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148 results for “Inequality”
The Intersectional Digital Inequality in Indonasia dataset
<p>The dataset presents the results of the research on intersectional digital inequality in Indonesia between the years 2018 and 2022.</p> <p>The raw data on internet access, signal availability and telecommunication expenditures were sourced from Badan Pusat Statistik - Statistic Office of Indonesia, published in the report Telecommunication Statistics in Indonesia (2022).</p> <p>Apart from one-dimensional or multi-dimensional groups averages, the dataset includes Equality Ratios and Intersectional Surplus computed according to the methodology proposed by Meili (2022).</p> <p>References:</p> <p><span>BPS-Statistics Indonesia, 2022. <em>Telecommunication Statistics in Indonesia. Technical Report.</em> Badan Pusat Statistik.</span></p> <p><span>Meili, D., Günther, I., Harttgen, K., 2022. Intersectional Inequality in Education, in: <em>37th IARIW General Conference.</em></span></p> <p> </p>
Replication Data & Code - Large-scale land acquisitions exacerbate local land inequalities in Tanzania
<h3><strong>Reference</strong></h3><p>Sullivan J.A., Samii, C., Brown, D., Moyo, F., Agrawal, A. 2023. Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania. Proceedings of the National Academy of Sciences 120, e2207398120. <a href="https://doi.org/10.1073/pnas.2207398120">https://doi.org/10.1073/pnas.2207398120</a> </p><h3><strong>Abstract</strong></h3><p>Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet, rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100 million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (<i>P</i> = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty, and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities.</p><h3><strong>Replication Data</strong></h3><p>We include anonymized household survey data from our analysis to support open and reproducible science. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. For the 2018 surveys, several anonymized extracts are provided including an imputed (n=10) dataset to fill in missing data that was used for the main analysis. This data can be found in the <i>hh_data</i> folder and includes:</p><ul><li><i>hh_imputed10_2018:</i> anonymized household dataset for 2018 with variables used for the main analysis where missing data was imputed 10 times</li><li><i>hh_compensation_2018:</i> anonymized household extract for 2018 representing household benefits and compensation directly received from LSLAs</li><li><i>hh_migration_2018:</i> anonymized household extract for 2018 representing household migration behavior following LSLAs</li><li><i>hh_rsdata_2018:</i> extracted remote sensing data at the household geo-location for 2018</li><li><i>hh_land_<strong>2019</strong>:</i><strong> </strong> anonymized household extract for <strong>2019 </strong>of land variables</li></ul><p>Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in <i>lsms_data</i> folder). We've provide sub-modules from the LSMS dataset relevant to our analysis but the full datasets can be access through the World Bank's Microdata Library (https://microdata.worldbank.org/index.php/home). </p><p>Across several analyses we use the LSLA boundaries for our four selected sites. We provide a shapefile for the LSLA boundaries in the <i>gis_data</i> folder.</p><p>Finally, our data replication includes several model outputs (found in <i>mod_outputs)</i>, particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our <i>main_analysis.Rmd</i> script. </p><h3><strong>Replication Code</strong></h3><p>We provide replication code in the form of R Markdown (.Rmd) or R (.R) files. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article. Scripts include:</p><ul><li><i>main_analysis.Rmd:</i> main analysis supporting the finding, graphs, and tables reported in our main manuscript</li><li><i>compensation.R:</i> analysis of benefits and compensation received directly by households from LSLAs</li><li><i>landvalue.R:</i> analysis of household land values as a function of distance from LSLAs</li><li><i>migration.R:</i> analysis of migration behavior following LSLAs</li><li><i>selection_bias.R:</i> analysis of LSLA selection bias between control and treatment enumeration areas</li></ul>
Data for "Demographic inequalities in digital spaces in China: The case of Weibo"
<p>These data underlie the results and figures used in the article "Demographic inequalities in digital spaces in China: The case of Weibo" (https://doi.org/10.36190/2023.01). This research was presented at the ICWSM workshop "Data for the wellbeing of the most vulnerable" on June 5, 2023.</p><p>The corresponding workflow can be found in the linked repository.</p><p>`README.md` provides more details.</p>
