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FULFILL dataset round 2 France
<p>This dataset and codebook correspond to the second round of survey data gathered in France in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. </p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained.</p> <p>This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</p> <p>New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.</p>
Dataset: Electrolyte-dependent deposition morphology on magnesium metal utilizing MeMgCl, Mg[B(hfip)4]2 and Mg(HMDS)2–2AlCl3 electrolytes
<p>This is a collection featuring the data generated and used within the paper: 'Electrolyte-dependent deposition morphology on magnesium metal utilizing MeMgCl, Mg[B(hfip)4]2 and Mg(HMDS)2–2AlCl3 electrolytes'. The deposition behavior of two state-of-the-art electrolytes, magnesium tetrakis(hexafluoroisopropyloxy)borate (Mg[B(hfip)~4~]~2~) in dimethoxyethane (DME) and magnesium bis(hexamethyldisilazide) with two equivalents of aluminum chloride (Mg(HMDS)~2~-2AlCl~3~) in tetrahydrofuran (THF) was investigated. Using symmetric flooded magnesium-magnesium cells with different electrolyte concentrations and current densities the deposition process was monitored optically in-situ by a video microscope. The depositions were characterized by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) and compared to depositions from methylmagnesium chloride (MeMgCl) in THF, known for its dendritic growth. In this work, MeMgCl showed unidirectional growth and for the harshest applied conditions, mossy depositions, but no branching dendrites as reported in previous literature. Mg[B(hfip)~4~]~2~ and Mg(HMDS)~2~-2AlCl~3~ did not show the formation of dendrites or a dendrite preform but also did not result in a desired smooth layer but in spherical depositions. For the Mg[B(hfip)~4~]~2~ electrolyte, the influence of magnesium borohydride (Mg(BH~4~)~2~) as an additive was additionally tested resulting in a more planar growth.</p>
Investigating the conformal behaviour of SU(2) with one adjoint Dirac flavor --- data release
<p>This dataset collects data and analysis results for non-perturbative lattice field theory calculations investigating the SU(2) gauge theory with one Dirac fermion in the adjoint representation. More detailed information is included in the file README_datapackage.md</p>
Data for SARS-CoV-2 Reinfection Trends in South Africa: Monthly Report (2022-12-07)
<p>This version contains a single file, with time series data for the most recent <a href="https://www.nicd.ac.za/diseases-a-z-index/disease-index-covid-19/surveillance-reports/sarscov2-reinfection-trends-in-south-africa-monthly-report/">monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa</a>:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> </ul> <p>Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data.</p> <p> </p> <p>Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. <a href="https://www.science.org/doi/10.1126/science.abn4947">Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa</a>. DOI: 0.1126/science.abn4947</p> <p>For code and more details see: <a href="https://github.com/jrcpulliam/reinfections/releases/tag/v3.0">https://github.com/jrcpulliam/reinfections/releases/tag/v3.0</a> or <a href="https://zenodo.org/record/6108448">10.5281/zenodo.6108448</a></p> <p>The version of this data set associated with the publication (available via the links above) included the following files:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> <li><code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or >0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>)</li> <li><code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript)</li> <li><code>sim_90_null.RDS</code> - simulation results (as used in the manuscript)</li> <li><code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)</li> </ul>
QPR: Datasets version 2
<p>Se publican dos archivos. </p> <ol> <li> <p>QPR_datacurada_feb23.xls: Dataset generado con la plataforma de Ciencia Ciudadana Social ¿Qué Pasa, Riachuelo? (QPR) versión alfa. Publicamos aquellos datos que pudieron ser curados en instancia de taller con co-investigadores. </p> </li> </ol> <ol> <li> <p>QPR_metadata_feb23.csv: Metadatos completos del set de datos que se genera en los siete formularios presentes en ¿Qué Pasa, Riachuelo? (QPR) </p> </li> </ol> <p><strong> <br> Proyecto CoAct: </strong></p> <p>CoAct es un proyecto global financiado por la Unión Europea que aborda problemas complejos asociados al empleo juvenil, la salud mental y la justicia ambiental mediante herramientas de ciencia ciudadana social. </p> <p>La ciencia ciudadana social se entiende en este contexto como investigación participativa co-diseñada e impulsada por grupos de personas que comparten una preocupación social. Esta metodología busca generar instancias que les permita a las comunidades tener una participación activa en la investigación, desde el diseño hasta la interpretación de los resultados y su transformación en acciones concretas. Las personas actúan así como co-investigadoras reconociendo sus competencias en el campo de aplicación. </p> <p><strong>Proyecto CoAct Justicia Ambiental </strong></p> <p>En la Cuenca Matanza-Riachuelo, un grupo de 