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363 results for “stack”
InSAR stack of Fernandina volcano in Galápagos, Ecuador from Sentinel-1 descending track 128 processed with ISCE2/topsStack
<p>A stack of unwrapped interferograms on Fernandina volcano, Galápagos, Ecuador</p> <p>Sensor: Sentinel-1descending track 128</p> <p>Processor: ISCE/topsStack</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.x (~750 MB)</strong><br> Time: 2014.12.13 - 2018.06.19 (98 acquisitions, 288 interferograms)</p> <p><strong>Version 0.1 (~280 MB; for fast testing of code development)</strong><br> Time: 2014.12.13 - 2016.05..24 (36 acquisitions, 102 interferograms)</p>
Software Developer Expertise GitHub and Stack Overflow data sets
<p>Cross-Platform Software Developer Expertise Learning by Norbert Eke</p> <p>This data set is part of my Master's thesis project on developer expertise learning by mining Stack Overflow (SOTorrent) and Github (GHTorrent) data. Check out my portfolio website at norberte.github.io</p>
InSAR stack of San Francisco Bay, California from Sentinel-1 descending track 42 processed with GMTSAR
<p>A stack of unwrapped interferograms in the San Francisco Bay area, California, USA</p> <p>Sensor: Sentinel-1 descending track 42</p> <p>Processor: <a href="https://github.com/gmtsar/gmtsar" target="_blank" rel="noopener">GMTSAR</a></p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p>The tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p><strong>Version 1.x (~2.3 GB)</strong><br>Time: 2014.12.31 - 2024.06.05 (333 acquisitions, 1297 interferograms)</p> <p><strong>Version 0.x (~290 MB; for fast testing of code development)</strong><br>Time: 2020.01.04 - 2021.07.15 (70 acquisitions, 184 interferograms)</p>
Static Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries
<p># About this artifact<br> This artifact contains the implementation and the results of the evaluation of a<br> static slicer for WebAssembly described in the ICSE 2022 paper titled "Static<br> Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries".</p> <p>The artifact contains a docker image (`wassail-eval.tar.xz`) that contains<br> everything necessary to reproduce our evaluation, and the actual data resulting<br> from our evaluation:<br> 1. The implementation of our slicer (presented in Section 4.1) is included in<br> the docker machine, and is available publicly here:<br> https://github.com/acieroid/wassail/tree/icse2022<br> 2. Test cases used for our evaluation of RQ1 are included in the docker machine<br> and in the `rq1.tar.xz` archive.<br> 3. The dataset used in RQ2, RQ3, and RQ4 is included in the docker machine.<br> 4. The code needed to run our evaluation of RQ2, RQ3, and RQ4 is included in the<br> docker machine.<br> 5. The scripts used to generate the statistics and graphs that are included in<br> the paper for RQ2, RQ3, and RQ4 are included in the docker machine and as the<br> `*.py` files in this artifact.<br> 6. The data of RQ5 that has been used in our manual investigation is included in<br> the docker machine and in the `rq5.tar.xz` archive, along with<br> `rq5-manual.txt` detailing our manual analysis findings.</p> <p># How to obtain it<br> Our artifact is available on Zenodo at the following URL: https://zenodo.org/record/5821007</p> <p># Setting up the Docker image<br> ## Downloading The Artifact<br> The artifact is available at the following URL: https://zenodo.org/record/5821007</p> <p>## Loading The Docker Image<br> Once the artifact is downloaded in the file `icse2022slicing.tar.xz`, it can be extracted and loaded into Docker as follows (this takes a few minutes):<br> ```<br> docker import icse2022slicing.tar.xz<br> ```<br> To simplify further commands, you can tag the image using the printed sha256 hash of the image: if the `docker import` command resulted in the hash `54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2`, you can run:<br> ```<br> docker tag 54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2 wassail-eval<br> ```</p> <p>Once the Docker image has been loaded, you can run the following commands to<br> obtain a shell in the appropriate environment:<br> ```<br> docker volume create result<br> docker run -it -v result:/tmp/out/ wassail-eval bash<br> su - opam<br> ```</p> <p># Reproducing results of RQ1<br> Our manual translations of the "classical" examples are included in the `rq1/`<br> directory (available in the docker image and in `rq1.tar.xz`). We<br> include the slices computed by our implementation in the `rq1/out/` directory.