The global precipitation time series provides time series charts showing observations of daily precipitation as well as accumulated precipitation compared to normal accumulated amounts for various stations around the world. These charts are created for different scales of time (30, 90, 365 days). Each station has a graphic that contains two charts. The first chart in the graphic is a time series in the format of a line graph, representing accumulated precipitation for each day in the time series compared to the accumulated normal amount of precipitation. The second chart is a bar graph displaying actual daily precipitation. The total accumulation and surplus or deficit amounts are displayed as text on the charts representing the entire time scale, in both inches and millimeters. The graphics are updated daily and the graphics reflect the updated observations and accumulated precipitation amounts including the latest daily data available. The available graphics are rotated, meaning that only the most recently created graphics are available. Previously made graphics are not archived.

The global temperature time series provides time series charts using station based observations of daily temperature. These charts provide information about the observations compared to the derived daily normal temperature for various time scales (30, 90, 365 days). Each station has a graphic that contains three charts. The first chart in the graphic is a time series in the format of a line graph, representing the daily average temperatures compared to the expected daily normal temperatures. The second chart is a bar graph displaying daily departures from normal, including a line depicting the mean departure for the period. The third chart is a time series of the observed daily maximum and minimum temperatures. The graphics are updated daily and the graphics reflect the updated observations including the latest daily data available. The available graphics are rotated, meaning that only the most recently created graphics are available. Previously made graphics are not archived.

The Toledo Adolescent Relationships Study (TARS) Series explores the relationship qualities and the subjective meanings that motivate adolescent behavior. More specifically, this series seeks to examine the nature and meaning of adolescent relationship experiences (e.g. with family, peers, and dating partners) in an effort to discover how experiences associated with age, gender, race, and ethnicity influence the meaning of dating relationships. The series further investigates the relative impact of dating partners and peers on sexual behavior and contraceptive practices, as well as involvement in other problem behaviors that can contribute independently to sexual risk-taking. The longitudinal design of the Toledo Adolescent Relationships Study (TARS) includes a schedule of follow-up interviews occurring one, three, and five years after the initial interview. Four prior waves of data have been collected (2001, 2002, 2004, and 2006). Data were collected from adolescent respondents through structured in-home interviews utilizing laptop computers. In addition, the fifth wave, conducted in 2011 when the participants were young adults, builds on prior waves by adding quantitative and qualitative assessments of intimate partner violence (IPV). Wave 1 and Wave 2 are available through DSDR and can be applied for together. Wave 5 is available through NACJD and must be applied for separately. Please see the study homepages for more details on applying for restricted-use data access.

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. Here, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also briefly discuss results on synthetic and real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods.

People regularly get information about the political world in visual form, such as graphs of past economic growth, nonverbal cues from politicians, or projections of future climate change. Visual characteristics affect people’s preferences, but it is difficult to measure the extent of this effect precisely and concisely in surveys. We present a new adaptive design that measures the impact of visual characteristics on people’s preferences: The plot staircase. We apply it to graphs of time series data, identifying the effect of the slope of a sequence on evaluations of the sequence. The plot staircase replicates the existing finding that people have a strong preference for increasing trends. Using fewer survey questions than past approaches, it measures at the individual level how much overall welfare a survey respondent is willing to sacrifice for an increasing trend. We demonstrate the flexibility of the plot staircase across domains (economic growth, jobs creation, and the COVID-19 vaccine rollout) and across sequence characteristics. Survey measurement is more difficult for concepts that cannot be represented textually or numerically; our method enables researchers to measure preferences for graphical properties not reducible to the individual pieces of information.

