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1,041 results for “Internet”
Salvaging the Internet Hate Machine: Using the discourse of extremist online subcultures to identify emergent extreme speech
<p>This dataset accompanies a paper submitted to the WebSci 20 conference. In this paper, we present a lexicon of 'extreme speech' that may be used to detect hate speech and extreme speech on online platforms. We outline a cross-disciplinary research protocol through which this lexicon is initially extracted from a corpus of 3,335,265 posts from 4chan's /pol/ sub-forum using a hybrid method comprising word2vec modeling and subsequent snowballing of nearest neighbours of a small initial expert seed list of extreme language. The choice of corpus is significant, as 4chan is a space of rapid language innovation and obscure extreme vernacular, complicating generalised approaches. Our lexicon detects significantly more extreme posts within a corpus from a more mainstream platform (Reddit) than another popular lexicon, Hatebase, with similar accuracy. Our lexicon and the method of its creation thus provide a contribution to the study of the toxicity of online subcultures similar to 4chan, as well as more mainstream platforms. As we demonstrate, the lexicon allows for more effective detecting of extreme speech in these spaces. This method and the lexicon have further been made available through an open-source web tool for the study of online social platforms, 4CAT. The computational methods and lexicon on offer here can thus be used by a wide academic audience, fostering interdisciplinary approaches to the study of online hate and extreme speech. </p> <p>The dataset comprises the following items:</p> <ul> <li>The 4chan corpus from which the extreme speech lexicon was generated (posts from /pol/, 1 October 2019 - 1 November 2019)</li> <li>The Reddit corpus used to verify and test the lexicon (posts from the_donald, theredpill, politics and chapotraphouse, 1 October 2019 - 1 November 2019)</li> <li>The word2vec model from which the extreme speech lexicon was generated</li> <li>The extreme speech lexicon that was generated</li> </ul>
Individuals Who Never Used the Internet
<p>Percentage of individuals who never used the internet. Based on the Eurostat dataset Internet use by individuals[tin00028] % of individuals aged 16 to 74 with various approximation and forecasting applied.<br> <br> </p> <p> </p>
Identification of emotional facial expressions in a lab and over the internet
<p>This dataset includes the data used for the analyses presented in the paper published (under the same title as this dataset) in the journal Psychology, Journal of the Higher School of Economics. </p> <p>Abstract of the publication:</p> <p>Collecting data over the internet is an approach that allows researchers to vastly expand the possible sample sizes of their studies, and enables the study of populations that may otherwise be difficult to access. However, to ensure that data collected over the internet is of the same level of quality as data collected in a lab, the comparability of internet-collected data with lab-collected data must first be assessed for individual areas of research and experimental approaches. To answer the question of whether internet data collection is suitable for experiments involving facial expressions, we conducted a deliberately difficult facial emotion-identification experiment where participants completed the same task either under supervision in our lab, or at an unsupervised location over the internet. Stimuli consisted of sad faces that participants were asked to identify as resembling either anger, fear, or disgust. Regardless of belonging to either the group tested in the lab or over the internet, participants showed highly similar response distributions, while differences between the groups were non-significant and of very low magnitude. We can therefore conclude from our findings that internet data collection is a viable method for experiments requiring the identification of emotional facial expressions, being able to produce similar results to those which can be obtained in a lab.</p>
Internet use: participating in social networks [percentage of individuals] processed Eurostat data [CEEMID indicator]
