed to standardize all units to MW. Any missing or bad data has been addressed though robust data cleaning methods, such as forward filling.

The final profiles have been validated by ensuring that the profile aligned with expected operational patterns.

Assurance Statement The dataset is generated using a script developed by the Network Access team, allowing an automated conversion from historical half hourly data to a yearly profile. The profiles will be reviewed annually to assess any changes in demand patterns and determine if updates of demand specific profiles are necessary. This process ensures that the profiles remain relevant and reflective of real-world demand dynamics over time.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

Abstract

Located in rural northern Uganda, Rhino Camp is home to more than 80,000 refugees3 – mostly South Sudanese who fled since July 2016. Other Rhino Camp residents come from the Democratic Republic of Congo, Rwanda, Sudan, as well as the host Ugandan community. 74% of all heads of household are women,4 and Rhino Camp is one of a growing number of refugee settlements across nine UNHCR operations where solar street lamps are in use. Between April and June 2015 UNHCR installed some three dozen community lights in 50% of Rhino Camp’s 14 villages. As demand for community lighting far exceeded available funds, UNHCR worked with the refugee community and its partner the Danish Refugee Council to prioritize the strategic placement of lights within villages. The partners jointly selected locations where (1) refugees were prone to nighttime violence, theft or other safety risks, and (2) lights would promote constructive night-time activity.

Using a 72-question survey, researchers asked respondents what day- and night-time6 activities they and their children do, and whether they do these activities in lit or unlit locations. Researchers then asked respondents if they feared or had been victims of something bad while doing these activities. The phrase something bad is the English translation for the most commonly used expressions – in Nuer, Dinka, Bari, and Kiswahili – of being a victim of an aggressive act or encountering danger. Survey responses reveal that the bad experiences that respondents most commonly fear are sexual and physical violence, theft, verbal harassment, injury, and encounters with animals.

Geographic coverage

Rhino Camp, Uganda

Analysis unit

Individuals

Universe

Four of Rhino Camp’s 14 villages: two unlit (Katiku and Siripi) and two lit (Ocea and Odobu).

Kind of data

Sample survey data [ssd]

Sampling procedure

The Office of the Prime Minister (OPM) provided UNHCR a numbered list of the names of all heads of household in each village. To ensure a representative sample among these four villages, 15% of households across all villages was selected using a random number generator. The UNHCR research team conducted interviews in 171 households. (38% of selected households were not able to be found due to outdated registry lists, and 1% did not consent.) Among the 171 randomly selected households, researchers conducted 267 individual interviews: 86% of respondents were female and 39% were adolescents.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

A locally-recruited data entry clerk input survey data in to a database using CSPro software. Statisticians cleaned the data, exported it to spreadsheets and organized it into tables, using SAS and R data analysis software. The tables displayed frequency and percentage values for responses to each survey question, and statisticians created additional tables to disaggregate data by gender, village and age. Using Google Earth software and GPS data, the UNHCR research team created a map that calculated the distance of each respondent’s home to each light in their village. Two epidemiologists supporting the assessment used the statistical program R to conduct hypothesis tests8 to determine if people living closer to lights are more likely, compared to those living farther, to 1) walk to lit areas at night and 2) feel safe at night. The lead researcher returned to Rhino Camp in November 2016 to present preliminary data to four groups of six to ten refugees who reside in the four surveyed villages. During these sessions, members of the research team showed participants the survey and explained the purpose of the research. The team also presented and described tables of survey data on the locations where respondents were most and least afraid at night.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Evergreen-Poolowanna Aquitard and Equivalents - Thickness and Extent data sets, are part of a set that represents the hydrostratigraphic units of the Great Artesian Basin, which include five major aquifers, four intervening aquitards, and the Cenozoic cover to the GAB.

There are five layers in the Evergreen-Poolowanna Aquitard and Equivalents map data

A: Formation Extent

B: Outcrop extent

C: Isopach Raster

D: Isopach Contours

E: Data Point Locations

The datasets have been derived from the lithostratigraphic intercepts in drillhole data from petroleum exploration wells, water bores, and stratigraphic wells. Seismic correlation and assessment of hydrogeological character based on electrofacies have not been used. The working datasest for this study has been derived primarily from the following databases:

* PEPS-SA (Petroleum Exploration and Production System - South Australia) (Department of Primary Industries and Regions SA, 2011)

* WaterConnect Groundwater database (Govt. of SA, 2011)

* QPED (Queensland Petroleum exploration database) (Geological Survey of Queensland, 2010).

* GABLOG (Great Artesian Basin Well Log Dataset) (Habermehl, 2001)

* Additional supplementary information was derived from published reports listed in the following section.

Interpretations by O'Brien & Wells, (1994); and O'Brien 2011 were used in generating the isopach data (thickness surface and contours) along the boundary of the Surat and Clarence-Moreton Basins.

This is a regional interpretation for mapping at approximately 1:1 000 000 to produce a broad scale overview, and examination of small areas by collecting extra data is most likely to produce results that differ from this regional interpretation.

Associated report reference:

Ransley, T., Radke, B., Feitz, A., Kellett, J., Owens, R., Bell, J. and Stewart, G., 2014. Hydrogeological Atlas the Great Artesian Basin. Geoscience Australia. Canberra. [available from www.ga.gov.au using catalogue number 79790]

REFERENCES:

References - main data sources

* Department of Primary Industries and Regions SA (2011). Petroleum Exploration and Production System - South Australia (PEPS-SA). Version 2011-06-15. Retrieved from http://www.pir.sa.gov.au/petroleum/access_to_data/peps-sa_database

* Geological Survey of Queensland (2010). Queensland Petroleum Exploration Data (QPED) database. Retrieved 25 September 2011, from http://mines.industry.qld.gov.au/geoscience/geoscience-wireline-log-data.htm.