• Name: GoiEner smart meters data
• Summary: The dataset contains hourly time series of electricity consumption (kWh) provided by the Spanish electricity retailer GoiEner. The time series are arranged in four compressed files:
  • raw.tzst, contains raw time series of all GoiEner clients (any date, any length, may have missing samples).
  • imp-pre.tzst, contains processed time series (imputation of missing samples), longer than one year, collected before March 1, 2020.
  • imp-in.tzst, contains processed time series (imputation of missing samples), longer than one year, collected between March 1, 2020 and May 30, 2021.
  • imp-post.tzst, contains processed time series (imputation of missing samples), longer than one year, collected after May 30, 2020.
  • metadata.csv, contains relevant information for each time series.
• License: CC-BY-SA
• Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.
• Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European Commission (EC). EASME or the EC are not responsible for any use that may be made of the information contained therein.
• Collection Date: From November 2, 2014 to June 8, 2022.
• Publication Date: December 1, 2022.
• DOI: 10.5281/zenodo.7362094
• Other repositories: None.
• Author: GoiEner, University of Deusto.
• Objective of collection: This dataset was originally used to establish a methodology for clustering households according to their electricity consumption.
• Description: The meaning of each column is described next for each file.
  • raw.tzst: (no column names provided)
    • timestamp;
    • electricity consumption in kWh.
  • imp-pre.tzst, imp-in.tzst, imp-post.tzst:
    • “timestamp”: timestamp;
    • “kWh”: electricity consumption in kWh;
    • “imputed”: binary value indicating whether the row has been obtained by imputation.
  • metadata.csv:
    • “user”: 64-character identifying a user;
    • “start_date”: initial timestamp of the time series;
    • “end_date”: final timestamp of the time series;
    • “length_days”: number of days elapsed between the initial and the final timestamps;
    • “length_years”: number of years elapsed between the initial and the final timestamps;
    • “potential_samples”: number of samples that should be between the initial and the final timestamps of the time series if there were no missing values;
    • “actual_samples”: number of actual samples of the time series;
    • “missing_samples_abs”: number of potential samples minus actual samples;
    • “missing_samples_pct”: potential samples minus actual samples as a percentage;
    • “contract_start_date”: contract start date; “contract_end_date”: contract end date;
    • “contracted_tariff”: type of tariff contracted (2.X: households and SMEs, 3.X: SMEs with high consumption, 6.X: industries, large commercial areas, and farms);
    • “self_consumption_type”: the type of self-consumption to which the users are subscribed;
    • “p1”, “p2”, “p3”, “p4”, “p5”, “p6”: contracted power (in kW) for each of the six time slots;
    • “province”: province where the user is located;
    • “municipality”: municipality where the user is located (municipalities below 50.000 inhabitants have been removed);
    • “zip_code”: post code (post codes of municipalities below 50.000 inhabitants have been removed);
    • “cnae”: CNAE (Clasificación Nacional de Actividades Económicas) code for economic activity classification.
• 5 star: ⭐⭐⭐
• Preprocessing steps: Data cleaning (imputation of missing values using the Last Observation Carried Forward algorithm using weekly seasons); data integration (combination of multiple SIMEL files, i.e. the data sources); data transformation (anonymization, unit conversion, metadata generation).
• Reuse: This dataset is related to datasets:
  • "A database of features extracted from different electricity load profiles datasets" (DOI 10.5281/zenodo.7382818), where time series feature extraction has been performed.
  • "Measuring the flexibility achieved by a change of tariff" (DOI 10.5281/zenodo.7382924), where the metadata has been extended to include the results of a socio-economic characterization and the answers to a survey about barriers to adapt to a change of tariff.
• Update policy: There might be a single update in mid-2023.
• Ethics and legal aspects: The data provided by GoiEner contained values of the CUPS (Meter Point Administration Number), which are personal data. A pre-processing step has been carried out to replace the CUPS by random 64-character hashes.
• Technical aspects:
  • raw.tzst contains a 15.1 GB folder with 25,559 CSV files;
  • imp-pre.tzst contains a 6.28 GB folder with 12,149 CSV files;
  • imp-in.tzst contains a 4.36 GB folder with 15.562 CSV files; and
  • imp-post.tzst contains a 4.01 GB folder with 17.519 CSV files.
• Other: None.

