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11,718 results for “life”
MATEdb2, a Collection of High-Quality Metazoan Proteomes across the Animal Tree of Life to Speed Up Phylogenomic Studies
<p>Recent advances in high-throughput sequencing have exponentially increased the number of genomic data available for animals (Metazoa) in the last decades, with high-quality chromosome-level genomes being published almost daily. Nevertheless, generating a new genome is not an easy task due to the high cost of genome sequencing, the high complexity of assembly, and the lack of standardized protocols for genome annotation. The lack of consensus in the annotation and publication of genome files hinders research by making researchers lose time in reformatting the files for their purposes but can also reduce the quality of the genetic repertoire for an evolutionary study. Thus, the use of transcriptomes obtained using the same pipeline as a proxy for the genetic content of species remains a valuable resource that is easier to obtain, cheaper, and more comparable than genomes. In a previous study, we presented the Metazoan Assemblies from Transcriptomic Ensembles database (MATEdb), a repository of high-quality transcriptomic and genomic data for the two most diverse animal phyla, Arthropoda and Mollusca. Here, we present the newest version of MATEdb (MATEdb2) that overcomes some of the previous limitations of our database: (i) we include data from all animal phyla where public data are available, and (ii) we provide gene annotations extracted from the original GFF genome files using the same pipeline. In total, we provide proteomes inferred from high-quality transcriptomic or genomic data for almost 1,000 animal species, including the longest isoforms, all isoforms, and functional annotation based on sequence homology and protein language models, as well as the embedding representations of the sequences. We believe this new version of MATEdb will accelerate research on animal phylogenomics while saving thousands of hours of computational work in a plea for open, greener, and collaborative science.</p>
Life Cycle Assessment Dataset For Haemodialysis in Modena
<p>The dataset provides a detailed breakdown of the processes and procedures involved in haemodialysis (HD) or haemodiafiltration (HDF) care, focusing on the operational pathways, roles, and activities required to deliver treatment effectively. It documents:</p> <ol> <li> <p><strong>Vascular Access Procedures</strong>: Data on patient assessments for vascular access (arteriovenous fistulas or central venous catheters), the surgical creation of access points, and ongoing monitoring of these accesses.</p> </li> <li> <p><strong>Dialysis Machine Preparation and Patient Connection</strong>: Information on the preparation of HD machines by nurses, the connection and disconnection of patients, and the operational setup for each session.</p> </li> <li> <p><strong>Routine and Emergency Monitoring</strong>: Insights into routine nephrologist visits during dialysis, monthly patient evaluations, and emergency examinations for complications like blood flow or infection issues.</p> </li> <li> <p><strong>Water and Dialysate Management</strong>: Records on the preparation, monitoring, and quality control of dialysis water and dialysate solutions.</p> </li> <li> <p><strong>Operational Tasks</strong>: Documentation of non-clinical work, such as machine and room turnover, and administrative responsibilities managed by nurses outside regular shifts.</p> </li> </ol> <p>This dataset captures the full workflow of HD care, emphasizing the procedural steps, resource allocation, and clinical oversight needed to ensure safe and effective treatment. It serves as a foundational resource for analyzing the environmental, resource, and operational impacts of haemodialysis care.</p>
Life Cycle Assessment Dataset for Kidney Care Environmental Optimisations within Haemodialysis
<p>This dataset supports a study on environmental optimizations in haemodialysis (HD) kidney care, focusing on reducing carbon emissions, water usage, and social impacts such as forced labour. It includes detailed analyses of interventions to improve sustainability across multiple domains:</p> <ol> <li> <p><strong>Travel Reduction</strong>: Data explores the impact of reducing patient travel distances by 10%, 50%, and 90%, highlighting significant greenhouse gas (GHG) emission savings (up to 2,540 kg CO2e per patient annually) and associated reductions in water usage and forced labour risks. Interventions include promoting home-based dialysis, telemedicine, and optimized patient facility allocation.