<p>The indicator '<strong>Internet use: participating in social networks (creating user profile, posting messages or other contributions to facebook, twitter, etc.) [percentage of individuals]</strong>' from the Eurostat statistical product <em>Individuals who used the internet, frequency of use and activities.</em></p> <p>- NUTS2013 regional codes are recoded to NUTS2016<br> - missing data is handled with last observation carry forward, next observation carry back, linear interpolation<br> -NUTS2 areas are imputed when only NUTS1 level data is available. <br> <br> The original dataset is available here:<br> <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en">https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse_i&lang=en</a></p> <p>More about CEEMID: <a href="http://ceemid.eu">www.ceemid.eu</a><br> Get in touch: <a href="http://danielantal.eu/#contact">danielantal.eu/#contact</a></p>
Analysis of internet usage and bioblitz frequency in the Global South
<p>This dataset was used to analyse factors contributing to the number of bioblitzes conducting in countries in the Global South.</p> <p>This is part of a review into the effectiveness of bioblitz as a method for collecting data on biodiversity.</p> <p>We modeled population and internet usage with the number of iNaturalist Bioblitzes in a country from our sample (Groom 2021). We only just looked at those global regions where citizen science has tended to have lower prevalence in the past (Africa, Asia, and Latin America, and the Caribbean) compared to other regions. We identified a total of 254 Bioblitz projects from iNaturalist in 37 countries, in Africa (30 projects in 13 countries); Asia (71 projects in 11 countries); and Latin America and the Caribbean (153 projects in 13 countries).</p> <p>We took the total population of each country from the mean of 2015-18 values in millions from <a href="https://population.un.org/wpp/Download/Standard/Population/">https://population.un.org/wpp/Download/Standard/Population/</a> (United Nations, Department of Economic and Social Affairs, Population Division (2019). <em>World Population Prospects 2019, Online Edition. Rev. 1.</em>). Internet usage was taken as the percentage of individuals using the internet in 2017 (<a href="http://data.un.org/">http://data.un.org/</a>).</p> <p>The natural log of the number of iNaturalist projects was modelled against the log of the population in millions and the internet usage using the lm package of R.</p> <pre><code>SUMMARY_DATA <- read.delim2("summary_data.tsv", row.names=1) model <- lm(log(projects) ~log(population) + internet, data=SUMMARY_DATA) summary(model) </code></pre> <table> <tbody> <tr> <td> <p>Variable</p> </td> <td> <p>Coefficient</p> </td> <td> <p>Std. Error</p> </td> <td> <p>t-Statistic</p> </td> <td> <p>Prob.</p> </td> </tr> <tr> <td> <p>log(population) in millions</p> </td> <td> <p>0.345</p> </td> <td> <p>0.0865</p> </td> <td> <p>3.99</p> </td> <td> <p>0.0003 ***</p> </td> </tr> <tr> <td> <p>internet usage as a percentage of individuals per country</p> </td> <td> <p>0.016</p> </td> <td> <p>0.0060</p> </td> <td> <p>2.62</p> </td> <td> <p>0.0130 *</p> </td> </tr> </tbody> </table> <p>Residual standard error: 0.9167 on 34 degrees of freedom</p> <p>Multiple R-squared: 0.3911, Adjusted R-squared: 0.3553 </p> <p>F-statistic: 10.92 on 2 and 34 DF, p-value: 0.0002176</p> <p>To view properties of the model to ensure it conformed to the assumptions of the model and was a good fit. Plots are included in the attached files.</p> <pre><code>par(mfrow = c(2,2)) plot(model)</code></pre> <p>To view other correlations in the data the following code can be used. The output is included in the attached files.</p> <pre><code>correlations <- cor(SUMMARY_DATA[,c(2,3,4,6,11,13,14)],method = c("spearman")) install.packages("corrplot") library("corrplot") corrplot(correlations, method="square") </code></pre> <p> </p>
Hybrid Deep Learning Techniques for Securing Bioluminescent Interfaces in Internet of Bio Nano Things
<p>The data-set presents normal and anomalous values of twelve traffic parameters, generated by <strong>Bioluminescent bio-cyber Interfacing </strong>(BBI) in the I<strong>nternet of Bio Nano Things </strong>(IoBNT) based systems.</p> <p>The traffic parameters included in the data-set represent bio-electric and electro-bio transduction unit operation of BBI incorporating normal, as well as abnormal data to train and test machine/deep learning classifiers in discriminating attack scenarios.</p> <p>The parameters considered include the following: <strong>Cumulative concentration of released molecules, Elimination rate, Michaelis-Menten constant, Kinetic constant, Forward rate constant, Catalytic reaction constant, Ligand-receptor binding constant, Concentration of ATP, Concentration of information molecules, Release rate Reverse kinetic constant,</strong> and <strong>Reverse forward rate constant.</strong></p> <p>The data set is divided into training and testing data for simplified analysis, and application.</p>