Ratings are used for a variety of reasons in water-resources investigations. The simplest rating relates discharge to the stage of the river. From a pure hydrodynamics perspective, all rivers and streams have some form of hysteresis in the relation between stage and discharge because of unsteady flow as a flood wave passes. Simple ratings are unable to represent hysteresis in a stage/discharge relation. A dynamic rating method is capable of capturing hysteresis owing to the variable energy slope caused by unsteady momentum and pressure. Using some simplifying assumptions, Fread (1973) developed what was termed a “dynamic loop” rating method to compute discharge from a time series of stage at a single streamgage for channels with compact geometry (no flood plain). Dynamic loop has specific meaning as it refers to a rating method that accounts for the variable energy slope “associated with the dynamic inertia and pressure forces of the unsteady flood discharge “ (Fread, 1975) as opposed to rating loops imposed by alluvial bedform dynamics or scour and fill processes. A dynamic rating method developed to compute discharge from stage for compact channel geometry, referred to as DYNMOD, was previously developed through a simplification of the one-dimensional Saint-Venant equations. A dynamic rating method, which accommodates compound and compact channel geometry, referred to as DYNPOUND, has been developed through a similar simplification as a part of this study. The DYNMOD and DYNPOUND methods were implemented in the Python programming language. Discharge time series computed with the dynamic rating method implementations were then compared to simulated discharge time series and discrete discharge measurements made at U.S. Geological Survey streamgage sites. References: Fread, D.L., 1973, A dynamic model of stage-discharge relations affected by changing discharge, National Oceanic and Atmospheric Administration NWS Hydro-16, November 1973, Silver Spring, MD, 38 p. Fread, D.L., 1975, Computation of stage-discharge relationships affected by unsteady flow, Water Resources Bulletin, American Water Resources Association, pp 213-228.