Data bases for Measuring impacts of oral health promotion interventions on health inequities: the example of New Caledonia
<p>Extract from the New Caledonian (NC) epidemiological survey database for identifying the determinants and risk factors explaining the presence of untreated dental caries and to compare the prevalence and severity of dental caries between 2012 and 2019, in order to identify potential changes that occurred in NC.</p>
Violation of Bell's Inequality under Strict Einstein Locality Conditions
<p><strong>Data reuse</strong></p> <p>Please cite G. Weihs et al., Phys. Rev. Lett. <strong>81,</strong> 5039 (1998) in publications that reuse this data and if possible inform the lead author, who is always keen to learn what else one can do with the data.</p> <p><strong>Data description</strong></p> <p>The data contained in the two archives <a href="Alice.zip">Alice.zip</a> and <a href="Bob.zip">Bob.zip</a> and were collected in the years 1997 through 1999 as a long-distance test of Bell's inequality and quantum key distribution. Publications that are based on this data include G. Weihs et al., Phys. Rev. Lett. <strong>81,</strong> 5039 (1998) and T. Jennewein et al. Phys. Rev. Lett. <strong>85,</strong> 4729 (2000). The former is cited in the 2022 Nobel Prize announcement.</p> <p><strong>Data format</strong></p> <p>All files come in triplets for each observer (Alice, blue, west end of Innsbruck campus; Bob, red, east end of campus). Everything has been packed into an archive for each observer (alice.zip, bob.zip).</p> <table> <tbody> <tr> <td> <p>*_H.txt</p> </td> <td> <p>Cryptic laboratory comments containing information about some unimportant(?) experimental parameters.</p> </td> </tr> <tr> <td> <p>*_V.dat</p> </td> <td> <p>Photon arrival times where the "arm" time has already been subtracted. IEEE-8bit double precision numbers in "Big Endian"-form naturally sorted ascendingly. Actual time resolution is 10<sup>-10</sup>seconds, but accuracy is only 0.5 ns.</p> </td> </tr> <tr> <td> <p>*_C.dat</p> </td> <td> <p>Apparatus setting and "outcome" for each photon detection encoded in the following way: 16-bit integers in "Big Endian"-form, each number showing the detector (0=vertical, 1=horizontal with repect to the polarizer) that fired in its LSB and the position of the switch (0=no rotation, 1=45° rotation) in the next to LSB. (Not very efficient, but quicker when sampling directly to the PCs RAM during data acquisition!)</p> <p>Somebody said that the bits must be reversed. I don't think so but there is always a chance that I made double, compensating mistakes in my LabView and C programming.</p> </td> </tr> </tbody> </table> <p><strong>Files and folders</strong></p> <table> <tbody> <tr> <td><strong>Folder Name</strong></td> <td><strong>Description</strong></td> <td><strong>Mode</strong></td> <td><strong>Start Date</strong></td> </tr> <tr> <td>bellstat</td> <td>Static BI test (no fast switching)</td> <td>local, static</td> <td>30-Nov-1997</td> </tr> <tr> <td>bluesine</td> <td>Static correlation scan varying "blue" modulator bias</td> <td>local</td> <td>30-Nov-1997</td> </tr> <tr> <td>first</td> <td> </td> <td>local</td> <td>28-Nov-1997</td> </tr> <tr> <td>firstlong</td> <td>First run of long-distance (360m separation) measurements</td> <td>remote</td> <td>28-Apr-1998</td> </tr> <tr> <td>korrel</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>localswitch</td> <td> </td> <td>local, switched (45°)</td> <td>11-Jan-1998</td> </tr> <tr> <td>locbell</td> <td>BI Test</td> <td>local, swiched (45°)</td> <td>26-Mar-1998</td> </tr> <tr> <td>loccorr</td> <td>Measurement of correlations in parallel bases</td> <td>local , switched (45°)</td> <td>30-Mar-1998</td> </tr> <tr> <td>longdist</td> <td>Main run of long-distance BI tests</td> <td>remote, switched (45°)</td> <td>15-Apr-1998</td> </tr> <tr> <td>longtime</td> <td>Maximum continous measurement time (about 5 minutes!) BI test</td> <td>remote, switched (45°)</td> <td>06-Jun-1998</td> </tr> <tr> <td>newlongtime</td> <td> </td> <td>remote, switched (45°)</td> <td>22-Dec-1998</td> </tr> <tr> <td>scanblue</td> <td>Scan varying "blue" side modulator bias</td> <td>remote, switched (45°)</td> <td>01-May-1998</td> </tr> <tr> <td>scanred</td> <td>Scan varying "red" side modulator bias</td> <td>remote, switched (45°)</td> <td>31-May-1998</td> </tr> <tr> <td>sineblue</td> <td>Scan varying "blue" side modulator bias</td> <td>local, switched (45°)</td> <td>01-Apr-1998</td> </tr> <tr> <td>sinered</td> <td>Scan varying "red" side modulator bias</td> <td>local, switched (45°)</td> <td>30-Mar-1998</td> </tr> <tr> <td>switchtest</td> <td> </td> <td>local, switched (45°)</td> <td>03-Jan-1998</td> </tr> <tr> <td>wignerscanalice</td> <td>Scan varying "blue"side modulator bias, basis choice for Wigner's inequality</td> <td>remote, switched (30°)</td> <td>23-Dec-1998</td> </tr> </tbody> </table>