56 co-investigadores trabajando en conjunto con el Centro de Investigaciones para la Transformación (CENIT) de la Universidad Nacional de San Martín (UNSAM) y la Fundación Ambiente y Recursos Naturales (FARN) y con la ayuda de muchas otras personas que en distintos momentos contribuyeron con el proyecto, se co-diseñó la plataforma ¿Qué pasa, Riachuelo?. Los co-investigadores son personas que tienen un conocimiento cercano de los problemas socio-ambientales de la cuenca porque viven o trabajan allí y están interesadas en su transformación. Durante talleres realizados durante los años 2020 y 2021 se definieron los temas, se co-diseñaron los formularios para generar datos y las principales funcionalidades de la plataforma. Se puede conocer más información sobre este proceso en <a href="https://farn.org.ar/coact-justicia-ambiental/">https://farn.org.ar/coact-justicia-ambiental/</a> o en <a href="https://coactproject.eu/">https://coactproject.eu/</a> </p> <p>La plataforma QPR recoge información sobre tres temas: calidad de agua, áreas naturales y relocalización y reurbanización. Describimos a grandes rasgos el contenido asociado a cada tema </p> <p>Calidad del agua: Información sobre la calidad observable del agua (e.g turbidez, color, olor, presencia de objetos flotantes, vegetación en la ribera y cauce, fauna, proximidad a posibles fuentes de contaminación y otros) y situaciones hidrometeorológicas (e.g. niveles del agua y situación climática en los días previos al reporte). </p> <p>Áreas naturales: Información sobre visitas a las áreas naturales Cuenca Matanza Riachuelo. Se informan usos, actividades u observaciones del patrimonio natural y cultural del área. Asimismo, se recoge información sobre valoraciones y amenazas que existen en las áreas naturales de la cuenca Matanza-Riachuelo y sobre la experiencia relacionada con su protección </p> <p>Relocalización y reurbanización: Información sobre los avances en el proceso de relocalización y reurbanización de villas y asentamientos de la cuenca Matanza-Riachuelo a nivel de barrio o complejo habitacional. Mudanzas, obras de vivienda, obras de servicios públicos, celebración de mesas de trabajo, acceso a la información e identificación de principales problemáticas a atender en el proceso. Además, también existe la posibilidad de compartir información sobre la experiencia de participación en las mesas de trabajo barriales por la relocalización o reurbanización como: fecha, lugar, participantes, presencia de autoridades, temas tratados, registro de la mesa, posibilidades de participación, fecha de la próxima reunión, propuesta de otros temas a tratar. </p> <p>Para cada uno de estos temas existen dos formularios, uno estructurado que no requiere registro previo y otro con campos abiertos donde se puede agregar relatos, imágenes y documentos que requiere registro previo. Además, existe un séptimo formulario que requiere registro previo para compartir novedades de actividades relacionadas con la justicia ambiental en la cuenca. En estos videos tutoriales se puede encontrar información sobre cada uno de los formularios de QPR. </p> <p>CoAct Justicia Ambiental | ¿Cómo compartir experiencias sin estar registrado en QPR? </p> <p><a href="https://youtu.be/7zhWlEOi_Ic">https://youtu.be/7zhWlEOi_Ic</a> </p> <p>CoAct Justicia Ambiental | ¿Cómo compartir experiencias registrándose en QPR? </p> <p><a href="https://youtu.be/YkjqRJvB4ZM">https://youtu.be/YkjqRJvB4ZM</a> </p> <p><strong>Set de datos y metadatos </strong></p> <p>El set de datos compartido a modo de ejemplo es sobre calidad de agua, y contiene información generada tanto por el formulario estructurado como por el abierto durante un taller con co-investigadores. Ver. QPR_datacurada_feb23.csv </p> <p>Compartimos también el archivo QPR_metadata_feb23.xls que contiene la metadata completa de los siete formularios. La primera columna del archivo de metadatos “Nombre de la variable” tiene el nombre de la variable que encabeza el set de datos. La segunda columna “Pregunta en el formulario” contiene la pregunta completa tal como está formulada en la plataforma. La tercera columna “Etiqueta en el mapa” contiene el título que se utiliza en la visualización de las respuestas en el mapa. La cuarta columna “Categorías o formato del dato” lista las categorías disponibles como respuesta en las preguntas estructuradas y en el caso que sea otro tipo de dato, por ejemplo, texto, fecha, coordenada, etc. se describe el formato. La quinta columna “Tipo de respuesta” describe el tipo de respuesta asociada a esa variable, por ejemplo, si es de respuesta única o múltiple, esta información es útil sobre todo para las variables categóricas. Y finalmente una sexta columna “Obligatoria u opcional” indica si la pregunta es de respuesta obligatoria u opcional. </p> <p><strong>Agradecimientos </strong></p> <p>El proyecto CoAct ha recibido financiamiento de la del programa Horizonte 2020 de la Unión Europea bajo el acuerdo de subvención número 873048. Expresamos nuestro agradecimiento a los co-investigadores por su conocimiento y el tiempo invertido en el co-diseño de la herramienta. En especial agradecemos a la Biblioteca Popular Sarmiento de Valentín Alsina por su rol en organizar salidas a campo para el tema calidad