On August 25th, 2022, Metro Council Passed Open Data Ordinance; previously open data reports were published on Mayor Fischer's Executive Order, You can find here both the Open Data Ordinance, 2022 (PDF) and the Mayor's Open Data Executive Order, 2013 Open Data Annual ReportsPage 6 of the Open Data Ordinance, Within one year of the effective date of this Ordinance, and thereafter no later than September1 of each year, the Open Data Management Team shall submit to the Mayor and Metro Council an annual Open Data Report.The Open Data Management team (also known as the Data Governance Team is currently led by the city's Data Officer Andrew McKinney in the Office of Civic Innovation and Technology. Previously, it was led by the former Data Officer, Michael Schnuerle and prior to that by Director of IT.Open Data Ordinance O-243-22 TextLouisville Metro GovernmentLegislation TextFile #: O-243-22, Version: 3ORDINANCE NO._, SERIES 2022AN ORDINANCE CREATING A NEW CHAPTER OF THE LOUISVILLE/JEFFERSONCOUNTY METRO CODE OF ORDINANCES CREATING AN OPEN DATA POLICYAND REVIEW. (AMENDMENT BY SUBSTITUTION)(AS AMENDED).SPONSORED BY: COUNCIL MEMBERS ARTHUR, WINKLER, CHAMBERS ARMSTRONG,PIAGENTINI, DORSEY, AND PRESIDENT JAMESWHEREAS, Metro Government is the catalyst for creating a world-class city that provides itscitizens with safe and vibrant neighborhoods, great jobs, a strong system of education and innovationand a high quality of life;WHEREAS, it should be easy to do business with Metro Government. Online governmentinteractions mean more convenient services for citizens and businesses and online governmentinteractions improve the cost effectiveness and accuracy of government operations;WHEREAS, an open government also makes certain that every aspect of the builtenvironment also has reliable digital descriptions available to citizens and entrepreneurs for deepengagement mediated by smart devices;WHEREAS, every citizen has the right to prompt, efficient service from Metro Government;WHEREAS, the adoption of open standards improves transparency, access to publicinformation and improved coordination and efficiencies among Departments and partnerorganizations across the public, non-profit and private sectors;WHEREAS, by publishing structured standardized data in machine readable formats, MetroGovernment seeks to encourage the local technology community to develop software applicationsand tools to display, organize, analyze, and share public record data in new and innovative ways;WHEREAS, Metro Government’s ability to review data and datasets will facilitate a betterUnderstanding of the obstacles the city faces with regard to equity;WHEREAS, Metro Government’s understanding of inequities, through data and datasets, willassist in creating better policies to tackle inequities in the city;WHEREAS, through this Ordinance, Metro Government desires to maintain its continuousimprovement in open data and transparency that it initiated via Mayoral Executive Order No. 1,Series 2013;WHEREAS, Metro Government’s open data work has repeatedly been recognized asevidenced by its achieving What Works Cities Silver (2018), Gold (2019), and Platinum (2020)certifications. What Works Cities recognizes and celebrates local governments for their exceptionaluse of data to inform policy and funding decisions, improve services, create operational efficiencies,and engage residents. The Certification program assesses cities on their data-driven decisionmakingpractices, such as whether they are using data to set goals and track progress, allocatefunding, evaluate the effectiveness of programs, and achieve desired outcomes. These datainformedstrategies enable Certified Cities to be more resilient, respond in crisis situations, increaseeconomic mobility, protect public health, and increase resident satisfaction; andWHEREAS, in commitment to the spirit of Open Government, Metro Government will considerpublic information to be open by default and will proactively publish data and data containinginformation, consistent with the Kentucky Open Meetings and Open Records Act.NOW, THEREFORE, BE IT ORDAINED BY THE COUNCIL OF THELOUISVILLE/JEFFERSON COUNTY METRO GOVERNMENT AS FOLLOWS:SECTION I: A new chapter of the Louisville Metro Code of Ordinances (“LMCO”) mandatingan Open Data Policy and review process is hereby created as follows:§ XXX.01 DEFINITIONS. For the purpose of this Chapter, the following definitions shall apply unlessthe context clearly indicates or requires a different meaning.OPEN DATA. Any public record as defined by the Kentucky Open Records Act, which could bemade available online using Open Format data, as well as best practice Open Data structures andformats when possible, that is not Protected Information or Sensitive Information, with no legalrestrictions on use or reuse. Open Data is not information that is treated as exempt under KRS61.878 by Metro Government.OPEN DATA REPORT. The annual report of the Open Data Management Team, which shall (i)summarize and comment on the state of Open Data availability in Metro Government Departmentsfrom the previous year, including, but not limited to, the progress toward achieving the goals of MetroGovernment’s Open Data portal, an assessment of the current scope of compliance, a list of datasetscurrently available on the Open Data portal and a description and publication timeline for datasetsenvisioned to be published on the portal in the following year; and (ii) provide a plan for the next yearto improve online public access to Open Data and maintain data quality.OPEN DATA MANAGEMENT TEAM. A group consisting of representatives from each Departmentwithin Metro Government and chaired by the Data Officer who is responsible for coordinatingimplementation of an Open Data Policy and creating the Open Data Report.DATA COORDINATORS. The members of an Open Data Management Team facilitated by theData Officer and the Office of Civic Innovation and Technology.DEPARTMENT. Any Metro Government department, office, administrative unit, commission, board,advisory committee, or other division of Metro Government.DATA OFFICER. The staff person designated by the city to coordinate and implement the city’sopen data program and policy.DATA. The statistical, factual, quantitative or qualitative information that is maintained or created byor on behalf of Metro Government.DATASET. A named collection of related records, with the collection containing data organized orformatted in a specific or prescribed way.METADATA. Contextual information that makes the Open Data easier to understand and use.OPEN DATA PORTAL. The internet site established and maintained by or on behalf of MetroGovernment located at https://data.louisvilleky.gov/ or its successor website.OPEN FORMAT. Any widely accepted, nonproprietary, searchable, platform-independent, machinereadablemethod for formatting data which permits automated processes.PROTECTED INFORMATION. Any Dataset or portion thereof to which the Department may denyaccess pursuant to any law, rule or regulation.SENSITIVE INFORMATION. Any Data which, if published on the Open Data Portal, could raiseprivacy, confidentiality or security concerns or have the potential to jeopardize public health, safety orwelfare to an extent that is greater than the potential public benefit of publishing that data.§ XXX.02 OPEN DATA PORTAL(A) The Open Data Portal shall serve as the authoritative source for Open Data provided by MetroGovernment.(B) Any Open Data made accessible on Metro Government’s Open Data Portal shall use an OpenFormat.(C) In the event a successor website is used, the Data Officer shall notify the Metro Council andshall provide notice to the public on the main city website.§ XXX.03 OPEN DATA MANAGEMENT TEAM(A) The Data Officer of Metro Government will work with the head of each Department to identify aData Coordinator in each Department. The Open Data Management Team will work to establish arobust, nationally recognized, platform that addresses digital infrastructure and Open Data.(B) The Open Data Management Team will develop an Open Data Policy that will adopt prevailingOpen Format standards for Open Data and develop agreements with regional partners to publish andmaintain Open Data that is open and freely available while respecting exemptions allowed by theKentucky Open Records Act or other federal or state law.§ XXX.04 DEPARTMENT OPEN DATA CATALOGUE(A) Each Department shall retain ownership over the Datasets they submit to the Open DataPortal. The Departments shall also be responsible for all aspects of the quality, integrity and securityPortal. The Departments shall also be responsible for all aspects of the quality, integrity and securityof the Dataset contents, including updating its Data and associated Metadata.(B) Each Department shall be responsible for creating an Open Data catalogue which shall includecomprehensive inventories of information possessed and/or managed by the Department.(C) Each Department’s Open Data catalogue will classify information holdings as currently “public”or “not yet public;” Departments will work with the Office of Civic Innovation and Technology todevelop strategies and timelines for publishing Open Data containing information in a way that iscomplete, reliable and has a high level of detail.§ XXX.05 OPEN DATA REPORT AND POLICY REVIEW(A) Within one year of the effective date of this Ordinance, and thereafter no later than September1 of each year, the Open Data Management Team shall submit to the Mayor and Metro Council anannual Open Data Report.(B) Metro Council may request a specific Department to report on any data or dataset that may bebeneficial or pertinent in implementing policy and legislation.(C) In acknowledgment that technology changes rapidly, in the future, the Open Data Policy shouldshall be reviewed annually and considered for revisions or additions that will continue to positionMetro Government as a leader on issues of