The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil
<p>This video shows de simulation of scenarios presented in the article "The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil"</p>
Unfair Inequality in Education: A Benchmark for AI-Fairness Research (Aequitas WP7 Use Case S2)
<h1>Unfair Inequality in Education: A Benchmark for AI-Fairness Research</h1> <p>This dataset proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students.</p> <h2>Structure</h2> <p><code>benchmark</code> contains:</p> <ul> <li>the proposed dataset (<code>dataset.csv</code>), </li> <li>the mask for dealing with missing values (<code>missing_mask.csv</code>), and</li> <li> <div> <div>the meta-columns providing grouping criteria and sample weights for each student (<code>meta_cols.csv</code>).</div> </div> </li> </ul> <p><code>raw_data</code> includes:</p> <ul> <li>the original dataset (<code>original.csv</code>), and</li> <li>the intermediate stages of the pre-processing and validation pipelines (<code>split</code>, <code>pre_processed</code>, and <code>validation</code>).</li> </ul> <p><code>res</code> contains the documentation, including:</p> <ul> <li>the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (<code>meta_data_mapping.csv</code>),</li> <li>the value type and domains of each column of the proposed datasets (<code>meta_data_stats.json</code>), and</li> <li> <div> <div>the statistical indices of the validation pipeline (<code>bias_preservation_results.json</code>).</div> </div> </li> </ul> <p><code>src</code> contains the source code for running the pre-processing and corresponding analysis:</p> <ul> <li><code>pre_processing</code> and <code>stats</code>contain the code for the two corresponding tasks, and</li> <li><code>pre_processing.py</code> and <code>split.py</code> are two entry points.</li> </ul> <p>Finally, <code>Dockerfile</code> and <code>requirements.txt</code> set up the environment for running the applications across multiple platforms and with Python, respectively.</p>
Replication package for the paper "A configurational approach to job quality analysis: forms of inequalities at work in Europe"
<p>The following replication package is appended to the article <span><span><span><span>Étienne Penissat</span><span>, </span></span><span><span>Cécile Rodrigues</span><span> & </span></span><span><span>Alexis Spire</span></span></span></span> <span>(2024)</span> "<span>A configurational approach to job quality analysis: forms of inequalities at work in Europe",</span> <span>European Societies,</span> <span>DOI: <a href="https://doi.org/10.1080/14616696.2024.2312950">10.1080/14616696.2024.2312950</a></span></p> <p>The scripts to be run in the following order are:</p> <p>- 1_Penissat_EuropeanSocieties_2023_DataPreparation.R : Recoding, formatting and scope of data used</p> <p>- 2_Penissat_EuropeanSocieties_2023_DataAnalysis.Rmd : Analysis and statistical results</p> <p>The data used in the article comes from the EWCS (2015) - European Working Condition Survey - provided by the European foundation for the improvement of living and working conditions. The data is not available on free access but can be obtained on request. Information on the survey wave used can be found here : https://www.eurofound.europa.eu/surveys/european-working-conditions-surveys/sixth-european-working-conditions-survey-2015</p> <p>- In the first "1_Penissat_EuropeanSocieties_DataPreparation.R" script, the file containing data named "ewcs_1991-2015.dta" is used. The file called "eseg2_trad.csv" contains english labels for the nomenclature of professional positions ESeG. As "ewcs_1991-2015.dta" is not freely available, it is not included in the package and "eseg2_trad.csv" is located in the "data" folder.</p> <p>- The first script creates the data file "Penissat_EuropeanSocieties_2023_EWCS15_cleanData.rds" in the "results" folder.</p> <p>- The second script called "2_Penissat_EuropeanSocieties_DataAnalysis.Rmd" uses the "Penissat_EuropeanSocieties_2023_EWCS15_cleanData.rds" data file and produces the "2_Penissat_EuropeanSocieties_DataAnalysis.html" file containing all code and results presented in the article.</p>
Online Appendix for Networks of Inequality: Access to Water in Roman Pompeii