del agua que ayudaron a generar el presente set de datos. </p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>English Description</strong></p> <p>Two files are published. </p> <ol> <li> <p>QPR_datacurada_feb23.xls: Dataset generated with the Citizen Social Science platform ¿Qué Pasa, Riachuelo? (QPR) alpha version. We publish the data that were curated in a workshop with co-researchers. </p> </li> </ol> <ol> <li> <p>QPR_metadata_feb23.csv: Complete metadata of the dataset that are generated with the seven forms available in ¿Qué Pasa, Riachuelo? (QPR) </p> </li> </ol> <p><strong>CoAct Project: </strong></p> <p>CoAct is a global EU-funded project that addresses complex issues associated with youth employment, mental health and environmental justice using social citizen science tools. </p> <p>Citizen social science is understood in this context as participatory research co-designed and driven by groups of people who share a social concern. This methodology seeks to generate instances that allow communities to have an active participation in research, from design to interpretation of results and their transformation into concrete actions. People thus act as co-researchers, and their competences and experience in the field of application is recognised. </p> <p><strong>CoAct Environmental Justice Project </strong></p> <p>The platform ¿Qué pasa, Riachuelo? (QPR) was co-designed in the Matanza-Riachuelo Basin with a group of 56 co-researchers working together with the Research Center for Transformation (CENIT) of the National University of San Martín (UNSAM) and the Environment and Natural Resources Foundation (FARN) and with the help of many other people who contributed to the project in different moments. Co-researchers are people who have local knowledge of the socio-environmental problems of the basin because they live or work there and are interested in the transformation of these realities. During workshops held during the years 2020 and 2021, the themes, the forms to generate data and the main functionalities of the platform were co-designed. More information about this process can be found in <a href="https://farn.org.ar/coact-justicia-ambiental/">https://farn.org.ar/coact-justicia-ambiental/</a> or <a href="https://coactproject.eu/">https://coactproject.eu/</a> </p> <p>The QPR platform collects information on three themes: water quality, natural areas and resettlement and redevelopment. We broadly describe the content associated with each theme </p> <ul> <li> <p>Water quality: Information on observable parameters of water quality (e.g. turbidity, colour, odour, presence of floating objects, vegetation on the riverbank and riverbed, fauna, proximity to possible sources of pollution and others) and hydrometeorological situations (e.g. water levels and weather situation in the days prior to the report). </p> </li> <li> <p>Natural areas: Information on visits to the natural areas of the Matanza-Riachuelo Basin. Uses, activities, observations of natural and cultural patrimony of area are reported. Likewise, information is collected on values and threats that exist in the natural areas of the Matanza-Riachuelo Basin and on the experiences related to their protection. </p> </li> <li> <p>Resettlement and redevelopment: Information on the progress in the processes of resettlement and redevelopment of slums and settlements in the Matanza-Riachuelo Basin at the neighbourhood or housing complex level. Removals, housing works, public service works, holding of work tables, access to information and identification of main problems to be addressed in the process. In addition, there is also the possibility of sharing information about the experience of participation in the neighbourhood’s work tables for resettlement or redevelopment such as: date, place, participants, presence of authorities, discussed topics, documentation of the table’s meetings, possibilities of participation, date of the next meeting, proposals of other topics to be discussed. </p> </li> </ul> <p>For each of these themes there are two forms: one structured that does not require prior registration and another that requires prior registration with open fields where stories, images and documents can be added. In addition, there is a seventh form that requires prior registration that allows to share news of activities related to environmental justice in the basin. In these tutorial videos you can find information about each of the QPR forms. </p> <ul> <li> <p>CoAct Environmental Justice | How to share experiences without being registered in QPR? <a href="https://youtu.be/7zhWlEOi_Ic">https://youtu.be/7zhWlEOi_Ic</a> </p> </li> <li> <p>CoAct Environmental Justice | How to share experiences by registering with QPR <a href="https://youtu.be/YkjqRJvB4ZM">https://youtu.be/YkjqRJvB4ZM</a> </p> </li> </ul> <p> </p> <p><strong>Data and metadata set </strong></p> <p>The dataset shared as an example is on water quality, and contains information generated by both the structured and open forms during a workshop with co-researchers. See. QPR_datacurada_feb23.csv </p> <p> </p> <p>We also share the QPR_metadata_feb23.xls file containing the complete metadata of the seven forms. The first column of the metadata file "Variable