Description:

*The World Development Indicators (WDI) is a premier compilation of cross-country comparable data about development. It provides a broad range of economic, social, environmental, and governance indicators to support analysis and decision-making for development policies. The dataset includes indicators from different countries, spanning multiple decades, enabling researchers and policymakers to understand trends and progress in development goals such as poverty reduction, education, healthcare, and infrastructure.*

*The dataset is a collection of multiple CSV files providing information on global indicators, countries, and time-series data. It is structured as follows:*

1. series:
Contains metadata for various indicators, including their descriptions, definitions, and other relevant information. This file acts as a reference for understanding what each indicator represents.

2. country_series:
Establishes relationships between countries and specific indicators. It provides additional metadata, such as contextual descriptions of indicator usage for particular countries.

3. countries:
Includes detailed information about countries, such as country codes, region classifications, income levels, and other geographical or socio-economic attributes.

4. footnotes:
Provides supplementary notes and additional context for specific data points in the main dataset. These notes clarify exceptions, limitations, or other special considerations for particular entries.

5. main_data:
The core dataset containing the actual indicator values for countries across different years. This file forms the backbone of the dataset and is used for analysis.

6. series_time:
Contains time-related metadata for indicators, such as their start and end years or periods of data availability.

*This dataset is ideal for analyzing global development trends, comparing country-level statistics, and studying the relationships between different socio-economic indicators over time.*

Columns and Examples:

Series Code:

Description: Unique code identifying the data series.

Example: AG.LND.AGRI.K2 (Agricultural land, sq. km).

Topic:

Description: Category under which the indicator is classified.

Example: Environment: Land use.

Indicator Name:

Description: Full name describing what the indicator measures.

Example: Agricultural land (sq. km).

Short Definition:

Description: A brief explanation of the indicator (if available).

Example: Not applicable for all indicators.

Long Definition:

Description: Detailed explanation of the indicator’s meaning and methodology.

Example: "Agricultural land refers to the share of land area that is arable, under permanent crops, or under permanent pastures."

Unit of Measure:

Description: Unit in which the data is expressed.

Example: Square kilometers.

Periodicity:

Description: How frequently the data is collected or reported.

Example: Annual.

Base Period:

Description: The reference period used for comparison, if applicable.

Example: Often not specified.

Other Notes:

Description: Additional context or remarks about the data.

Example: "Data for former states are included in successor states."

Aggregation Method:

Description: Method used to combine data for groups (e.g., regions).

Example: Weighted average.

Limitations and Exceptions:

Description: Constraints or exceptions in the data.

Example: "Data may not be directly comparable across countries due to different definitions."

Notes from Original Source:

Description: Remarks provided by the data source.

Example: Not specified for all indicators.

General Comments:

Description: Broad remarks about the dataset or indicator.

Example: Not available in all cases.

Source:

Description: Organization providing the data.

Example: Food and Agriculture Organization.

Statistical Concept and Methodology:

Description: Explanation of how the data was generated.

Example: "Agricultural land is calculated based on land area classified as arable."

Development Relevance:

Description: Importance of the indicator for development.

Example: "Agricultural land availability impacts food security and rural livelihoods."

