This dataset contains data collected during a study "Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries" conducted by Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

It is being made public both to act as supplementary data for "Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries", Government Information Quarterly*, and in order for other researchers to use these data in their own work.

Methodology

The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems.

This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.

Description of the data in this data set

The file "OpenDataIndex_2013_2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:

1) Global Open Data Index (GODI) (4 editions)

2) Open Data Maturity Report (ODMR) (8 editions)

3) Open Data Inventory (ODIN) (6 editions)

4) Open Data Barometer (ODB) (5 editions)

5) Open, Useful and Re-usable data (OURdata) Index (3 editions)

6) Open Government Development Index (OGDI) (2 editions)

These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.

Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.

Format of the file.xls, .csv (for the first spreadsheet only)

Licenses or restrictionsCC-BY

For more info, see README.txt

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

The National Airspace System (NAS) is an ever changing and complex engineering system. As the Next Generation Air Transportation System (NextGen) is developed, there will be an increased emphasis on safety and operational and environmental efficiency. Current operations in the NAS are monitored using a variety of data sources, including data from flight recorders, radar track data, weather data, and other massive data collection systems. Although numerous technologies exist to monitor the frequency of known but undesirable behaviors in the NAS, there are currently few methods that can analyze the large repositories to discover new and previously unknown events in the NAS. Having a tool to discover events that have implications for safety or incidents of operational importance, increases the awareness of such scenarios in the community and helps to broaden the overall safety of the NAS, whereas only monitoring the frequency of known events can only provide mitigations for already established problems. This paper discusses a novel approach for discovering operationally significant events in the NAS that are currently not monitored and have potential safety and/or efficiency implications using radar-track data. This paper will discuss the discovery algorithm and describe in detail some flights of interest with comments from subject matter experts who are familiar with the operations in the airspace that was studied.

SOL-Experiment-Data OverviewThe SOL-Experiment-Data dataset presents the data acquired from the execution of the SOL-Experiments project. The SOL-Experiments case studies include queries from music (Band and Musical Artists) and movies (Film) domain. The experiments uses SOL-Engine framework to query DBpedia, retrieving not only the query results but also related data found by serendipity.The experiment consisted of the following steps:(1-a) Retrieving a list of 10,000 entities of selected types (Band, Musical Artists and Film) from DBpedia SPARQL endpoint.(1-b) Submitting each entity to SOL-Engine in order to locate related content based on SOL-Engine serendipity patterns set (Music Influenced Analogy, Generic Hierarchy Analogy, SeeAlso Surprizing Observation, Music Association Surprizing Observation, SameAs Surprizing Observation, Different From Inversion). Each serendipity pattern is considered alone. One last configuration is used to combine all the serendipity patterns together.(2) The related content is compared to its source entity according to a dissimilarity metric that observes the retrieved labels. This analysis is used to value unexpectedness.(3) The unexpectedness score of each serendipity pattern is computed as the average of unexpectedness of each entity processed.The data is compressed in .zip format and can be uncompressed by standard compression utilities. The dataset contains raw data (.json).The underlying data and code can be accessed through standard text edit software.Maintainability PlanThe SOL-Experiment-Data dataset is result of the following projects: SOL-Engine framework and SOL-Experiments. This way, as the cited projects evolve, the SOL-Experiment-Data dataset is going to be update in order to reflect the projects modification.Additionally, there are plans to expand the dataset to include other queries, and therefore, to retrieve different entities set. This dataset update is planned to be conducted every semester. This will enable the dataset to incrementally grow with diverse scenarios.

The purpose of data mining analysis is always to find patterns of the data using certain kind of techiques such as classification or regression. It is not always feasible to apply classification algorithms directly to dataset. Before doing any work on the data, the data has to be pre-processed and this process normally involves feature selection and dimensionality reduction. We tried to use clustering as a way to reduce the dimension of the data and create new features. Based on our project, after using clustering prior to classification, the performance has not improved much. The reason why it has not improved could be the features we selected to perform clustering are not well suited for it. Because of the nature of the data, classification tasks are going to provide more information to work with in terms of improving knowledge and overall performance metrics. From the dimensionality reduction perspective: It is different from Principle Component Analysis which guarantees finding the best linear transformation that reduces the number of dimensions with a minimum loss of information. Using clusters as a technique of reducing the data dimension will lose a lot of information since clustering techniques are based a metric of 'distance'. At high dimensions euclidean distance loses pretty much all meaning. Therefore using clustering as a "Reducing" dimensionality by mapping data points to cluster numbers is not always good since you may lose almost all the information. From the creating new features perspective: Clustering analysis creates labels based on the patterns of the data, it brings uncertainties into the data. By using clustering prior to classification, the decision on the number of clusters will highly affect the performance of the clustering, then affect the performance of classification. If the part of features we use clustering techniques on is very suited for it, it might increase the overall performance on classification. For example, if the features we use k-means on are numerical and the dimension is small, the overall classification performance may be better. We did not lock in the clustering outputs using a random_state in the effort to see if they were stable. Our assumption was that if the results vary highly from run to run which they definitely did, maybe the data just does not cluster well with the methods selected at all. Basically, the ramification we saw was that our results are not much better than random when applying clustering to the data preprocessing. Finally, it is important to ensure a feedback loop is in place to continuously collect the same data in the same format from which the models were created. This feedback loop can be used to measure the model real world effectiveness and also to continue to revise the models from time to time as things change.