<p><strong>Online Appendix for Networks of Inequality: Access to Water in Roman Pompeii (Samuli Simelius)</strong></p><p>in Journal of Computer Applications in Archaeology "A Bridge too Far – Historical, Archaeological and Criminal Network Research"(eds. M. De Bernardin, M. Lorenzon, L. Tambs & A. Traviglia)<br><br>Figure 1: The locations of the dwelling entrances used in the study, as well as the entrances to the unexcavated area.<br>Figure 2: The polyline layer representing the street network.</p><p>Table 1: The dwellings which I have interpreted differently from Matthew Notarian's interpretation – including their new water collection values.</p><p>Table 2: The population estimations of the dwellings and their new water collection values (seconds).<br>Table 3: The distances (minutes and meters) from dwellings to baths. </p>
Narratives on inequalities in enablers and hindrances for advancing behavioural change through an inclusive Green Deal in Europe. ACCTING dataset
<p><span>The impact of climate change and the capacity to mitigate its negative impacts are unevenly distributed across and within societies; it is the poorer, marginalised and vulnerable groups who are the most acutely affected. This dataset captures some of the experiences of those groups. It includes 358 narratives collected via individual narrative interviews in 13 European countries during June-December 2022 across eight thematic research lines – each research line addressing an EU Green Deal policy area.</span></p> <p><span>The data was gatered in the EU funded </span><span>ACCTING, which </span><span>mobilises research experimentation and innovation to promote an inclusive and socially just European Green Deal focusing on the inequalities produced by its policies and supporting behavioural change at individual and collective levels. </span></p> <p><span>The project explores the impact of Green Deal policy initiatives on individual and collective behaviours, provides evidence, and empowers policymakers and stakeholders to anticipate policy responses and potential negative influences, and mitigate such impacts in decision-making. ACCTING collects new data on Green Deal policy interventions and co-designs and implements pilot actions to reduce or prevent policy-related inequalities and advance behavioural change for an inclusive and equal European Green Deal. </span></p>
PreprintMatch: a tool for preprint publication detection applied to analyze global inequities in scientific publishing
<p>Dataset underlying the paper "PreprintMatch: a tool for preprint publication detection applied to analyze global inequities in scientific publishing." preprint-paper-matches.csv lists all matches found by our algorithm between bioRxiv/medRxiv and PubMed, and preprint_affiliations.csv lists all extracted affiliations from bioRxiv/medRxiv. The Rxivist data dump (https://zenodo.org/record/4738007) was used for all preprint data, and the scrips to download PubMed data are available on our GitHub repository, https://github.com/PeterEckmann1/preprint-match.</p> <p>The full database dump, with all data used in the study, is available on Google Drive at https://drive.google.com/file/d/1ZoafhYUP-DO4Hd_4A_v7mbQLjN3JPzJv/view?usp=sharing. The PostgreSQL database can be restored using the pg_restore command.</p>
Survey data on the perceptions and impacts of gender inequality in the geosciences
<p>The data stem from an anonymous, online survey conducted from March 25 to April 11, 2018 using Google Surveys. The link to the survey was distributed by the authors via emails and social media (Twitter and Facebook). Among the 1415 participants, we analyzed the responses of those who identified as either female or male (leaving out seven non-binary respondents due to the small sample size), and currently work in academia (i.e., universities or research institutes, including emeritus and adjunct professors, research support staff, and research assistants). We thereby retained 1220 respondents with a gender distribution of 67.0% female to 33.0% male survey participants.</p> <p>Based on this survey, we published to following freely accessible article: Popp, A. L.; Lutz, S. R.; Khatami, S.; van Emmerik, T. H. M.; Knoben, W. J. M. (2019) A global survey on the perceptions and impacts of gender inequality in the earth and space sciences, <em>Earth and Space Science</em>, 6(8), 1460-1468, <a href="http://doi.org/10.1029/2019EA000706">doi:10.1029/2019EA000706</a>.</p>
Output pathway data for "Income and inequality pathways consistent with eradicating poverty"
<p>This is a dataset presenting GDP and Gini pathways developed for the publication of our article "Income and inequality pathways consistent with eradicating poverty" (forthcoming at ERL) under the SHAPE project (Sustainable development pathways achieving Human well-being while safeguarding the climate And Planet Earth).</p> <p>More info about the SHAPE project is <a href="https://shape-project.org/">here</a>.</p>