Name" has the name of the variable that heads the dataset. The second column "Question in the form" contains the complete question as it is formulated on the platform. The third column "Label on the map" contains the title that is used in the display of responses on the map. The fourth column "Categories or data format" lists the categories available as an answer in the structured questions and in the case that it is another type of data, for example, text, date, coordinate, etc. the format is described. The fifth column "Type of response" describes the type of response associated with that variable, for example, if there are multiple options. This information is useful especially for categorical variables. And finally a sixth column "Mandatory or optional" indicates whether the question is mandatory or optional. (Note that the metadata fields have been translated from Spanish here for description purposes) </p> <p><strong>Acknowledgements </strong></p> <p>The CoAct project has received funding from the European Union's Horizon 2020 programme under grant agreement number 873048. We especially thank the co-researchers for their knowledge and the time invested in co-designing the tool. We thank especially the Community Library Sarmiento of Valentin Alsina for their role in organising fieldwork activities in the water quality theme which helped to create this dataset </p>
Dataset for the IntoValue 1 + 2 studies on results dissemination from clinical trials conducted at German university medical centers completed between 2009 and 2017
<p>The IntoValue dataset contains clinical trials conducted at one of 35 German UMCs and registered on ClinicalTrials.gov or the German Clinical Trials Registry (DRKS). All trials were reported as complete between 2009 and 2017 on the trial registry at the time of data collection. The dataset also includes a results publication found via manual searches; if multiple results publications were found, the earliest was included.</p> <p>Trials were associated with a German UMC by searching for trials with a UMC listed as responsible party or lead sponsor, or with a principle investigator (PI) from a UMC ('lead_city'). Version 1 additionally includes trials with a UMC only as a facility (`facility_city`). A lookup table of regular expressions used to identify German UMCs is available at <a href="https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv">https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv</a>.</p> <p>Trials include all interventional studies and are not limited to investigational medical product trials, as regulated by the EU's Clinical Trials Directive or Germany's Arzneimittelgesetz (AMG) or Novelle des Medizinproduktegesetzes (MPG).</p> <p>DRKS data were searched (pre-filtered for completion years and study status as well as Germany as 'Country of recruitment') and downloaded as CSVs from the DRKS website (<a href="https://www.drks.de/">https://www.drks.de/</a>). ClinicalTrials.gov data were downloaded downloaded as pipe files from Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) (<a href="https://aact.ctti-clinicaltrials.org/pipe_files">https://aact.ctti-clinicaltrials.org/pipe_files</a>). DRKS and ClinicalTrials.gov use different terminology for various trial aspects, such as phase and masking; these different levels are captured in the data dictionary as `levels_drks` and `levels_ctgov`. For later analyses requiring parity across registries, levels for some variables were collapsed and a lookup table is provided in `iv_data_lookup_registries.csv`.</p> <p>These data were generated and used for two publications (Wieschowski et al., 2019; Riedel et al. 2021) and therefore comprises two versions (indicated as `iv_version`).</p> <p>For version 1, registry data was collected on April 17, 2017 from ClinicalTrials.gov and on July 27, 2017 for DRKS and was limited to trials with a completion date on DRKS and primary completion date on ClinicalTrials.gov between 2009 and 2013. Version 1 manual searches for results publications were conducted from 2017-07-01 to 2017-12-01.<br> For version 2, registry data was collected on June 3, 2020 and was limited to trials with a completion date on DRKS and ClinicalTrials.gov between 2014 and 2017. Version 2 manual searches for results publications were conducted from 2020-07-01 to 2020-09-01.</p> <p>Raw registry data for versions 1 and 2 is available in `raw-registries.zip`.</p> <p>Publication identifiers (DOI, PMID, URL) were manually entered during the publication search and then further enhanced using the API of Internet Archive's open-source Fatcat catalog of research publications, to add PMIDs based on DOIs, and vice versa.</p> <p>Manual search steps differed slightly in the two versions and are indicated and described in `identification_step`.<br> Version 1 includes trials with a German UMC as either a `lead_city` or a `facility_city`, whereas version 2 is limited to trials a German UMC as a `lead_city`.</p> <p>Each row indicates a single trial registration. Due to changes in completion dates, some trials are duplicated between versions as indicated in `is_dupe`. Cross-registered trials were manually deduplicated, and some cross-registered duplicates remain (e.g., DRKS00004156 and NCT00215683) and are not indicated in the dataset.