MineDojo Internet Knowledge Base (YouTube)
<p><strong>Project website:</strong> <a href="https://minedojo.org">minedojo.org</a></p> <p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2206.08853">arxiv.org/abs/2206.08853</a></p> <p><strong>GitHub:</strong> <a href="https://github.com/MineDojo/MineDojo">github.com/MineDojo/MineDojo</a></p> <p>Minecraft is among the most streamed games on YouTube. Human players have demonstrated a stunning range of creative activities and sophisticated missions that take hours to complete. We collect 730K+ narrated Minecraft videos, which add up to <strong>33 years of duration and 2.2B words</strong> in English transcripts. The time-aligned transcripts enable the agent to ground free-form natural language in video pixels and learn the semantics of diverse activities without laborious human labeling.</p> <p>There are two files in our YouTube knowledge base.</p> <ul> <li><strong>youtube_tutorial.json</strong> (tutorial videos): <p>Minecraft tutorial videos include step-by-step demonstrations and sometimes detailed verbal explanations. They also serve as a rich source of creative missions that humans find interesting. We harvest thousands of tasks from these videos in our benchmarking suite. </p> </li> <li><strong>youtube_full.json</strong> (general gameplay videos): <p>Unlike tutorials, general gameplay videos do not necessarily provide guidance on particular tasks. Instead, they capture the “in-the-wild” human experiences that are much larger in quantity, diverse in contents, and rich in learning signals.</p> </li> </ul> <p>Data Structure</p> <pre><code class="language-python">list[ { "id": str, # video id "title": str, # video title "link": str, # video link "view_count": int # number of times the video has been viewed "like_count": int # number of users who have indicated that they liked the video "duration": float # video duration in seconds "fps": float, # video FPS } ]</code></pre> <p>Check out our paper!</p> <pre><code class="language-markdown">@article{fan2022minedojo, title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge}, author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar}, year = {2022}, journal = {arXiv preprint arXiv: Arxiv-2206.08853} }</code></pre> <p> </p>
Dataset of "Comparison of Localization Methods for Internet of Things in 5G Cellular Networks: A Wide-scale Assessment"
<p>As the 3rd generation partnership project (3GPP) organization pushes out new releases,<br>positioning in heterogeneous mobile networks enables the achievement of the accuracy required<br>in the majority of industrial applications without dependence on global navigation<br>satellite systems (GNSS). This study presents the results gathered during an extensive measurement<br>campaign related to the practical applicability of localization in next-generation<br>heterogeneous networks. We present an accuracy comparison of basic timing advance (TA)<br>localization with the k-nearest neighbor (KNN), decision tree-based random forest (RF),<br>extreme gradient boosting (XGBoost), and long short-term memory (LSTM) recurrent neural<br>network. Our results demonstrate that TA cannot be considered an optimal solution<br>from the perspective of localization accuracy because the error roughly corresponds to the<br>average separation distance from the base station (BS) to the end device (ED). In addition,<br>we found that the LSTM approach is not optimal for the outdoor localization of moving<br>ED because of the combination of multiple factors, with sparse deployment being the most<br>important. The median value of the location error of the LSTM was more than 200m higher<br>than that of the TA for the self-validation dataset. However, a simple KNN regression shows<br>solid results for 5G New Radio (NR) operating in the non-standalone (NSA) mode. KNN<br>provided the most accurate results of all methods, with median error values of approximately<br>12 (k=3) and 82 (k=5) m for the self-validated and cross-validated datasets, respectively.</p>