</p> <p>A slice can be produced for each example in the docker image as follows, where<br> the first argument is the name of the program being sliced, the second the<br> function index being sliced, the third the slicing criterion (indicated as the<br> instruction index, where instructions start at 1), and the last argument is the<br> output file for the slice:</p> <p>```<br> cd rq1/<br> wassail slice scam-mug.wat 5 8 scam-mug-slice.wat<br> wassail slice montreal-boat.wat 5 19 montreal-boat-slice.wat<br> wassail slice word-count.wat 1 41 word-count-slice1.wat<br> wassail slice word-count.wat 1 43 word-count-slice2.wat<br> wassail slice word-count.wat 1 39 word-count-slice3.wat<br> wassail slice word-count.wat 1 45 word-count-slice4.wat<br> wassail slice word-count.wat 1 37 word-count-slice5.wat<br> wassail slice agrawal-fig-3.wat 3 38 agrawal-fig-3-slice.wat<br> wassail slice agrawal-fig-5.wat 3 37 agrawal-fig-5-slice.wat<br> ```</p> <p>The slice results can then be inspected manually, and compared with the original<br> version of the .wat program to see which instructions have been removed, or with<br> the expected solutions in the `out/` directory, e.g. by running:<br> ```<br> diff word-count-slice1.wat out/word-count-slice1.wat<br> ```<br> (No output is expected if the slice is correct)</p> <p># Reproducing results of RQ2, RQ3, and RQ4<br> For these RQ, we include the data resulting from our evaluation, but we also<br> allow reviewers to rerun the full evaluation if needed. However, such an<br> evaluation requires a heavy machine and takes quite some time (4-5 days to run<br> to completion with a 4 hours timeout). In our case, we used a machine with 256<br> GB of RAM and a 64-core processor with HyperThreading enabled, allowing us to<br> run 128 slicing jobs in parallel.</p> <p>## Runnig the Evaluation<br> We explain how to run the full evaluation, or only a partial evaluation below.<br> One can directly skip to the next section and reuse our raw evaluation results,<br> provided alongside this artifact.</p> <p>### Running the Full Evaluation<br> In order to reproduce our evaluation, you can run the following commands in the<br> docker image. It is recommended to run them in a tmux session if one wants to<br> inspect other elements in parallel (tmux is installed in the docker image). The<br> timeout (set to 4 hours per binary, like in the paper) can be decreased by<br> editing the `evaluate.sh` script (vim is installed in the docker image).</p> <p>This is expected to take 2-3 days of time, on a machine with 128 cores.<br> In order to produce only partial results, see the next section.</p> <p>```<br> cd filtered<br> cat ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {}<br> ```</p> <p>The results are outputted in the `/tmp/out/` directory.</p> <p>### Running a Partial Evaluation<br> If one does not have access to a high-end machine with 128 cores nor the time to<br> run the full evaluation, it is possible to produce partial results. To do so,<br> the following commands can be run. This will run the evaluation on the full<br> dataset in a random order, which can be stopped early to represent a partial<br> view of our evaluation, on a random subset of the data. In order to gather more<br> datapoints, it is also advised to decrease the timeout in the `evaluate.sh`<br> file, for example to 20 minutes by setting `TIMEOUT=20m` with `nano<br> evaluate.sh`. The number of slicing jobs running in parallel can also be<br> decreased to match the number of processors on the machine running the<br> experiments (the `-j 128` argument in the following command runs 128 parallel<br> jobs)</p> <p>```<br> sudo chown opam:opam /tmp/out/<br> cd filtered<br> shuf ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {}<br> ```</p> <p>The evaluation results will be stored in the `/tmp/out/` directory.