</p> <p>All dates are provided as `yyyy-mm-dd`.</p> <p>Additional documentation on each variable (type, description, levels) is provided in `iv_data_dictionary.csv`.</p> <p>Additional information on the project and methods for generating the dataset is available in associated publications and at the project's OSF page (<a href="https://osf.io/98j7u/">https://osf.io/98j7u/</a>). Code for the project is available at <a href="https://github.com/quest-bih/IntoValue2">https://github.com/quest-bih/IntoValue2</a>.</p> <p><strong>References:</strong></p> <p>Wieschowski, S., Riedel, N., Wollmann, K., Kahrass, H., Müller-Ohlraun, S., Schürmann, C., Kelley, S., Kszuk, U., Siegerink, B., Dirnagl, U., Meerpohl, J., & Strech, D. (2019). Result dissemination from clinical trials conducted at German university medical centers was delayed and incomplete. Journal of Clinical Epidemiology, 115, 37–45. <a href="https://doi.org/10.1016/j.jclinepi.2019.06.002">https://doi.org/10.1016/j.jclinepi.2019.06.002</a></p> <p>Riedel, N., Wieschowski, S., Bruckner, T., Holst, M. R., Kahrass, H., Nury, E., Meerpohl, J. J., Salholz-Hillel, M., & Strech, D. (2021). Results dissemination from completed clinical trials conducted at German university medical centers remained delayed and incomplete. The 2014-2017 cohort. Journal of Clinical Epidemiology, 0(0). <a href="http://doi.org/10.1016/j.jclinepi.2021.12.012">https://doi.org/10.1016/j.jclinepi.2021.12.012</a><br> </p>
Dataset for "Magnetic catalysis in the (2+1)-dimensional Gross-Neveu model"
<p>We study the Gross-Neveu model in 2 + 1 dimensions in an external magnetic field B. We<br> first summarize known mean-field results, obtained in the limit of large flavor number N f , before<br> presenting lattice results using the overlap discretization to study one reducible fermion flavor,<br> N f = 1. Our findings indicate that the magnetic catalysis phenomenon, i.e., an increase of the chiral<br> condensate with the magnetic field, persists beyond the mean-field limit for temperatures below the<br> chiral phase transition and that the critical temperature grows with increasing magnetic field. This<br> is in contrast to the situation in QCD, where the broken phase shrinks with increasing B while the<br> condensate exhibits a non-monotonic B-dependence close to the chiral crossover, and we comment on<br> this discrepancy. We do not find any trace of inhomogeneous phases induced by the magnetic field.</p> <p> </p> <p>If you use this data, please cite the corresponding paper:<br> https://doi.org/10.48550/arXiv.2302.05279 (or better the not-yet-existing published version)</p>
2-Dimensional habitat files for 47 representative marine species
<p><strong>2D marine species habitats in NetCDF format on 0.5*0.5 degree global regular grid.</strong></p> <p>Based on Close et al. (2006) and converted from .CSV format. </p> <p>Filename is in format 'presence_speciesnumber.nc' where species numbers are listed in the README.txt file (identifier for each species). The README.txt file further contains each species' species_group which is the assigned depth group for each species (1=0-200m epipelagic, 2=200-1000m mesopelagic, 3=sea floor demersal) and species_name which is the Latin name of each species with underscore in between.</p> <p>The species occurs where the variable 'presence' equals 1 (in the accompanying paper we assume this to be the 1995-2014 climatological mean distribution).</p> <p>In the NetCDF files, the variable 'presence' has as an attribute 'species' which contains the Latin species name without underscore.</p>
Dataset for "The magnetized (2+1)-dimensional Gross-Neveu model at finite density"
<p>We perform a lattice study of the (2+1)-dimensional Gross-Neveu model in a background magnetic field <em>B</em> and at non-zero chemical potential <em>μ</em>. The complex-action problem arising in our simulations using overlap fermions is under control. For <em>B</em>=0 we observe a first-order phase transition in <em>μ</em> even at non-vanishing temperatures. Our main finding, however, is that the rich phase structure found in the limit of infinite flavor number <em>N</em>f is washed out by the fluctuations present at <em>N</em>f=1. We find no evidence for inverse magnetic catalysis, i.e., the decrease of the order parameter of chiral symmetry breaking with <em>B</em> for <em>μ</em> close to the chiral phase transition. Instead, the magnetic field tends to enhance the breakdown of chiral symmetry for all values of <em>μ</em> below the transition. Moreover, we find no trace of spatial inhomogeneities in the order parameter. We briefly comment on the potential relevance of our results for QCD.</p> <p>If you use this data, please cite the corresponding paper:<br> https://doi.org/10.48550/arXiv.2304.14812 (or better the not-yet-existing published version)</p>
Nutrient mineralization from green leaves in litterbags of three mesh sizes in the LUQ-LTER Canopy Trimming 2 Experiment