Leaflet Engage IoT Societal Engagements with the Internet of Things
<p>Leaflet of the project Engage IoT, funded by the Fundação para a Ciência e a Tecnologia (EXPL/SOC-SOC/1375/2021), in the shape of a fortune teller.</p> <p>Website of the project: https://engageiot.wordpress.com/</p>
DOGA Video about Internet Safety
<p>This video presents a webinar developed by Doga (Turkey) and Early Years (North Ireland) about Internet Safety last 5<sup>th</sup> February 2019 within the online conversations of WYRED Project</p>
MAD (MAlicious Traffic Dataset) in home and commercial environments - Internet environment
<p>We have for the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server.</p> <p>The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases.</p> <p>events.csv.gz - Suricata / Evebox collections</p> <p>net.csv.gz - Telegraf collections from the TICK stack</p> <p>netstat.csv.gz - Telegraf collections from the TICK stack</p> <p>For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files.</p> <p>The interval between collections, non-consecutive, was from 2018-08-28 to 2019-11-14</p>
Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices
<p>This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices"</p> <p>With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule.</p> <p>For more detail about this dataset, please refer to the paper listed above.</p>
Pre-Publication Dataset: Is Remote Sensing a Better Measure of Internet Censorship than Expert Analysis? Analyzing Tradeoffs for International Donors and Advocacy Organizations
<p>These are the underlying data and do file to support the analysis in the forthcoming paper "Is Remote Sensing a Better Measure of Internet Censorship than Expert Analysis? Analyzing Tradeoffs for International Donors and Advocacy Organizations" that has been submitted to the <em>Data & Policy </em>Journal. This is an expanded and updated version of the Data For Policy conference paper "Comparing Measures of Internet Censorship: Analyzing the Tradeoffs between Expert Analysis and Remote Measurement" (10.5281/zenodo.3967398).</p>
Internet of Laboratories - Live Demonstration
<p>A video recording of a live demonstration of an experiment that has been performed during the JRC workshop "Supporting a glocal energy transition: from local energy communities to global simulation networks". It is the result of a long-term collaboration with our research partners: RWTH Aachen and the members of the ENET RT-lab in the ENSIEL consortium (Politecnico di Torino, Università di Genova, Politecnico di Bari and Università di Napoli Federico II).</p>
Japanese Expressions Dataset from Human Rights Infringement on Internet
<p>Japanese Expressions Dataset from Human Rights Infringement on Internet</p> <p>これは、言語処理学会2023において”<a href="https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/D2-2.pdf"><strong>権利侵害と不快さの間:日本語人権侵害表現データセット</strong></a>”の発表で用いた、誹謗中傷等の表現を集めたデータセットです。</p> <p>仕様は変更される可能性があります</p> <p>Release Date</p> <p>Version 0.1 2023/03/09</p> <p>Version 0.2 2023/06/01</p> <p> </p> <p>1.調査対象</p> <p>民事事件</p> <ul> <li>発信者情報開示請求事件</li> <li>損害賠償等請求事件</li> </ul> <p>インターネット上の投稿による人権侵害を争った事件よりこのデータセットは作成されています。</p> <p>Annotation Scheme</p> <p>1: Text in Dispute </p> <p>Text in Dispute is transcribed from the list of articles submitted or the facts and reasons section of the court cases.<br> This text is what the plaintiff alleges is infringing his or her rights.<br> <br> For example, we use the tag [Plaintiff] for descriptions related to plaintiffs, the tag [Defandant] for descriptions related to defandantss and [Third Party] for any mention of third parties.<br> The tag [Other] is designated for texts involving anonymized place names and any other content not covered by the preceding tags. <br> If distinguishing between tags proves challenging, we advise the use of [Other]. <br> Should a URL be present in the text, it is substituted with the [URL] tag.</p> <p>2: Context Utilized in Adjudication</p> <p>In some cases, the history of the conversation and previous postings are taken into account in determining whether or not an infringement has occurred.<br> Such text is transcribed from the fact-finding and judgment portions of the court case.<br> Contextual information that can be used for tasks other than proper understanding of the issue and accurate learning and classification.