Related Source Links:

Description: URLs to related information sources (if any).

Example: Not specified.

Other Web Links:

Description: Additional web resources.

Example: Not specified.

Related Indicators:

Description: Indicators conceptually related...

This dataset is about book series. It has 1 row and is filtered where the books is The new meaning of educational change. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Graph and download economic data for Real gross domestic product per capita (A939RX0Q048SBEA) from Q1 1947 to Q2 2025 about per capita, real, GDP, and USA.

The Controlled Anomalies Time Series (CATS) Dataset consists of commands, external stimuli, and telemetry readings of a simulated complex dynamical system with 200 injected anomalies.

The CATS Dataset exhibits a set of desirable properties that make it very suitable for benchmarking Anomaly Detection Algorithms in Multivariate Time Series [1]:

Multivariate (17 variables) including sensors reading and control signals. It simulates the operational behaviour of an arbitrary complex system including:

4 Deliberate Actuations / Control Commands sent by a simulated operator / controller, for instance, commands of an operator to turn ON/OFF some equipment.

3 Environmental Stimuli / External Forces acting on the system and affecting its behaviour, for instance, the wind affecting the orientation of a large ground antenna.

10 Telemetry Readings representing the observable states of the complex system by means of sensors, for instance, a position, a temperature, a pressure, a voltage, current, humidity, velocity, acceleration, etc.

5 million timestamps. Sensors readings are at 1Hz sampling frequency.

1 million nominal observations (the first 1 million datapoints). This is suitable to start learning the "normal" behaviour.

4 million observations that include both nominal and anomalous segments. This is suitable to evaluate both semi-supervised approaches (novelty detection) as well as unsupervised approaches (outlier detection).

200 anomalous segments. One anomalous segment may contain several successive anomalous observations / timestamps. Only the last 4 million observations contain anomalous segments.

Different types of anomalies to understand what anomaly types can be detected by different approaches. The categories are available in the dataset and in the metadata.

Fine control over ground truth. As this is a simulated system with deliberate anomaly injection, the start and end time of the anomalous behaviour is known very precisely. In contrast to real world datasets, there is no risk that the ground truth contains mislabelled segments which is often the case for real data.

Suitable for root cause analysis. In addition to the anomaly category, the time series channel in which the anomaly first developed itself is recorded and made available as part of the metadata. This can be useful to evaluate the performance of algorithm to trace back anomalies to the right root cause channel.

Affected channels. In addition to the knowledge of the root cause channel in which the anomaly first developed itself, we provide information of channels possibly affected by the anomaly. This can also be useful to evaluate the explainability of anomaly detection systems which may point out to the anomalous channels (root cause and affected).

Obvious anomalies. The simulated anomalies have been designed to be "easy" to be detected for human eyes (i.e., there are very large spikes or oscillations), hence also detectable for most algorithms. It makes this synthetic dataset useful for screening tasks (i.e., to eliminate algorithms that are not capable to detect those obvious anomalies). However, during our initial experiments, the dataset turned out to be challenging enough even for state-of-the-art anomaly detection approaches, making it suitable also for regular benchmark studies.

Context provided. Some variables can only be considered anomalous in relation to other behaviours. A typical example consists of a light and switch pair. The light being either on or off is nominal, the same goes for the switch, but having the switch on and the light off shall be considered anomalous. In the CATS dataset, users can choose (or not) to use the available context, and external stimuli, to test the usefulness of the context for detecting anomalies in this simulation.

Pure signal ideal for robustness-to-noise analysis. The simulated signals are provided without noise: while this may seem unrealistic at first, it is an advantage since users of the dataset can decide to add on top of the provided series any type of noise and choose an amplitude. This makes it well suited to test how sensitive and robust detection algorithms are against various levels of noise.

No missing data. You can drop whatever data you want to assess the impact of missing values on your detector with respect to a clean baseline.

Change Log

Version 2

Metadata: we include a metadata.csv with information about:

Anomaly categories

Root cause channel (signal in which the anomaly is first visible)

Affected channel (signal in which the anomaly might propagate) through coupled system dynamics

Removal of anomaly overlaps: version 1 contained anomalies which overlapped with each other resulting in only 190 distinct anomalous segments. Now, there are no more anomaly overlaps.

Two data files: CSV and parquet for convenience.