Hurricanes generate disturbances in forests that alter physicochemical characteristics of the habitat by opening the canopy and depositing fresh wood and leaves. Our objectives were to evaluate the effects of simulated hurricane driven changes to nutrient fluxes from litter to soil immediately following canopy disturbance. This study used three complete replicated blocks with two canopy treatments, control and trim+debris. Measurements were made in three 5 x 5 m subplots within 20 x 20 m plots nested in the 30 x 30 m treatment areas. Anion and cation resin membranes were inserted into the fermentation layer at the litter-soil interface and retrieved after one week. The measurement intervals were 2-4 weeks before canopy trimming, 0-1, 1-2, 2-3 and 4-5 weeks after trimming. Nutrient mineralization differed significantly between control and trim+detritus. Total N and P fluxes occurred at 4-5 weeks after canopy trimming. Litter decomposition depends primarily on the interaction among climate, litter quality and biota, so consequently any change in habitat will result in changes in these factors. Our objectives were to evaluate the effects of hurricane driven changes to forests on green litter decomposition, invertebrate communities and nutrient mineralization. This study used three complete replicated blocks with two canopy treatments, control and trim+debris. Measurements were made in three 5 x 5 m subplots within 20 x 20 m plots nested in the 30 x 30 m treatment areas. Green leaves were enclosed in litterbags of three different mesh sizes in each subplot. Litterbags were retrieved after 21, 35, 84 and 168 days; decomposer fauna was extracted and identified, mineralized nutrients were measured using ion resin membranes, and weight loss was determined. Arthropod abundance differed significantly through time. In addition, the number of arthropod taxonomic groups and nutrient mineralization differed significantly between control and trim+detritus, and nutrient mineralizat
Zooplankton collected with a 2-m, 700-um net towed from surface to 120 m, aboard Palmer Station Antarctica LTER annual cruises off the western Antarctic peninsula, 2009 - 2024.
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton and thus provide a link between primary producers and higher trophic levels. Zooplankton density and biovolume were determined at grid stations on the annual LTER cruises along the western Antarctic Peninsula (WAP). Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Typically, zooplankton were collected with a 2x2 meter, 700um mesh net fitted with a flow meter and towed obliquely to 120m. Zooplankton distributions vary spatially due to water column characteristics, which affect their predators' distributions. As climate change continues to affect the WAP, the relative abundance of the various zooplankton components can also be expected to change.
Standard body length of Euphausia superba collected with a 2-m, 700-um net towed from surface to 120 m, collected aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 2009 - 2024.
Antarctic krill, Euphausia superba, are a critical food-web link between phytoplankton primary production and higher trophic levels, such as whales, penguins, and seals. Krill standard length was measured from LTER zooplankton tows along the western Antarctic Peninsula. Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Length data provides estimates of age-class abundance and recruitment. Climate-induced changes in krill recruitment are an important consideration in the management and modelling of krill populations.
Length of Salpa thompsoni collected with a 2-m, 700-um net towed from surface to 120 m, collected aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 2009 - 2024.
Salps (Salpa thompsoni) are conspicuous gelatinous zooplankton capable of rapid population increases, enabling them to respond quickly to unpredictable phytoplankton blooms common in the Antarctic. Body length was measured on salps collected from LTER zooplankton tows along the western Antarctic Peninsula. Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Salps have amongst the highest filtration rates of all zooplankton, and package their waste into large, fast sinking fecal pellets. These pellets provide a mechanism to export carbon fixed in the surface waters into the deep ocean. Since filtration rates and pellet size are positively related to the size of a salp, population estimates of grazing and exported carbon can be determined through length data.
ReFAB 10,000 Year Statistical Estimate of Aboveground Woody Biomass, Midwest US, Level 2
How terrestrial biomass changed before the advent of industrial society is a major gap in our understanding of the Earth's carbon cycle. Here, we archive data used to reconstruct 10,000 years of aboveground woody biomass across the US Upper Midwest using statistical models based on historical forest surveys and fossil pollen assemblages. From our analyses we document a 5,000 year long carbon sink into vegetation, primarily caused by the range expansion of two late-successional species into the region during the late Holocene. The importance of such large slow-growing tree species in storing carbon during the pre-industrial past argues for protecting similar species in wild forests today. This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.
Cognitive control of sensory pain encoding in the pregenual anterior cingulate cortex. d1 - decoder construction in day 1, d2 - adaptive control in day 2.