</p> </li> <li> <p><strong>Water Management</strong>: The dataset documents innovations such as reclaiming reverse osmosis water for reuse, optimizing water treatment plant operations, and reducing water consumption during dialysis processes. Larger centres and daily operation schedules show better water efficiency compared to smaller, less frequent setups.</p> </li> <li> <p><strong>Waste Management</strong>: Data highlights strategies for diverting waste from clinical to domestic streams, recycling dialysis materials, and adopting advanced technologies like pyrolysis. These measures reduce the environmental and economic burden of waste disposal, including incineration costs.</p> </li> <li> <p><strong>Energy Optimizations</strong>: Included interventions cover energy-saving technologies such as heat exchangers in dialysis machines, solar panel installations, and IT system automation. Solar energy adoption demonstrates varied CO2e savings based on regional energy mixes.</p> </li> <li> <p><strong>Incremental Dialysis</strong>: Data supports the transition to incremental dialysis—starting with fewer weekly sessions—to preserve resources, reduce GHG emissions, and maintain residual kidney function, offering both environmental and clinical benefits.</p> </li> </ol> <p>Each intervention was assessed using Life Cycle Assessment (LCA) methodologies, with functional units based on annual HD use for one patient. Metrics include carbon dioxide equivalent emissions (CO2e), water deprivation, and forced labour hours, aligned with EU Product Environmental Footprint standards. The dataset provides comparative results to guide clinical sites in prioritizing high-impact interventions, offering actionable insights into sustainable HD care.</p>
Modeling Early Life Histories of Marine Organisms
<p>This is a recorded presentation to introduce students to ecosystem modeling. The presentation was developed for students of an early life histories class so discusses lagrangian individual-based modeling but the supporting material for understanding eulerian physical and lower trophic level models is also introduced.</p> <p>If you use part or all of this educational material as part of your lesson content it would be appreciated if you could inform the author (gagibson@alaska.edu) for tracking purposes.</p>
CE-MS data for Autonomous CE Mass-Spectra Examination (ACME) for the Ocean Worlds Life Surveyor (OWLS)
<p>These folders contain the original data used to develop the ACME software [1].</p> <p>The Golden and Silver dataset come from simulations. They underrepresent the complexity in the CE-MS observations but provide additional data with known peak locations and peak properties. For more information see [1]</p> <p>The Dev-, Train-, and Test-set contain CE-MS [2] observations of Mix25 (a standard set of 25 organic compounds relevant to astrobiology) and labels for peak locations from subject matter experts. </p> <p>The ACME software is available at: <br> https://github.com/JPLMLIA/OWLS-Autonomy </p> <p> </p> <p>When using the data please cite this dataset [3] and the two papers below. </p> <p>For further questions please reach out to:<br> Steffen Mauceri, Steffen.Mauceri@jpl.nasa.gov</p> <p> </p> <p>References:<br> [1] Mauceri, S., Lee, J., Wronkiewicz, M., et.al. (2022). Autonomous CE Mass-Spectra Examination (ACME) for the Ocean Worlds Life Surveyor (OWLS). (submitted) Earth and Space Science</p> <p>[2] Mora et al., F.(2021). Detection of biosignatures by capillary electrophoresis and mass spectrometry in the presence of salts relevant to missions to ocean worlds (submitted). Astrobiology.</p> <p>[3] 10.5281/zenodo.5849873</p> <p><br> © 2022. California Institute of Technology. Government sponsorship acknowledged</p>
Dataset associated with Banks et al. (2022): "Impacts of the desiccation of the Aral Sea on the Central Asian dust life-cycle"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol described by the paper "Impacts of the desiccation of the Aral Sea on the Central Asian dust life-cycle", written by Banks et al. and published in JGR in 2022 (<a href="https://doi.org/10.1029/2022JD036618">https://doi.org/10.1029/2022JD036618</a>).</p>
MastaBase: a research tool for the study of 'daily life' scenes in Old Kingdom elite tombs