* Geoscience Australia, 2013. Mesozoic Geology of the Carpentaria and Laura Basins (dataset). Scale 1:6000000. Geoscience Australia, Canberra. [available from www.ga.gov.au using catalogue number 75840]

* Gibson, D. L., B. S. Powell & Smart, J. (1974). Shallow stratigraphic drilling, northern Cape York Peninsula, 1973. Record 1974/76. Australia, Bureau of Mineral Resources.

* Govt. of South Australia (2011). WaterConnect Groundwater database [available at https://www.waterconnect.sa.gov.au].

* Habermehl, M. A. and J. E. Lau (1997). Hydrogeology of the Great Artesian Basin Australia (Map at scale 1:2,500,000). Canberra, Australian Geological Survey Organisation.

* O'Brien, P. E. (2011). The eastern edge of the Great Artesian Basin: relationships between the Surat and Clarence-Moreton basins. Internal report. Canberra, Geoscience Australia.

* Wells, A.T. , O'Brien, P.E. 1994 Lithostratigraphic framework of the Clarence-Moreton Basin IN Wells, A.T. and O'Brien, P.E. (eds.) "Geology and Petroleum Potential of the Clarence-Moreton Basin, New South Wales and Queensland" Australian Geological Survey Organisation. Bulletin 241 p4-47

References - Seismic Surveys

* none

References - Well Completion Reports and drilling logs

* none

Dataset History

This dataset and associated metadata can be obtained from www.ga.gov.au, using catalogue number 81683.

SOURCE DATA:

Data was obtained from a variety of sources, as listed below:

1. WaterConnect Groundwater database (Govt. of SA, 2011)

2. Great Artesian Basin Well Log Dataset (GABLOG) (Habermehl, M. A., 2001).

3. Petroleum Exploration and Production System - South Australia (PEPS-SA) (Department of Primary Industries and Regions SA, 2011).

4. Queensland Petroleum Exploration Database (QPED) (Geological Survey of Queensland, 2010).

5. Well completion and drill log reports (see references in abstract)

6. Other reports (see references in abstract)

7. Seismic surveys and associated reports (see seismic references section in abstract)

METHOD:

Formation Extent

Extents were based on drill hole data (listed above).

Extent lines were adjusted to envelop all drill hole intercepts of the Hydrostratigraphic unit. This produced some varied and irregular shapes, some patchy regions, and required some interpretation to establish the most likely extent boundary.

Outcrop Extent

Outcrop extents were sourced and extracted from Hydrogeology of the Great Artesian Basin Australia (Habermehl & Lau, 1997) for the Eromanga and Surat sub-basins. For the Carpentaria Basin, Mesozoic Geology of the Carpentaria and Laura Basins (Geoscience Australia, 2013) was used.

Isopach Raster

Source point thickness values calculated from drillhole intercepts by using the depth to top and bottom values of formations within the drillhole database attributes, and adding them together to form the isopach values for each data point across the whole aquifer/aquitard.

These values were extrapolated using the ESRI ANUDEM Topo-To-Raster surface modeller tool to create grid surface. Zero thickness constraints were applied at the known extent of the aquifer/aquitard, except in cases where the formation extends beyond the GAB boundary (for example the Precipice formation on the eastern side of the GAB, where the formation is quite thick and is exposed as a cliff). In these cases, constraints were not applied and the software was allowed to model a thickness right up to the GAB boundary. Resulting grids were modified using the ESRI Grid Calculator to set the minimum thickness to 0, and clipped to the aquifer/aquitard extent.

Isopach Contours

Isopach contours were calculated from the Evergreen-Poolowanna Aquitard and equivalents thickness grid using the ESRI Contour Tool. These were calculated at 50m intervals. In most cases the zero contour lines generated by the tool were replaced by the extent of the aquifer due to the erratic nature of the generated lines. In cases where the aquifer/aquitard is thick at the extent, the zero isoline is outside the extent and is not mapped in that area. Isopachs were clipped to the aquifer/aquitard extent.

Data Point Locations

Data Point Locations have been derived from the bore hole data collected for this project. Only the location has been included.

SOFTWARE:

All modifications/edits and geoprocessing were performed using ESRI ArcGIS 10 software.

QAQC:

Data sets were searched for errors such as negative thickness, missing data, incorrectly calculated thickness, aquifers/aquitards with missing formations, and false XY data.

The data was given a second Q&A after the thickness grids had been calculated. This involve plotting the points and the thickness grid and looking carefully for bad values. Sometimes a false outlier value would cause a 'bullseye' effect on the grid. To check the veracity, nearby data would be compared, and if necessary the original data would be searched check the value. Some petroleum fields would have wildcat picks at certain bore holes and these were compared with nearby boreholes and adjusted or deleted.

Additionally, if whole subregions had suspect values the data was check to ensure the relevant data had all been included. Finally, data sets were also checked to ensure the bore whole data recorded the full thickness of the Aquifer. In many cases water bores only go down until a suitable water source is found and often will not penetrate the whole aquifer. This data was considered on a case by case basis, in areas where plenty of suitable data was available they were removed, and in areas of sparse borehole data they were included to establish the occurrence of the formation albeit as a minimum thickness value.

Data has undergone a QAQC verification process in order to capture and repair attribute and geometric errors.

Dataset Citation

Geoscience Australia (2015) GABATLAS - Evergreen-Poolowanna Aquitard and Equivalents - Thickness and Extent. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/b9c0d451-e7f0-4810-95eb-51fa6d9f552b.