Rising Environmental Inequalities and Their Relationship to Racial and Socioeconomic Disparities in the US Southwest
<p><em><strong>Note: These datasets are part of our research, which was published in Environmental Science & Technology. </strong></em></p> <p><em>Please cite this paper when you use these datasets:</em></p> <p><strong><em>Yuanhui Zhu, Soe W. Myint, Danica Schaffer-Smith, Daoqin Tong, Yuyu Zhou, Yubin Li, and Rebecca L. Muenich. </em>Rising Environmental Inequalities and Their Relationship to Racial and Socioeconomic Disparities in the US Southwest, 2025, <em>Environmental Science & Technology. <a title="DOI URL" href="https://doi.org/10.1021/acs.est.4c14369">https://doi.org/10.1021/acs.est.4c14369</a></em></strong></p> <p><em><strong>For any questions or concerns, contact Yuanhui Zhu at <a href="mailto:jta90@txstate.edu">jta90@txstate.edu</a></strong></em></p> <p> </p> <p>This study aims to provide insight into the US Southwest social and environmental inequality problems by combining high-resolution ECOSTRESS data, including Land Surface Temperature (LST), Evaporative Stress Index (ESI), and actual Evapotranspiration (ETa) with sociodemographic data at the block group level acquired from US Census. ESI and ETa represent drought and consumptive water use, respectively. Further, disparities of environmental changes over the past two decades in connection with races/ethnicities are explored using Landsat-based LST and ET from 2000 to 2020 across major US Southwest cities in light of global climate changes. We narrow our investigations to the summer months, including June, July, and August, when environmental issues are more pronounced during the day, such as heat-related mortality and morbidity and higher water consumption.</p> <p>This dataset covers major US Southwest cities. The dataset includes social-environmental data at the block level and remotely sensed environmental data. The description for each individual dataset is listed as follows,</p> <p><em><strong>T01(T01_dataset_meta_race_ethnicity_economy_env_southwest.csv)</strong></em>: This dataset provides statistics on race/ethnicity, social economy, and arithmetic mean of environmental variables at the block group level.</p> <p><em><strong>T02</strong></em>: These datasets provide processed environmental data, including Daytime Land Surface Temperature (LST), Actual Evapotranspiration (ETa), and Evaporative Stress Index (ESI) images at 70m resolution.</p> <p><em><strong>T03</strong></em>: These datasets include annual Landsat-based summer LST, and ETa means at 30m resolution from 2000 to 2020. The images of LST and ETa in 2012 are missing because Landsat datasets in 2012 are unavailable. We collected all available Landsat images with cloud cover lower than 75% in the summer from 2000 to 2020 to produce the dataset. The high-resolution (30m) LST and ETa are processed on Google Earth Engine (GEE). There are a total of 8860 scenes (including Landsat 4, 5, 7, and 8) from 2000 to 2020 to cover all major US Southwest urban areas. We also include two sample images of 2020 LST and ETa for demonstration.</p> <p><em><strong>T04</strong></em>: These datasets contain LST and ETa Sen's slope results, which represent the median rate of change of LST and ETa from 2000 to 2020 on an annual basis.</p> <p><em><strong>T05</strong></em>: The boundary of the major US Southwest urban areas</p>
SMT-Solving Induction Proofs of Inequalities Benchmarking Repository
<p>This repository contains the full list of files and the benchmarking results that were used in the benchmarking processes described in the paper:<br> A.K. Uncu, J.H. Davenport and M. England. "SMT-Solving Induction Proofs of Inequalities". Proceedings of the 7th International Workshop on Satisfiability Checking and Symbolic Computation (SC^2 2022). </p> <p>The files are split in three branches. The Mathematica and Maple files include the calls that were made to the respective computer algebra systems, and the smt2 files are the ones used by the considered SMT solvers: Z3, CVC5 and Yices.</p> <p>The Benchmarking Results cvc has the results. The columns record the file names, the satisfiability outcome of the calls, then the times (in seconds) of the respective programmes. Any empty box (which the Maple:-RegularChains column has) would mean that the implementation does not accept that sort of input (this is due to rational functions - see the paper for details). Any time over 1200 seconds would mean that the program times out and the outcome of the question was not found in the given time.</p> <p> </p> <p> </p>