</p> <p>### Skipping the Evaluation Run<br> Instead of rerunning the evaluation, one can rely on our full results included<br> in the `data.txt.xz` and `error.txt.xz` archives. These can simply be downloaded<br> from within the Docker machine and extracted in `/tmp/out/`:</p> <p>```<br> cd /tmp/out/<br> wget https://zenodo.org/record/5821007/files/data.txt.xz<br> wget https://zenodo.org/record/5821007/files/error.txt.xz<br> unxz data.txt.7z<br> unxz error.txt.7z<br> ```</p> <p>## Processing the data</p> <p>In order to process this data, we included multiple python script.<br> These require around 100GB of RAM to load the full dataset in memory.<br> The scripts should be run with Python 3.<br> When running this in the docker image, first run `cd /tmp/out/ && cp /home/opam/*.py ./`<br> - To count the number of functions sliced, run `cut -d, -f 1,2 data.txt | sort<br> -u | wc -l`. This takes around 6 minutes to run on the full dataset.<br> - To count the total number of slices encountered, run `wc -l data.txt<br> error.txt`. This takes around 15 seconds to run.<br> - To count the number of errors encountered, run `wc -l error.txt`. This takes<br> around 1 second to run.<br> - To produce data and graphs regarding the sizes and timing, run `python3<br> statistics-and-plots.py`. This will output the statistics presented in the<br> paper, along with Figure 2 (rq2-sizes.pdf) and Figure 3 (rq2-times.pdf). This<br> script takes around 35 minutes to run.<br> - To find the executable slices that are larger than the original programs, run<br> `python3 larger-slices.py > larger.txt`. This script takes around 2h30 to<br> run. It will list the slice using the notation `filename function-sliced<br> slicing-criterion` in the larger.txt file, from which the slice can be<br> recomputed by running `wassail slice function-sliced slicing-criterion<br> output.wat` in the docker image. It will also output statistics regarding<br> these slices, which you can easily inspect by running `tail larger.txt`.<br> - To investigate slices that could not be computed, run:<br> ```<br> sed -i error.txt -e 's/annotation,/annotation./'<br> python3 errors.py<br> ```<br> This will take a few seconds to run and will print a summary of the errors<br> encountered during the slicing process, and requires some manual sorting to map<br> to the categories we discuss in the paper. Here is a summary of the errors<br> encountered and their root cause:</p> <p>### Root Cause: Unsupported Usage of br_table<br> Error: (Failure"Invalid vstack when popping 2 values")<br> Error: (Failure"Spec_inference.drop: not enough elements in stack")<br> Error: (Failure"Spec_inference.take: not enough element in var list")<br> Error: (Failure"unsupported in spec_inference: incompatible stack lengths (probably due to mismatches in br_table branches)")<br> ### Root Cause: Unreachable Code<br> Error: (Failure"Unsupported in slicing: cannot find an instruction. It probably is part of unreachable code.")<br> Error: (Failure"bottom annotation")<br> Error: (Failure"bottom annotation. this an unreachable instruction")</p> <p># RQ5: Comparison to Slicing C Programs<br> For this RQ, we include the following data in the `rq5.7z` archive, and in the `rq5/` directory in the docker image:<br> - The slicing subjects in their C and textual wasm form in `rq5/subjects/`<br> - The CodeSurfer slices in their C and textual wasm form in `rq5/codesurfer/`<br> - Our slices in their wasm form in `rq5/wasm-slices/`</p> <p>As this RQ requires heavy manual comparison, we do not expect the reviewers to<br> reproduce all of our results. We include a summary of our manual investigation<br> in `rq5-manual.txt`. In order to validate these manual findings, one can for<br> example inspect a specific slice. For example, the following line in<br> `rq5-manual.txt`:</p> <p>```<br> adpcm_apl1_565_expr.c.wat INTERPROCEDURAL<br> ```</p> <p>can be validated as follows:<br> ```<br> cd ~/<br> # This generates a trimmed down version of the CodeSurfer slice, only containing the function of interest<br> wassail count-in-slice rq5/codesurfer/adpcm_slices/adpcm_apl1_565_expr.c.wat slice.wat<br> # This compares the CodeSurfer slice with our slice<br> diff --side-by-side slice.wat rq5/adpcm_apl1_565_expr.c.wat<br> ```</p> <p>In this case, most extraneous instructions are present in the CodeSurfer slices,<br> at the end of the function. This indicates that these are present in order to<br> preserve interprocedural behavior, which corresponds to the `INTERPROCEDURAL`<br> tag in the `rq5-manual.txt`</p> <p> </p>