Colour patterns and their visual backgrounds consist of a mosaic of patches that vary in colour, brightness, size, shape and position. Most studies of crypsis, aposematism, sexual selection, or other forms of signalling concentrate on one or two patch classes (colours), either ignoring the rest of the colour pattern, or analysing the patches separately. We summarize methods of comparing colour patterns making use of known properties of bird eyes. The methods are easily modifiable for other animal visual systems. We present a new statistical method to compare entire colour patterns rather than comparing multiple pairs of patches. Unlike previous methods, the new method detects differences in the relationships among the colours, not just differences in colours. We present tests of the method's ability to detect a variety of kinds of differences between natural colour patterns and provide suggestions for analysis.

This project is to determine horizontal and vertical movement patterns of two jellyfish species in Hood Canal, in relation to environmental variables. It is being conducted by NMFS scientists in collaboration with a NOAA Hollings Scholar; we also are making use of publicly available oceanographic data from the University of Washington. We used acoustic tags and receivers to track jellyfish movement patterns and correlated their movements with oceanographic data. This project will produce peer reviewed manuscripts. The target audience is fisheries and marine resource managers in Puget Sound and along the West Coast. This is a one-time, standalone project without a firm deadline. This data set contains acoustic telemetry data for lions mane and fried egg jellyfish.

Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response data without the need of fitting any model. In our attentional datasets, we demonstrate that data-driven methods provide descriptions of tuning curve features such as preferred stimulus direction or attentional gain modulations which are in agreement with fit-based approaches when a good fit exists. Furthermore, these methods naturally extend to the frequent cases of uncertain model selection. We show that model-free approaches can identify attentional modulation patterns, such as general alterations of the irregular shape of tuning curves, which cannot be captured by fitting stereotyped conventional models. Finally, by comparing datasets across different conditions, we demonstrate effects of attention that are cell- and even stimulus-specific. Based on these proofs-of-concept, we conclude that our data-driven methods can reliably extract relevant tuning information from neuronal recordings, including cells whose seemingly haphazard response curves defy conventional fitting approaches.

The data for a multivocal literature review on design patterns for AI-based systems.

• mlr-search-and-selection.xlsx: the results from the queried databases and search engines, the inclusion/exclusion process, and the backward and forward snowballing results
• mlr-results.xlsx: the final set of selected resources, the patterns extracted from them, and some analysis
• query-strings-google-and-google-scholar.txt: the individual terms of the search query (broken up for Google Scholar and Google Search)

The Great Isaiah Scroll (1QIsa^a) data set for writer identification

This data set is collected for the ERC project:
The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls
PI: Mladen Popović
Grant agreement ID: 640497

Project website: https://cordis.europa.eu/project/id/640497

Copyright (c) University of Groningen, 2021. All rights reserved.
Disclaimer and copyright notice for all data contained on this .tar.gz file:

1) permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.

2) provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.

3) provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.

4) the user should refer to the first public article on this data set:

Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence-based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsa^a). arXiv preprint arXiv:2010.14476.

BibTeX:

@article{popovic2020artificial,
 title={Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)},
 author={Popovi{\'c}, Mladen and Dhali, Maruf A and Schomaker, Lambert},
 journal={arXiv preprint arXiv:2010.14476},
 year={2020}
}

5) the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site for obtaining their own copy.

Organisation of the data:

The .tar.gz file contains three directories: images, features, and plots. The included 'README' file contains all the instructions.