<p><em>Mastabase: a research tool for the study of the secular or 'daily life' scenes and their accompanying texts in the elite tombs of the Memphite area in the Old Kingdom</em></p> <p>The Leiden Mastaba Project was initiated in 1998 to develop a coherent database of iconographic programmes in Old Kingdom elite tombs from the Memphite area (c. 2600-2150 BCE). It was published as a CD-ROM in 2008 by Peeters Publishers in Leuven.</p> <p>The project was directed by dr. René van Walsem at Leiden University, and partly funded by NWO and LUF/Gratama. Hans van den Berg and drs. Marije Vugts played a vital role in its development.</p> <p>The ISO was uploaded by Nicky van de Beek.</p> <p>Project page: https://digitalegyptology.org/mastabase/</p> <p>---</p> <p>The Leiden Mastaba Project (LMP) concerns an integral and analytic study of the secular or 'daily life' scenes and their accompanying texts in the elite tombs of the Memphite area in the Old Kingdom (c. 2600-2150 B.C.). The project has the aim to get insight in the developments of number, size, internal organization and shape of the various (sub)themes, their location in the tomb, their wall position (upper/middle/lower level), and their orientation (north/east/south/west) on the walls. This reflects the dynamics of Old Kingdom funerary culture in general aspects (collective) and in specific cases (individual). Simultaneously it reveals possible local variations, mainly among the large necropoleis of Saqqara and Giza.</p> <p>The data are digitized in a database called MastaBase, published on this cd-rom. Thanks to the standardisation of the material offered in the MastaBase, with this cd-rom it is now possible to gain quick oversights into various different aspects of these tombs and their decoration via extensive selection procedures.</p>
Sharkipedia: A Curated Open Access Database of Shark and Ray Life History Traits and Abundance Time-series
<p>This dataset represent the intial launch of Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series. A curated database of shark and ray biological data is increasingly necessary both to support fisheries management and conservation efforts, and to test the generality of hypotheses of vertebrate macroecology and macroevolution. Sharks and rays are one of the most charismatic, evolutionary distinct, and threatened lineages of vertebrates, comprising around 1,250 species. To accelerate shark and ray conservation and science, we developed Sharkipedia as a curated open-source database and research initiative to make all published biological traits and population trends accessible to everyone. Sharkipedia hosts information on 58 life history traits from 264 sources, for 170 species, from 39 families, and 12 orders related to length (n=9 traits), age (8), growth (12), reproduction (19), demography (5), and allometric relationships (5), as well as 871 population time-series from 202 species. Sharkipedia relies on the backbone taxonomy of the IUCN Red List and the bibliography of Shark-References. Sharkipedia has profound potential to support the rapidly growing data demands of fisheries management, international trade regulation as well as anchoring vertebrate macroecology and macroevolution.</p>
Supplementary Material for "'A Certain Enemy Robbed Me of My Life': Medieval Riddles, Digital Transformations, and Pandemic Pedagogy"
<p>Assignment, games, and illustrations associated with "'A Certain Enemy Robbed Me of My Life': Medieval Riddles, Digital Transformations, and Pandemic Pedagogy"</p>
Data for 'Phenological shifts in a warming world affect physiology and life history in a damselfly'
<p>In this analysis we studied, in laboratory conditions, the impact of warming and hatching dates on key life history and physiological traits in a cannibalistic damselfly, <em>Ischnura</em> <em>elegans</em>. Larvae were reared in groups from hatching to emergence through one or two growth seasons, depending on the voltinism. Larvae were equally divided by hatching dates (early and late) and temperature treatment (current and warming). Early and late hatched groups were not mixed. This data set includes:</p> <ul> <li>survival until emergence and emergence success data</li> <li>development time (from hatching to emergence, in days)</li> <li>mass of adult insects</li> <li>growth rate (mass of adult insect/development time in days)</li> <li>protein content (μg of protein/μl of prepared homogenate)</li> <li>phenoloxidase activity (PO, value of activity curve slope)</li> <li>PO activity/protein (value of activity curve slope/(μg of protein/μl of prepared homogenate)</li> <li>insects voltinism (univoltine/semivoltine)</li> </ul>
Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"