Sentinel-1 data stack for Masjed Soleyman Dam
<p>This is a Sentinel-1 sample dataset for the SARvey InSAR time series analysis software.</p> <p>This dataset consists of:</p> <ul> <li> A stack of coregistered SLCs for the Masjed Soleyman Dam and its corresponding geometry data in MiaplPy format. These files serve as the input data for SARvey.<br> SARvey_input_data_Masjed_Soleyman_dam_S1_dsc_2015_2018.zip</li> <li> The final products generated by SARvey for reference.<br> SARvey_final_results_Masjed_Soleyman_dam_S1_dsc_2015_2018.zip</li> </ul> <p><br>A cookbook is available to help you run the software using this dataset.</p> <p> </p>
Data underlying the paper titled "Positron unveiling high mobility graphene stack interfaces in Li-ion cathodes"
<p>The folder includes data regarding 4 figures shown in this paper. </p> <p>FIG_1: Simulation structure of 6 layers of graphene bulk and slab (6C_Bulk.vasp, 6C_Slab.vasp), LiCoO2(LCO_336.vasp), ABA Graphite coating LiCoO2(G@LCO.vasp). </p> <p>FIG_2: Raw data of band structure of Graphite coating LCO (Band_G@LCO_EIGENVAL), Density of States (DOS_G@LCO_DOSCAR)</p> <p>FIG_3: Calculated plane-averaged charge density difference of SW-G@LCO perpendicular to (001) plane at the equilibrium distance. G@LCO_CHGCAR, LCO_CHGCAR, C_CHGCAR are the CHGCAR for Graphitte coating LCO, LCO, graphite, respectively. </p> <p>d24_diff.vasp is the charge difference</p> <p>d24_PACD.dat is the plane-averaged charge density difference. </p> <p>FIG_4: posden indicates the positron density, while posvtot means positron potential. Data are named by their structure. </p>
pKaDatabase for Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge
<p>A curated a database of small molecules with experimentally measured pKa values. </p> <p>This pickle file can be loaded into memory using Pandas. In the code block below we will print out the columns of the DataFrame:</p> <pre><code class="language-python">import pandas as pd df = pd.load("pKaDatabase.pkl") print(df.keys()). # print the columns</code></pre> <blockquote> <p>['deprotonated microstate ID', 'protonated microstate ID', 'deprotonated microstate smiles', 'protonated microstate smiles', 'AM1BCC partial charge (prot. atom)', 'AM1BCC partial charge (deprot. atom)', 'AM1BCC partial charge (prot. atoms 1 bond away)', 'AM1BCC partial charge (deprot. atoms 1 bond away)', 'AM1BCC partial charge (prot. atoms 2 bond away)', 'AM1BCC partial charge (deprot. atoms 2 bond away)', 'Gasteiger partial charge (prot. atom)', 'Gasteiger partial charge (deprot. atom)', 'Gasteiger partial charge (prot. atoms 1 bond away)', 'Gasteiger partial charge (deprot. atoms 1 bond away)', 'Gasteiger partial charge (prot. atoms 2 bond away)', 'Gasteiger partial charge (deprot. atoms 2 bond away)', 'Extented Hückel partial charge (prot. atom)', 'Extented Hückel partial charge (deprot. atom)', 'Extented Hückel partial charge (prot. atoms 1 bond away)', 'Extented Hückel partial charge (deprot. atoms 1 bond away)', 'Extented Hückel partial charge (prot. atoms 2 bond away)', 'Extented Hückel partial charge (deprot. atoms 2 bond away)', '∆G_solv (kJ/mol) (prot-deprot)', 'SASA (Shrake)', 'SASA (Lee)', 'Bond Order', 'Change in Enthalpy (kJ/mol) (prot-deprot)', 'pKa','href', 'num ionizable groups', 'Weight', 'pKa source']</p> </blockquote> <p> </p> <p>For more information regarding feature calculations, please read the following paper:</p> <blockquote> <p>Raddi, Robert, and Vincent Voelz. "Stacking Gaussian Processes to Improve pKa Predictions in the SAMPL7 Challenge." (2021). <a href="https://doi.org/10.26434/chemrxiv.14650302.v1">10.26434/chemrxiv.14650302.v1</a></p> </blockquote>
Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 2
Open the record for dataset details and reuse information.
Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 3
Open the record for dataset details and reuse information.
C. elegans HSP-17 translational reporter LSM z-stacks
<p>A translational reporter (transgene: <em>phsp-17::hsp-17::wrmScarlet</em>) of the small heat shock protein HSP-17 from C. elegans was generated. This construct expresses a C-terminal fusion of wrmScarlet to HSP-17 in a wild type background, controlled by the native promoter of <em>hsp-17</em>. A publication, for which this worm strain was generated, titled "The non-canonical small heat shock protein HSP-17 from <em>C. elegans</em> is a selective protein aggregase" to JBC is pending (manuscript number: JBC/2019/011185). The dataset contains the raw LSM-data, a version modified for easier viewing as well as a short movie of a 3d-projection. Data for the worms head, body and tail are given.</p> <p>For wrmScarlet, refer Bindels <em>et al.</em>, 2016 & El Mouridi <em>et al.</em>, 2017. Data was evaluated and modified with Fiji (Rueden <em>et al.</em>, 2017 & Schindelin <em>et al.</em>, 2012).</p>
Measurement of 139La(p,x) cross sections from 35-60 MeV by stacked-target activation
<p>This repository contains all raw gamma-ray spectra analyzed for the present manuscript, as well as calibration spectra. Further details and analysis code are available on reasonable request. </p> <p>A stacked-target of natural lanthanum foils (99.9119% 139La) was irradiated using a 60 MeV proton beam at the LBNL 88-Inch Cyclotron. 139La(p,x) cross sections are reported between 35–60 MeV for nine product radionuclides. The primary motivation for this measurement was the need to quantify the production of 134Ce. As a positron-emitting analogue of the promising medical radionuclide 225Ac, 134Ce is desirable for in vivo applications of bio-distribution assays for this emerging radio-pharmaceutical. The results of this measurement were compared to the nuclear model codes TALYS, EMPIRE and ALICE (using default parameters), which showed significant deviation from the measured values.</p>
CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning
<p>Advanced computational methods integrating ensemble learning with interpretable machine learning are essential for precision livestock management under increasing environmental constraints and food security pressures. This study develops a novel stacking-based interpretable machine learning (IML) framework that combines multiple algorithms with SHAP analysis techniques to generate the China's Long-term Ruminant Livestock Distribution in Grazing Livestock Production Systems (CLRD-GLPS) dataset. Our computational approach addresses critical challenges in livestock distribution modelling: livestock segmentation and spatial prediction accuracy. The framework integrates Random Forest, XGBoost, CatBoost, LightGBM, and Extra Trees through a two-layer stacking architecture, enhanced with SHAP (Shapley Additive Explanations) analysis for model interpretability. We also implemented interpretable machine learning for livestock production system segmentation to distinguish grazing from total livestock populations. The stacking ensemble demonstrated superior performance over individual algorithms, achieving R² values of 0.954-0.961 for cattle and 0.896-0.901 for sheep and goats, with improvements of up to 8.3% compared to best performance single-model approaches. Multi-scale validation confirmed computational robustness: livestock segmentation achieved R² = 0.80 at county level, while independent city-level validation of CLRD-GLPS datasets yielded R² = 0.76-0.80. SHAP interpretability analysis revealed distinct environmental drivers, with vegetation indices and topography primarily influencing cattle distribution, while snow conditions and elevation dominated sheep and goat patterns. This computational framework advances livestock distribution modelling through enhanced prediction accuracy, model stability, and interpretability, while the CLRD-GLPS dataset provides essential spatial-temporal information for rangeland sustainability assessments and evidence-based livestock management policies. This dataset is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, grant no. 2019QZKK0906).</p>