[1] Example Benchmark of Anomaly Detection in Time Series: “Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock. Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779 - 1797, 2022. doi:10.14778/3538598.3538602”

About Solenix

Solenix is an international company providing software engineering, consulting services and software products for the space market. Solenix is a dynamic company that brings innovative technologies and concepts to the aerospace market, keeping up to date with technical advancements and actively promoting spin-in and spin-out technology activities. We combine modern solutions which complement conventional practices. We aspire to achieve maximum customer satisfaction by fostering collaboration, constructivism, and flexibility.

Identification of errors or anomalous values, collectively considered outliers, assists in exploring data or through removing outliers improves statistical analysis. In biomechanics, outlier detection methods have explored the ‘shape’ of the entire cycles, although exploring fewer points using a ‘moving-window’ may be advantageous. Hence, the aim was to develop a moving-window method for detecting trials with outliers in intra-participant time-series data. Outliers were detected through two stages for the strides (mean 38 cycles) from treadmill running. Cycles were removed in stage 1 for one-dimensional (spatial) outliers at each time point using the median absolute deviation, and in stage 2 for two-dimensional (spatial–temporal) outliers using a moving window standard deviation. Significance levels of the t-statistic were used for scaling. Fewer cycles were removed with smaller scaling and smaller window size, requiring more stringent scaling at stage 1 (mean 3.5 cycles removed for 0.0001 scaling) than at stage 2 (mean 2.6 cycles removed for 0.01 scaling with a window size of 1). Settings in the supplied Matlab code should be customised to each data set, and outliers assessed to justify whether to retain or remove those cycles. The method is effective in identifying trials with outliers in intra-participant time series data.

Index Time Series for Vanguard Short-Term Tax-Exempt Bond ETF Shares. The frequency of the observation is daily. Moving average series are also typically included. The fund invests by sampling the index, meaning that it holds a range of securities that, in the aggregate, approximates the full index in terms of key risk factors and other characteristics. It is non-diversified.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Food at Home in U.S. City Average (CUUR0000SAF11) from Jan 1947 to Jul 2025 about food, urban, consumer, CPI, housing, inflation, price index, indexes, price, and USA.

This data collection contains test Word Usage Graphs (WUGs) for English. Find a description of the data format, code to process the data and further datasets on the WUGsite.

The data is provided for testing purposes and thus contains specific data cases, which are sometimes artificially created, sometimes picked from existing data sets. The data contains the following cases:

• afternoon_nn: sampled from DWUG EN 2.0.1. 200 uses partly annotated by multiple annotators with 427 judgments. Has clear cluster structure with only one cluster, no graded change, no binary change, and medium agreement of 0.62 Krippendorff's alpha.
• arm: standard textbook example for semantic proximity (see reference below). Fully connected graph with six words uses, annotated by author.
• plane_nn: sampled from DWUG EN 2.0.1. 200 uses partly annotated by multiple annotators with 1152 judgments. Has clear cluster structure, high graded change, binary change, and high agreement of 0.82 Krippendorff's alpha.
• target: similar to arm, but with only two repeated sentences. Fully connected graph with six words uses, annotated by author. Same sentence (exactly same string) is annotated with 4, different string is annotated with 1.

Please find more information in the paper referenced below.

Version: 1.0.0, 05.05.2023.

Reference

Dominik Schlechtweg. 2023. Human and Computational Measurement of Lexical Semantic Change. PhD thesis. University of Stuttgart.

This dataset is an ATLAS.ti copy bundle that contains the analysis of 86 articles that appeared between March 2011 and March 2013 in the Dutch quality newspaper NRC Handelsblad in the weekly article series 'the last word' [Dutch: 'het laatste woord'] that were written by NRC editor Gijsbert van Es. Newspaper texts have been retrieved from LexisNexis (http://academic.lexisnexis.nl/). These articles describe the experience of the last phase of life of people who were confronted with approaching death due to cancer or other life-threatening diseases, or due to old age and age-related health losses. The analysis focuses on the meanings concerning death and dying that were expressed by these people in their last phase of life. The data-set was analysed with ATLAS.ti and contains a codebook. In the memo manager a memo is included that provides information concerning the analysed data. Culturally embedded meanings concerning death and dying have been interpreted as 'death-related cultural affordances': possibilities for perception and action in the face of death that are offered by the cultural environment. These have been grouped into three different ‘cultural niches’ (sets of mutually supporting cultural affordances) that are grounded in different mechanisms for determining meaning: a canonical niche (grounding meaning in established (religious) authority and tradition), a utilitarian niche (grounding meaning in rationality and utilitarian function) and an expressive niche (grounding meaning in authentic (and often aesthetic) self-expression. Interviews are in Dutch; Codes, analysis and metadata are in English.