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HD-EEGtask(Dataset 2)
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EEG: Probabilistic Learning with Affective Feedback: Exp #2
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Galaxy Zoo 2: Images from Original Sample
<p>The Galaxy Zoo team regularly receives requests for subject images for various versions of Galaxy Zoo, in order to facilitate other investigations, e.g. machine learning projects. This repository is an updated attempt to provide those in a way that is useful to the wider community.</p> <p>The images here are meant to be used with the data tables available at <a href="http://data.galaxyzoo.org">data.galaxyzoo.org</a>. They are the "original" sample of subject images in Galaxy Zoo 2 (Willett et al. 2013, MNRAS, 435, 2835, DOI: <a href="https://doi.org/10.1093/mnras/stt1458">10.1093/mnras/stt1458</a>) as identified in Table 1 of Willett et al. and also in Hart et al. (2016, MNRAS, 461, 3663, DOI: <a href="https://doi.org/10.1093/mnras/stw1588">10.1093/mnras/stw1588</a>). The original GZ2 subjects also gave the option to view an inverted version of the subject image; these inverted images are not provided but are easily reproducible from the included subject images. </p> <p><strong>If you use this dataset, please cite</strong> Willett et al. (2013) as the general data release and <em>also</em> cite the DOI for this dataset; if you use the updated debiased tables from Hart et al. (2016) please cite that as well.</p> <p>There are 243,434 images in total. This is off by about 0.08% from the total count in the tables - it's not clear what the cause of the discrepancy is, but we don't think the missing images have any particular sampling bias, so this sample should be useful for research.</p> <p>The images are available in a single zip file (<strong>images_gz2.zip</strong>).</p> <p>The most recent and reliable source for morphology measurements is "GZ2 - Table 1 - Normal-depth sample with new debiasing method – CSV" (from Hart et al. 2016), which is available at <a href="https://data.galaxyzoo.org">data.galaxyzoo.org</a>. To cross-reference the images with Table 1, this sample includes another CSV table (<strong>gz2_filename_mapping.csv</strong>) which contains three columns and 355,990 rows. The columns are:</p> <ul> <li><strong>objid</strong>: the Data Release 7 (DR7) object ID for each galaxy. This should match the first column in Table 1.</li> <li><strong>sample</strong>: string indicating the subsampling of the galaxy. </li> <li><strong>asset_id</strong>: an integer that corresponds to the filename of the image in the zipped file linked above.</li> </ul> <p>As an example row:</p> <p>587722981742084144,original,16</p> <p>The galaxy is 587722981741363294, which is in Table 1 and was identified by GZ2 volunteers as a barred spiral galaxy with a mild bulge and two tightly-wound arms (morphology='Sc2t'). It is in the original GZ2 sample, and can be found in the zipped file as 16.jpg. </p> <p>The overlap between the set of images, the attached table, and Table 1 is not 100%; there are a few rows in the tables that don't have a corresponding image. Again, it's not clear what the exact reason is for this, but we suggest just dropping any missing rows/images from your analysis unless you have a need for analyzing specific subjects. If you do need a 100% complete sample, you can obtain the missing images directly from SDSS. </p> <p>Based on spot checks the mappings between asset ID and DR7 object ID appear correct, but we strongly suggest that you pick some random images and verify on your own that the image seems to match the label/classifications that are listed in Table 1. </p> <p>If you have any issues using this dataset, please contact the Galaxy Zoo team, in particular Brooke Simmons (b.simmons@lancaster.ac.uk). Should Dr Simmons be unavailable, try contacting Karen Masters or Chris Lintott.</p> <p>- the GZ team, 5 Dec 2019<br> </p>
A 2-minute rainfall (12 locations) and discharge time series at the Vallon de Nant catchment, Switzerland, for 2018 summer seasons
<p>The data set contains rainfall time series within the experimental 13.4 km² Vallon de Nant catchment, Switzerland (Michelon et al., 2020), from June 30th to September 23rd 2018 at 12 locations. A network of <em>Pluvimate</em> drop-counting raingauges (www.driptych.com) measured continuously the rainfall intensity at a 2-minute resolution. Operation and characteristics of the raingauges are detailed in Benoit et al. (2018) and Michelon et al. (2020), and the rating curve is described by Ceperley et al. (2018).