Novel Libraries in Stack Overflow Posts
<p># Summary</p> <p>We present datasets detailing the appearance of novel libraries and library pairs in Stack Overflow posts in 12 languages between 2008 and 2023.</p> <div> <div># Disclaimer</div> <br> <div>Pair of libraries are displayed in the canonical format of <lib_a>|<lib_b> where lib_a precedes lib_b in alphabetical ordering.</div> <br> <div>Some of the examples are truncated for better readability.</div> <br> <div>GitHub source of the project: https://github.com/MeszarosGabor/SO_Post_Analyzer</div> <br> <div># Descriptions</div> <div>## `<language>`/all_`<language>`_so_posts.jsonl</div> <br> <div>JSONL file that contains the raw extracted Stack Overflow fields. Within a single JSON object:</div> <div>key: post_id,</div> <div>values:</div> <div>- post_type: 1 for question and 2 for answer</div> <div>- accepted_answer_id</div> <div>- date_posted</div> <div>- score</div> <div>- view_count</div> <div>- code_snippets</div> <div>- post_length</div> <div>- poster_id</div> <div>- last_actiivity</div> <div>- tags</div> <div>- number of comments</div> <div>- number of answers</div> <div>- parent id</div> <br> <div>Example:</div> <div>```</div> <div>{"72": ["1", "", "2008-08-01T13:38:27.133", "48", "2148", "<p>I want to format my existing comments as 'RDoc comments' so they can be viewed using <code>ri</code>.</p>\n\n<p>What are some recommended resources for starting out using RDoc?</p>\n", "25", "2016-12-30T06:56:18.310", "<ruby><rdoc>", "1", "2", ""]}</div> <div>```</div> <br> <div>## `<language>`/`<language>`_all_libs_dates.json</div> <br> <div>JSON file that lists the dates (with multiplicity, one for every post) when an individual library was mentioned in a post.</div> <br> <div>Example:</div> <div>```</div> <div>'FileUtils': ['2011-06-09',</div> <div>'2011-07-01',</div> <div>'2011-11-20',</div> <div>'2011-11-20',</div> <div>...</div> <div>'2013-09-04',</div> <div>'2020-05-08',</div> <div>'2021-02-25']</div> <div>```</div> <br> <div>## `<language>`/`<language>`_all_pairs_dates.json</div> <br> <div>JSON file that lists the dates (with multiplicity, one for every post) when a pair of libraries was mentioned in a post.</div> <br> <div>Example:</div> <div>```</div> <div>'mongo_mapper|sinatra': ['2010-09-12',</div> <div>'2011-12-30',</div> <div>'2012-02-23',</div> <div>'2012-09-04'],</div> <div>```</div> <br> <div>## `<language>`/`<language>`_libs_count.json</div> <br> <div>JSON file that lists the occurrence count of the individual libraries.</div> <br> <div>Example:</div> <div>```</div> <div>{</div> <div>'cairo': 4,</div> <div>'pango': 2,</div> <div>'radix': 1,</div> <div>}</div> <div>```</div> <br> <div>## `<language>`/`<language>`_pairs_count.json</div> <br><br> <div>JSON file that lists the co-occurrence count of the pairs of libraries.</div> <br> <div>Example:</div> <div>```</div> <div>'mongo_mapper|sinatra': 4,</div> <div>'fileutils|getoptlong': 1,</div> <div>'redis|rubygems': 24,</div> <div>```</div> <br> <div>## `<language>`/`<language>`_libs_first_dates.json</div> <br> <div>JSON file that lists the dates of the first appearances of individual libraries alongside the post id and poster id.</div> <br> <div>Example:</div> <div>```</div> <div>{</div> <div>'cairo': {'id': '6242589', 'poster_id': '784674', 'date': '2011-06-05'},</div> <div>}</div> <div>```</div> <br> <div>## `<language>`/`<language>`_pairs_first_dates.json</div> <div>JSON file that lists the dates of the first co-appearances of pairs libraries alongside the post id and poster id.