The 'images' directory contains NetPBM images of the columns of 1QIsa^a. The NetPBM format is chosen because of its simplicity. Additionally, there is no doubt about lossy compression in the processing chain. There are two images for each of the Great Isaiah Scroll columns: one is the direct binarized output from the BiNet (arxiv.org/abs/1911.07930) system, and the other one is the manually cleaned version of the binarized output. The file names for the direct binarized output are of the format '1QIsaa_col

The 'features' directory contains feature files computed for each of the column images. There are two types of feature files: Hinge and Adjoined. They are distinguishable by their extension, for example, '1QIsaa_col15_cleaned.hinge' and '1QIsaa_col15_cleaned.adjoined'. They are also arranged in separate directories for ease of use.

The 'plots' directory contains a simple python script to perform PCA on the feature files and then visualize them in a 3D plot. The file takes the location of feature files as an input. The 'README_plot' file contains examples of how-to-run in the terminal.

Brief description:
According to ImageMagick's' identify' tool, the original images are in grayscale (.jpg) from Brill collection, in '8-bit Gray 256c'. These images pass through multiple preprocessing measures to become suitable for pattern recognition-based techniques. The first step in preprocessing is the image-binarization technique. In order to prevent any classification of the text-column images based on irrelevant background patterns, a specific binarization technique (BiNet) was applied, keeping the original ink traces intact. After performing the binarization, the images were cleaned further by removing the adjacent columns that partially appear on the target columns' images. Finally, few minor affine transformations and stretching corrections were performed in a restrictive manner. These corrections are also targeted for aligning the texts where the text lines get twisted due to the leather writing surface's degradation. Hence, the clean images are there in the directory along with the direct binarized images. No effort has been made to obtain a balanced set in any way.

Tools:
Binarization:
The BiNet tool is available for scientific use upon request (m.a.dhal(at)rug.nl)

Image Morphing:
In the original article, data augmentation was performed using image morphing. The tool is available on GitHub:
https://github.com/GrHound/imagemorph.c

Features for writer identification:
Lambert Schomaker
http://www.ai.rug.nl/~lambert/allographic-fraglet-codebooks/allographic-fraglet-codebooks.html
http://www.ai.rug.nl/~lambert/hinge/hinge-transform.html
1. L. Schomaker & M. Bulacu (2004). Automatic writer identification using connected-component contours and edge-based features of upper-case Western script. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 26(6), June 2004, pp. 787 - 798.
2. Bulacu, M. & Schomaker, L.R.B. (2007). Text-independent Writer Identification and Verification Using Textural and Allographic Features, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Special Issue - Biometrics: Progress and Directions, April, 29(4), p. 701-717.

The features (hinge, fraglets) have been combined in a single MS Windows application, GIWIS, which is available for scientific use upon request (l.r.b.schomaker(at)rug.nl)

If you have any question, please contact us:
Maruf A. Dhali

Please cite our papers if you use this data set:
1. Popović, M., Dhali, M. A., & Schomaker, L. (2020). Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsa^a). arXiv preprint arXiv:2010.14476.
2. Dhali, M. A., de Wit, J. W., & Schomaker, L. (2019). Binet: Degraded-manuscript binarization in diverse document textures and layouts using deep encoder-decoder networks. arXiv preprint arXiv:1911.07930.

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

This data set provides fine-granular statistics on trading traffic generated by six global exchanges over the course of two days in February 2019 for a set of representative feeds and recorded by the systems of vwd Vereinigte Wirtschaftsdienste GmbH (now known as Infront Financial Technology GmbH).

Please note that these numbers represent only limited market segments of the actual exchange and the measured feeds might provide different products and instrument types.

The exchanges are identified as AU = Sydney, FFM = Frankfurt am Main (GER), HK = Hong Kong (CN), Q = NASDAQ (USA), TK = Tokyo (JPN), UK = London (UK).

Please see the Zenodo page https://doi.org/10.5281/zenodo.6381970 for details on syntax etc.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Of the 14 nations included in the original study, these data cover the following ten: Brazil, Cuba, Dominican Republic, India, Israel, Nigeria, Panama, United States, West Germany, and Yugoslavia. (The data for Egypt, Japan, the Philippines, and Poland are not available through ICPSR.) In India and Israel the interviews were conducted in two waves, with different samples. Besides ascertaining the usual personal information, the study employed a "Self-Anchoring Striving Scale," an open-ended scale asking the respondent to define hopes and fears for self and the nation, to determine the two extremes of a self-defined spectrum on each of several variables. After these subjective ratings were obtained, the respondents indicated their perceptions of where they and their nations stood on a hypothetical ladder at three different points in time. Demographic variables include the respondents' age, gender, marital status, and level of education. For more information on the samples, coding, and the means of measurement, see the related publication listed below.