PRODIGEES Research Stay in Brazil: Knowledge production, inequalities, colonialism
<p>Originally published on YouTube: https://www.youtube.com/watch?v=V-S6onyof9E</p> <p>Researcher Ramona Hägele about her research stay at FGV (Fundação Getulio Vargas) in Rio de Janeiro, Brazil. FGV and IDOS are partner in PRODIGEES: The European Union funded project enables researcher to work on digitalisation and sustainability.</p> <p>Wissenschaftlerin Ramona Hägele berichtet über ihren Aufenthalt an der Fundação Getúlio Vargas (FGV) in Rio de Janeiro, Brasilien. FGV und IDOS sind Partner beim Projekt PRODIGEES. Das von der Eurpäischen Union geförderte Projekt ermöglicht es Wissenschaftler*innen, zu den Themen Digitalisierung und Nachhaltigkeit zu forschen.</p>
RESISTIRÉ Dataset: Narratives on inequalities caused by policy and societal responses to Covid-19 in Europe – second cycle
<p>This dataset contains 295 narratives, including keywords and specifically telling quotes. It is the second of three sets of narratives collected and analysed in RESISTIRÉ. The narrative interviews were conducted in national languages by the consortium partners and a network of national researchers covering the EU27 (except Malta), and Iceland, Serbia, and the UK in December 2021-February 2022. Narrative interviewing is a qualitative research method that involves inviting participants to tell their own stories and experiences in their own words. The technique is used to collect and share a person’s story which entails both a research methodology and a mechanism for storytelling i.e. both a way of telling a story, and a way of knowing. Narratives as a technique can make visible how multiple sources of inequalities intersect, as well as the situational and contextual nature of inequalities from a single person’s perspective. </p>
RESISTIRÉ Dataset: Narratives on inequalities caused by policy and societal responses to Covid-19 in Europe – third cycle
<p>This dataset contains 289 narratives, including keywords and specifically telling quotes. It is the third of three sets of narratives collected and analysed in RESISTIRÉ. These narrative interviews were conducted in national languages by the consortium partners and a network of national researchers covering the EU27 (except Malta), and Iceland, Serbia, and the UK in September-December 2022.<a href="#_ftn1">[1]</a> Narrative interviewing is a qualitative research method that involves inviting participants to tell their own stories and experiences in their own words. The technique is used to collect and share a person’s story which entails both a research methodology and a mechanism for storytelling i.e. both a way of telling a story, and a way of knowing<em>. </em>Narratives as a technique can make visible how multiple sources of inequalities intersect, as well as the situational and contextual nature of inequalities from a single person’s perspective.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> For an analysis of the narratives, see Sandström, L., Callerstig, A-C., Strid, S., Lionello, L., & Rossetti, F. (2023). RESISTIRE D4.3 Summary report on qualitative indicators - cycle 3. Zenodo. <a href="https://doi.org/10.5281/zenodo.7708724">https://doi.org/10.5281/zenodo.7708724</a>.</p>
Inequalities in noise will affect urban wildlife
<p>Understanding the extent to which systemic biases influence local ecological communities is essential for developing just and equitable environmental practices. With over 270 million people across the United States living in urban areas, understanding the socio-ecological consequences of racially-targeted zoning, such as redlining, provides crucial information for urban planning. There is a growing body of literature documenting the relationships between redlining and disparities in the distribution of environmental harms and goods, including inequities in green space cover and pollutant exposure. Yet, it remains unknown whether noise pollution is also inequitably distributed, and whether inequitable noise is an important driver of ecological change in urban environments. We conducted 1) a spatial analysis of urban noise to determine the extent to which noise overlaps with the distribution of redlining categories and 2) a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence that noise is inequitably distributed in cities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife. These findings lay a foundation for future research that advances acoustic and urban ecology by centering equity and challenging systems of oppression.</p>
The violation of Bell inequality in frustrated interference
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.