</div> <br> <div>Example:</div> <div>```</div> <div>'rubygems|server': {'id': '3748309',</div> <div>'poster_id': '262808',</div> <div>'date': '2010-09-20'</div> <div>```</div> <br> <div>## `<language>`/`<language>`_`<language>`_code_count_list.json</div> <br> <div>JSON file that contains a single list of library counts in the posts (in chronological order) that contain *at least one* library import.</div> <br> <div>## `<language>`/`<language>`_daily_post_stats.json</div> <div>JSON file that counts the number of posts on a given day, listed chronologically, containing dates *with at least one post*. Dictionary of key=date value=count(int) pairs.</div> <br> <div>Example:</div> <div>```{...</div> <div>'2011-09-03': 6,</div> <div>'2011-09-04': 3,</div> <div>'2011-09-05': 10,</div> <div>'2011-09-06': 5,</div> <div>'2011-09-07': 15,</div> <div>...}</div> <div>```</div> <br> <div>## `<language>`/`<language>`_`<langugae>`_post_stats.json</div> <br> <div>JSON file that lists the individual post metadata (sorted by post date).</div> <div>Fields:</div> <div>- post id,</div> <div>- post type,</div> <div>- list of imports</div> <div>- post date</div> <div>- poster id</div> <div>- score</div> <br> <div>Example:</div> <br> <div>```</div> <div>{'id': '1892176',</div> <div>'post_type': '1',</div> <div>'imports': ['mechanize', 'rubygems'],</div> <div>'date': '2009-12-12T03:31:43.823',</div> <div>'poster_id': '124685',</div> <div>'score': '5'},</div> <div>```</div> <br> <div>## `<language>`/`<language>`_time_based_new.jsonl</div> <br> <div>JSONL file that contains JSON objects (in chronological order) detailing post metadata.</div> <div>Fields:</div> <div>- post id,</div> <div>- post date</div> <div>- poster id (user id)</div> <div>- post type,</div> <div>- list of imports</div> <div>- list of novel libraries in post</div> <div>- list of novel pairs in post</div> <br> <div>Example:</div> <div>```</div> <div>{'post_id': '3543',</div> <div>'post_date': '2008-08-06T15:24:00.787',</div> <div>'user_id': '399',</div> <div>'post_type': '2',</div> <div>'imports': ['metric_fetcher', 'rake'],</div> <div>'new_libs': ['metric_fetcher', 'rake'],</div> <div>'new_pairs': ['metric_fetcher|rake']}</div> <div>```</div> <br> <div>## `<language>`/`<language>`_user_to_posts.json</div> <br> <div>JSON file that lists the post ids corresponding to a given user id. Keyed by user ids, values are list of post ids.</div> <br> <div>Example:</div> <div>```</div> <div>'303675': ['2941479'],</div> <div>'348325': ['2945141', '2956990', '2968924', '3832703'],</div> <div>'325477': ['2945228'],</div> <div>'27196': ['2949100', '3177217'],</div> <div>```</div> </div>
Stacked remote sensing indices covering OAL-Austria
<p>Stacked indices derived from Sentinel-2A/B imagery (processing level 2A) covering the period from 2017/04/24 to 2022/01/16</p> <p># Normalized Difference Vegetation Index(Rouse etal. 1974) NDVI = (NIR ‒ R)/(NIR + R)<br> # Visible Difference Vegetation Index (Wang et al. 2015) VDVI = ((2*G) - R - B)/((2 * G) + R + B)<br> # Enhanced vegetation index (Schwieder et al. 2022) EVI=G*(nir-red)/(nir+C1*red-C2*blue+X)<br> # Excess green index ExGI=2*g-(r+b)<br> # Green chromatic coordinate GCC=g/(r+g+b)<br> # Normalized difference moisture index (Lastovicka et al. 2020) NDMI = (NIR − SWIR) / (NIR + SWIR)</p>
Combining Horizontal Strain DAS and Local Seismic Stations in a Full Waveform Attribute Stacking Detector/Locator Algorithm: Verification Test for the Thorbjörn, Iceland, 2020 Unrest Episode
<p>We present a waveform stacking-based earthquake catalog of the seismicity unrest episode in the Svartsengi fissure swarm close to Mt. Thorbjörn, SW Iceland, which started in January 2020 and was still ongoing in January 2021. The magmatic unrest produced more than 5 earthquake swarms comprising thousands of individual events each. We were able to combine local and regional seismic networks with 6 months recording of a 17 km long distributed acoustic sensing (DAS) fibre optical cable with a channel resolution of 4 m. The kHz DAS data were downsampled to 200 Hz and stacked every 64 m. The catalog is based on a migration-based detector / locator technique as for instance implemented in Lassie (Pyrocko). In the accompanying we demonstrate the robustness in a wide variety of applications in seismology. For this dataset, we have extended Lassie to efficiently combine linear ultra-dense sensor arrays with sparse seismological networks.</p>