Data and algorithmsData and algorithms for analysis associated with manuscript. See 'readme.txt' for further detail.alldata.zip

Aim

Investigation of realised niche contraction in declining species can help us understand how and where threats are being either mediated or tolerated across landscapes. It also provides insights into species’ sensitivity to environmental change that are unable to be identified through analysis of declines in range size or abundance alone. Here, we apply the recently proposed ‘niche reduction hypothesis’ to investigate relationships between trends in niche breadth and geographic distribution of declining species.

Location

Northern Australia

Methods

We compare and contrast contemporary and historical datasets to examine the relationship between extent of occurrence (EOO) and realised niche hypervolume, and investigate changes in species’ utilisation of environmental space through time via generalised linear modelling and bootstrapping of historical values. We also use the ‘Maxent’ algorithm to create and stack contemporary and historical ecological niche models (ENMs) an...

The Global Pattern Search for alpha-level Optimisation (aGPS) is a global optimisation approach explicitly designed for efficient and user-friendly alpha-level optimisations. It can be used to improve the efficiency of fuzzy structural analyses. The efficiency of aGPS stems from its deterministic sample generation, which allows a reuse of many samples within the various alpha-level optimisations. Moreover, information gained within an alpha-level optimisation is used for all subsequent optimisations. It outperforms state-of-the-art algorithms. This means that it requires less model evaluations, and therefore, it has lower computing times. Here, the entire source code for its implementation in MATLAB is given. For further information, it is referred to "Huebler, C., & Hofmeister, B. (2021). Efficient and user-friendly alpha-level optimisation for application-orientated fuzzy structural analyses. Submitted to Engineering Structures."

These two scripts can be used to pull all of the water quality and flow data from the USGS "Current Conditions" page for a given state. You will first need to build a list of URLs using the URL script, and then you can use the second script to pull all of the data from each URL. The scraper is configured to use the maximum available date range for each variable of interest (e.g. discharge, conductivity...), meaning that file sizes can be anywhere from very small to very large. If you're finding that your data is being cut-off, you'll need to extend the "timeout" parameter within the url request call (currently set to 199 s), so that the total extent of the data may load. The USGS prefers automated data retrieval take place between 12 am - 6 am. Please adhere to these guidelines or your connection may be blocked. To facilitate friendly scraping, I've added a start time parameter to the second script. Set this variable ('then') to the time you want to start running the scraper. Another common issue is the parameter code dictionary. The USGS maintains a large list of parameters assigned to 5 digit codes. If a code is not present in the dictionary as is (i.e. dictionary 'd'), the URL will be saved to Broken_links.csv and data will not be collected. To remedy this, follow these URLs in your browser, find the new variables/codes, and then add them to the dictionary.

Tool support in software engineering often relies on relationships, regularities, patterns, or rules mined from other users’ code. Examples include approaches to bug prediction, code recommendation, and code autocompletion. Mining is typically performed on samples of code rather than the entirety of available software projects. While sampling is crucial for scaling data analysis, it might influence the generalization of the mined patterns. This paper focuses on sampling software projects filtered for specific libraries and frameworks, and on mining patterns that connect different libraries. We call these inter-library patterns.

We observe that limiting the sample to a specific library may hinder the generalization of inter-library patterns, posing a threat to their use or interpretation. Using a simulation and a real case study, we demonstrate this threat for different sampling methods. Our simulation shows that only when sampling for the disjunction of both libraries involved in the implication of a pattern, the implication generalizes well. Additionally, we demonstrate that real empirical data sampled using the GitHub search API does not behave as expected from our simulation. This identifies a potential threat relevant for many studies that use the GitHub search API for studying inter-library patterns.

A land pattern change represents a globally significant trend with implications for the environment, climate, and societal well-being. While various methods have been developed to predict land change, our understanding of the underlying change processes remains inadequate. To address this issue, we investigate the suitability of the 2D kinetic Ising model (IM), an idealized model from statistical mechanics, for simulating land change dynamics. We test the IM on a variety of patterns, each with different focus land type. Specifically, we investigate four sites characterized by distinct patterns, presumably driven by different physical processes. Each site is observed on eight occasions between 2001 and 2019. Given the observed pattern at the time $t_i$ we find two parameters of the IM such that the model-evolved land pattern at $t_{i+1}$ resembles the observed land pattern at that time. The data supports simulating seven such transitions per site.

Our findings indicate that the IM produces approximate matches to the observed patterns in terms of layout, composition, texture, and patch size distributions. Notably, the IM simulations even achieve a high degree of cell-scale pattern accuracy in two of the sites. Nevertheless, the IM has certain limitations, including its inability to model linear features, account for the formation of new large patches, and handle pattern shifts.