Synoptic stations in Greenland have been operated with different degrees of automation over time, which has influenced the way parameters are observed and the quality of the data series. Furthermore, some stations in remote areas are unmanned, meaning that maintenance and calibration often are done at long intervals (at least a year).All stations included in the dataset are synoptic stations except seven manual precipitation stations, see below. All over the world, synoptic stations (or SYNOP-stations) follow in at least a 3-hour interval (00, 03, 06, 09, 12, 15, 18, and 21 hours UTC). Since 1996, Greenland stations (not all from 1996) started with 1-hour observations (every hour UTC). Recently some stations also started with observations every 10 minutes, but this report only includes hourly observations. Synoptic stations always follow the same guidelines. In the attached file data series overview 14-08.pdf it is indicated, which DMI Greenlandic observations are 3-hourly or 1-hourly. The data series have variable lengths and characteristics depending on the type of station, parameter, and location, along with other factors. 91 Greenlandic stations with up to 17 meteorological parameters are included in the data set.Following institutions contributes or has contributed to the dataset:DMI: Danish Meteorological Institute. MIT: Mittarfeqarfiit (Greenland Airports) before GLV: Greenland Airport Authority. SLV: Denmark Airport Authority. USAF: US Air Force. GTO: Greenland’s Technical Organization. LORAN: US Navigation system. Please notice that coordinates and elevation are provided with a varying number of decimals due to varying levels of certainty. Find more information from the following link:Publications and reportsDownload the free data from the following link:Free Weather Observation Data

This data is based on the Seshat data release in https://zenodo.org/record/6642230 and aims to dissect the time series of each NGA into culturally and institutionally continuous time series. For both continuity criteria, the central continuous time series is marked in the data (central meaning that this is the time interval during which the NGA has crossed a specified threshold between low-complexity and high-complexity societies). Details can be found in v3 of https://arxiv.org/abs/2212.00563

Temperature time series with high spatial and temporal resolutions are important for several applications. The new MODIS Land Surface Temperature (LST) collection 6 provides numerous improvements compared to collection 5. However, being remotely sensed data in the thermal range, LST shows gaps in cloud-covered areas. With a novel method [1] we fully reconstructed the daily global MODIS LST products MOD11C1 and MYD11C1 (spatial resolution: 3 arc-min, i.e. approximately 5.6 km at the equator). For this, we combined temporal and spatial interpolation, using emissivity and elevation as covariates for the spatial interpolation. Here we provide a time series of these reconstructed LST data aggregated as monthly average, minimum and maximum LST maps. [1] Metz M., Andreo V., Neteler M. (2017): A new fully gap-free time series of Land Surface Temperature from MODIS LST data. Remote Sensing, 9(12):1333. DOI: http://dx.doi.org/10.3390/rs9121333 The data available here for download are the reconstructed global MODIS LST products MOD11C1/MYD11C1 at a spatial resolution of 3 arc-min (approximately 5.6 km at the equator; see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table), aggregated to monthly data. The data are provided in GeoTIFF format. The Coordinate Reference System (CRS) is identical to the MOD11C1/MYD11C1 product as provided by NASA. In WKT as reported by GDAL: GEOGCS["Unknown datum based upon the Clarke 1866 ellipsoid", DATUM["Not specified (based on Clarke 1866 spheroid)", SPHEROID["Clarke 1866",6378206.4,294.9786982138982, AUTHORITY["EPSG","7008"]]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] Acknowledgments: We are grateful to the NASA Land Processes Distributed Active Archive Center (LP DAAC) for making the MODIS LST data available. The dataset is based on MODIS Collection V006. File name abbreviations: avg = average of daily averages min = minimum of daily minima max = maximum of daily maxima Meaning of pixel values: The pixel values are coded in degree Celsius * 100 (hence, to obtain °C divide the pixel values by 100.0).