SAR Stack of Pichincha volcano in Ecuador, from Sentinel-1
<p>A stack of Coregistered SLCs on Pichincha volcano, Ecuador</p> <p>Sensor: Sentinel-1 Descending track 142</p> <p>Time: 2016.04.19 - 2018.12.28, 46 acquisitions</p> <p>Processor: ISCE/topsStack</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p>
Stack Exchange Open Source site questions categorization
<p>This dataset contains the posts of Open Source Stack Exchange site, collected at the end of 2020, along with the categorization of the posts. For each post a category, and potentially a second one is indicated, along with the cluster (generic group) each category belongs to. The coding task of assigning each question to a category was performed by two independent coders for each question (the categorization of each coder is also provided in the dataset). The dataset contains also (in a separate file) a dictionary of the most correlated unigrams and bigrams per category.</p>
InSAR stack of Western Cape, South Africa from Sentinel-1 ascending track 29 processed with SNAP
<p>A stack of unwrapped interferograms on Western Cape, South Africa.</p> <p>Sensor: Sentinel-1ascending track 29</p> <p>Time: 2019.03.03 - 2019.05.14, 7 acquisitions, 15 interferograms</p> <p>Processor: SNAP (accessed on 14 July 2019)</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p>
Multi-channel seismic reflection profiles SALTFLU (Salt deformation and sub-salt fluid circulation in the Algero-Balearic abyssal plain) - Pre-Stack Kirchhoff Time & Depth Migration 2022
<p>This archive contains sections of reprocessed multi-channel seismic reflection profiles SALTFLU, acquired south of Ibiza (Spain) in 2012 with the OGS Explora (pre-stack Kirchhoff time and depth stacks, and migration velocities in SEG-Y format). It also contains the cruise report describing the survey acquisition in 2012. Connected articles describe the processing flow applied to this dataset and interpretations led by the first author. </p> <p>Field File Identification and Shot Numbers (FFID, SHOTNO) are linearly interpolated by matching the CMP numbers before and after migration. Bytes 73-76 and 77-80 are identical to bytes 181-184 and 185-188 and contain the CMP coordinates.</p> <p> </p> <p> </p> <p> </p>
InSAR stack of San Francisco Bay in California from Sentinel-1 descending track 42 processed with ARIA
<p>A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42</p> <p>Processor: ARIA (processed using ISCE and prepared using <a href="https://github.com/aria-tools/ARIA-tools">ARIA-tools</a> as shown below)</p> <p>Tropospheric delay estimated from ERA5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.2 (~5 GB):</strong><br> Time: 2015.03.01 - 2022.06.04, 189 acquisitions, 961 interferograms<br> Used ARIA-tools and MintPy commands:</p> <pre><code>ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42 ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download prep_aria.py -s ../stack/ -d ../DEM/SRTM_3arcsec.dem -i ../incidenceAngle/*.vrt -a ../azimuthAngle/*.vrt -w ../mask/watermask.msk</code></pre> <p><strong>Version 0.2 (~280 MB; for fast testing of code development)</strong><br> Time: 2016.01.31 - 2017.05.10, 23 acquisitions, 91 interferograms<br> Used ARIA-tools commands (access date Jun 18th, 2022):</p> <pre><code>ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --start 20160101 --end 20170510 ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download</code></pre> <p> </p>
ScienceDex guides
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