</p> <p>Description of the files:</p> <ul> <li><em><strong>data.csv</strong></em> contain the rainfall intensities for the observation period, along with the main river discharge measured at the <a href="https://map.geo.admin.ch/?lang=fr&topic=ech&bgLayer=ch.swisstopo.pixelkarte-farbe&layers=ch.swisstopo.zeitreihen,ch.bfs.gebaeude_wohnungs_register,ch.bav.haltestellen-oev,ch.swisstopo.swisstlm3d-wanderwege,KML%7C%7Chttps:%2F%2Fpublic.geo.admin.ch%2FaLKDanGXRPGMpB_D51f2Tg&layers_visibility=false,false,false,false,true&layers_timestamp=18641231,,,,&E=2574619.27&N=1122462.26&zoom=8">outlet</a> over the same 2-minutes time step as the rainfall intensity. We also provide areal rainfall intensity aggregated over the whole catchment:<br> Columns: <ul> <li>year [-]</li> <li>month [-]</li> <li>day [-]</li> <li>hour [-]</li> <li>minute [-]</li> <li>specific discharge 95% inf. [mm/day]: inferior values of the specific discharge (with 95% of confidence interval) over 2 minutes</li> <li>specific discharge 95% sup. [mm/day]: superior values of the specific discharge (with 95% of confidence interval) over 2 minutes</li> <li>specific discharge mean [mm/day]: mean value of the specific discharge over 2 minutes</li> <li>specific discharge median [mm/day]: median value of the specific discharge over 2 minutes</li> <li>P St. #X [mm]: rainfall amount measured at the station X over 2 minutes</li> <li>P stochastic mean [mm/h]: rainfall amount interpolated over the whole catchment over 2 minutes</li> <li>P stochastic std [mm/h]: standard deviation of the stochastic rainfall interpolation, over 2 minutes</li> </ul> </li> <li><strong><em>stations.csv</em></strong> describes the raingauge locations.<br> Columns: <ul> <li>Station ID [-]</li> <li>lon [WGS84]: decimal longitude of the station into WGS84</li> <li>lat [WGS84]: decimal latitude of the station into WGS84</li> <li>E [CH1903]: east coordinate into Swiss Coordinate System</li> <li>N [CH1903]: north coordinate into Swiss Coordinate System</li> <li>elevation [m asl]: altitude of the station in meters above the sea level</li> <li>data in 2017 [-]: flag if the station was working over the 2017 observation period</li> <li>data in 2018 [-]: flag if the station was working over the 2018 observation period</li> </ul> </li> <li><em><strong>rainfall_viewer.m</strong></em> is a <em>MatLab</em> script (created with <em>MatLab 2017b</em>) which allows the joint visualization of the rainfall intensities and river discharge. It produces a composite figure with the following plots: <ul> <li>On top the general hydrograph over the whole observation period [mm/day]. The red dashed lines mark out period that the other plots are focus on. The shaded orange areas correspond to when the river stage data was not available.</li> <li>Below, the zoomed hydrogram show a detailed view of the river discharge (and uncertainty). In case a river reaction is associated, the discharge event is marked out by red dashed lines. Between these vertical lines is drawn a line joining the initial and final baseflow, separating the discharge amount fed by the baseflow (under the line) to the fast runoff (over the line). The red square shows the center of mass of the fast runoff part.</li> <li>In the middle a zoomed magnification of the hydrograph that shows a detailed view of the discharge in the river [mm/day]. When a river response is associated, the discharge event is marked with dashed red lines. Between these vertical lines a line joining the initial and final baseflow is drawn, separating the discharge amount fed by the baseflow (under the line) to the fast runoff (over the line). The red square shows the center of mass of the fast runoff.</li> <li>At the bottom are shown the rainfall recorded by each of the 12 rain gauges (the y-axis scale between 2 stations is about 20 mm/h). The rainfall event is marked out by green dashed lines.</li> <li>Above is shown the rainfall amount (and uncertainty) interpolated over the catchment using the stochastic method. The rainfall event is marked out by green dashed lines.</li> <li>On the left, a map with the 12 raingauge locations show the total amount of rainfall recorded by each station during the event (a red cross shows missing data).<br> <br> It is possible to zoom in the plots by clicking with the left and right mouse buttons to define respectively the starting and ending of the visualization window. The middle button defines a third time reference used to identify rainfall intensity peaks or discharge peaks. Statistics concerning the visualization period are displayed on the MatLab console.<br> Pressing [enter] will save the figure into a PNG file named with the starting and ending dates of the visualization window.</li> </ul> </li> <li><strong><em>Q_stats.m </em></strong>is a MatLab function used by the main code rainfall_viewer.m</li> <li><strong><em>print_figure.m </em></strong>is a MatLab function used by the main code rainfall_viewer.m</li> <li><strong>data.mat</strong> is a MatLab data file with all data required by the main code rainfall_viewer.m</li> </ul>
Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention
<p>Single cell RNA seq datasets used for analysis in the Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.