<br> Anonymization is applied in the same way as for [1: Text in Dispute].</p> <p>3: Allegedly Infringed Right and Judgement on the Infringement Allegation</p> <p>This category provides a label indicating the types of personal rights infringement complained of by the plaintiff against Text in Dispute and whether or not the infringement of that right was recognized by the trial court.<br> If there are more than two types of infringement of rights complained of by the plaintiff against one text, up to two labels are given.</p> <p>3-1a: The types of Allegedly Infringed Right 1</p> <p>Labels are assigned from the above types of personal rights, the right of reputation, the sense of honor, the right of peaceful private life, and other personal rights, from the complaint of infringement of rights by the plaintiff.</p> <p>3-1b: Judgement on the Infringement Allegation 1</p> <p>This category indicates whether the trial court granted the plaintiff's complaint against the text under consideration.<br> The annotator reads the result of the Judgement for each case from the court's judgment in the case.<br> If we find that the court has admitted the plaintiff's claim, we assign "1" label to the case concerning the infringement of personal rights.<br> If we find that the court has dismissed the plaintiff's claim, we assign "0" label to those cases that are not infringing.<br> We assign the label "UNJUDGE" to cases where we cannot find the court's Judgement. <br> This appears in cases where the plaintiff alleges multiple infringements and only one of the claims is adjudicated, or where the complaint is found to have been dismissed based on factors other than a determination of infringement by textual expression.</p> <p>3-2a & b:The types of Allegedly Infringed Right and Judgement on the Infringement Allegation 2</p> <p>If two infringements are complained of, the annotator assigns a label for the second right.</p> <p><br> 4: Index Information of Court Cases</p> <p>This section contains information about the cited court case.</p> <p>4-1: Case Number</p> <p>This is the case number of the court case and the court in which the case was argued.</p> <p>4-2: Case Name</p> <p>This is the case name of the court case and the court in which the case was argued.<br> Either a case requesting for disclosure of sender's information. or a case requesting compensation for damages is applicable.</p> <p>4-3: Bibliography</p> <p>This is the bibliography of the cited case.</p> <p>4-4: Article Number</p> <p>This is the number assigned to Text in Dispute in the court case.</p> <p>4-5: Online Platform</p> <p>If the online platform where Text in Dispute is explicitly indicated in the court case, it is stated.<br> Since the form of the text and context differs from platform to platform, considering the uniqueness of each service is an important factor to be taken into account.</p>
Performance measurements for in-depth energy analysis of security algorithms and protocols for the Internet of Things
<p>Performance dataset of cryptographic algorithms running on the following embedded devices (results in ms):</p> <p><strong>nuc </strong>The NUCLEO-L073RZ is a STM32 Nucleo-64 Development Board of STMicroelectronics. It features the STM32L073RZT6 32~MHz ARM Cortex-M0+ microcontroller with 192~KB flash memory and 20~KB RAM.<br> <strong>msp </strong>The TI SimpleLink MSP-EXP432P401R development kit uses the MSP432P401R 48~MHz ARM Cortex-M4F microcontroller with 256~KB flash and 64~KB RAM.<br> <strong>max </strong>The MAXREFDES\#100 health sensor platform features the MAX32620 96~MHz ARM Cortex-M4F microcontroller with 2~MB flash and 256~KB RAM. It has a wide range of sensors, like a human body temperature sensor and a heart rate sensor.</p> <p>The measured cryptographic operations:</p> <ul> <li><strong>The basic arithmetic operations for elliptic curve cryptography </strong>(point addition~(PA), point doubling~(PD), point multiplication~(PM), and fixed-point multiplication~(PMG))</li> <li><strong>The AES symmetric-key cipher in five modes of operations</strong> (Electronic Codebook (ECB), Cipher Block Chaining (CBC), Counter (CTR), Counter with CBC-MAC (CCM), and Galois/Counter Mode (GCM))</li> <li><strong>Hash functions </strong>(SHA256 and SHA3-256)</li> </ul> <p>The performance of all identified basic operations is measured on the three platforms. 50 time measurements are done for each basic operation using the platforms' available timer. Moreover, the AES cipher operation is an encryption on 256 Bytes of data. We have chosen a multiple of the AES block size, because, longer time periods ensure less influence of potential timing inaccuracies like an early start and late end. For the hash function, the maximum input size of the respective algorithm for one round is chosen as follows: 55~B for SHA256 and 135~B for SHA3-256. The total available internal state size is not used for SHA256 and SHA3-256, as we take into account the minimal padding or suffix that is required for the last block of input data. Note that the most optimal scenario, i.e. the maximum amount of input data to fill up the internal state completely, is used for each of the operations.</p> <p>All basic operations are implemented using software libraries and cross-compiled with the GNU Tools for ARM Embedded Processors version 6-2017-q2-update. Furthermore, the compiler is configured to optimise for size (-Os). The RELIC-toolkit library is used to implement the EC arithmetic and the SHA256 hash function. We use the SECG K-256 prime elliptic curve, BASIC;COMBA;COMBA;MONTY;MONTY;SLIDE configuration for the prime field arithmetic, and PROJC;LWNAF;COMBS;INTER}} configuration for the prime elliptic curve arithmetic. For more information on how to configure RELIC and other examples that use it, we refer to the relic-toolkit wiki. The AES ciphers are implemented using Mbed TLS and SHA3 using wolfCrypt. We use the SHA3-256 hash function as specified in FIPS PUB 202.</p>
dataset for paper Vanhaebost J, Faouzi M, Mangin P, Michaud K: New reference tables and user-friendly Internet application for predicted heart weights. Int J Legal Med 2014, 128(4):615-620.
<p>The heart weight is the most important parameter in the determination of cardiac hypertrophy. The obtained heart weight value should be compared against tables of normal weights by age, gender and body weight and height</p> <p>In the study by Vanhaebost<em> et al</em>. has been shown in the Swiss population that the heart weight increases along with the increase of the body weight, body height, BMI and body surface area (BSA). The mean heart weight is greater in men than in women at a similar body weight. The reference tables for predicted heart weights obtained from this study are presented as an user-friendly internet application (<a href="http://calc.chuv.ch/Heartweight">http://calc.chuv.ch/Heartweight</a>) enabling the comparison of heart weights observed at autopsy with the reference values.</p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Elements of Karl Popper's Philosophy as the Foundation of a Sociology of the Internet
<p>Talk at the <a href="https://www.digital-philosophy.org/" target="_blank" rel="noopener">Philosophy [in:of:for:and] Digital Knowledge Infrastructures</a> online workshop 2023 (28/09/2023).</p>
List of DNS over HTTPS resolvers on the internet
<p>The DoH Internet Servers dataset comprises a verified list of Internet servers offering DNS over HTTPS (DoH) service. The list was created by active scanning of the IPv4 address space. The scanning was done two times in April 2021 and in January 2022. Together, the list contains 1987 different IP addresses with their reverse DNS record (if available), supported DoH method, and TLS 1.3 support. </p> <p>The scanning was done in three phases:</p> <ol> <li>We scanned the IPv4 address space for opened port 443 using masscan. </li> <li>IP addresses found in the previous step were scanned for DoH support using a custom Nmap-NSE script.</li> <li>The IP addresses found in previous steps were reached by a slower python script that also validated responses, obtained domain names.</li> </ol> <p> </p> <p><strong>Scanning limitation: </strong>The main limitation of our scanning was that it could not find DoH resolvers hosted on infrastructures hosting multiple services behind a single IP address. In such cases, an SNI, or HTTP Host header, or HTTP/2 <em>:authority</em> header is needed for a successful request. Since we did not have the SNI, it was impossible to provide it. </p> <p><strong>Used Scripts:</strong></p> <p>NMap: https://github.com/cejkato2/dns-doh.nse<br> Python: https://github.com/hynekkar/DoH-Checker</p> <p> </p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.