<p><strong>Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"</strong></p> <p><em>cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</em></p> <p>These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</p> <p><strong>40-lifetables.csv</strong></p> <p>Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided.</p> <p><strong>30-lt_input.csv</strong></p> <p>Life table input data.</p> <ul> <li>`id`: unique row identifier</li> <li>`region_iso`: iso3166-2 region codes</li> <li>`sex`: Male, Female, Total</li> <li>`year`: iso year</li> <li>`age_start`: start of age group</li> <li>`age_width`: width of age group, Inf for age_start 100, otherwise 1</li> <li>`nweeks_year`: number of weeks in that year, 52 or 53</li> <li>`death_total`: number of deaths by any cause</li> <li>`population_py`: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)</li> <li>`death_total_nweeksmiss`: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)</li> <li>`death_total_minnageraw`: the minimum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxnageraw`: the maximum number of age-groups in the raw input data within this region-sex-year stratum</li> <li>`death_total_minopenageraw`: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_maxopenageraw`: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum</li> <li>`death_total_source`: source of the all-cause death data</li> <li> <p>`death_total_prop_q1`: observed proportion of deaths in first quarter of year</p> </li> <li> <p>`death_total_prop_q2`: observed proportion of deaths in second quarter of year</p> </li> <li> <p>`death_total_prop_q3`: observed proportion of deaths in third quarter of year</p> </li> <li> <p>`death_total_prop_q4`: observed proportion of deaths in fourth quarter of year</p> </li> <li> <p>`death_expected_prop_q1`: expected proportion of deaths in first quarter of year</p> </li> <li> <p>`death_expected_prop_q2`: expected proportion of deaths in second quarter of year</p> </li> <li> <p>`death_expected_prop_q3`: expected proportion of deaths in third quarter of year</p> </li> <li> <p>`death_expected_prop_q4`: expected proportion of deaths in fourth quarter of year</p> </li> <li>`population_midyear`: midyear population (July 1st)</li> <li>`population_source`: source of the population count/exposure data</li> <li>`death_covid`: number of deaths due to covid</li> <li>`death_covid_date`: number of deaths due to covid as of <date></li> <li>`death_covid_nageraw`: the number of age groups in the covid input data</li> <li>`ex_wpp_estimate`: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year</li> <li>`ex_hmd_estimate`: life expectancy estimates from the Human Mortality Database</li> <li>`nmx_hmd_estimate`: death rate estimates from the Human Mortality Database</li> <li>`nmx_cntfc`: Lee-Carter death rate projections based on trend in the years 2015 through 2019</li> </ul> <p><em>Deaths</em></p> <ul> <li>source: <ul> <li>STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)</li> <li>ONS for GB-EAW pre 2020</li> <li>CDC for US pre 2020</li> </ul> </li> <li>STMF: <ul> <li>harmonized to single ages via pclm</li> <li>pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110</li> <li>smoothing parameters estimated via BIC grid search seperately for every pclm iteration</li> <li>last age group set to [110,111)</li> <li>ages 100:110+ are then summed into 100+ to be consistent with mid-year population information</li> <li>deaths in unknown weeks are considered; deaths in unknown ages are not considered</li> </ul> </li> <li>ONS: <ul> <li>data already in single ages</li> <li>ages 100:105+ are summed into 100+ to be consistent with mid-year population information</li> <li>PCLM smoothing applied to for consistency reasons</li> </ul> </li> <li>CDC: <ul> <li>The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data</li> </ul> </li> </ul> <p><em>Population</em></p> <ul> <li>source: <ul> <li>for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019</li> <li>for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100</li> </ul> </li> <li>mid-year population <ul> <li>mid-year population translated into exposures: <ul> <li>if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates</li> <li>if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364\*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.</li> </ul> </li> </ul> </li> </ul> <p><em>COVID deaths</em></p> <ul> <li>source: COVerAGE-DB (https://osf.io/mpwjq/)</li> <li>the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total</li> </ul> <p><em>External life expectancy estimates</em></p> <ul> <li>source: <ul> <li>World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019</li> <li>Human Mortality Database (https://mortality.org/), single year and age tables</li> </ul> </li> </ul>
Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"
<p><strong>Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"</strong></p> <p><em>cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</em></p> <p>These are CSV files of data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".</p> <p><strong>50-e0diffT.csv</strong></p> <p>Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries are ordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate the average annual LE changes over the years 2015 through 2019.</p> <p><strong>51-arriagaT.csv</strong></p> <p>Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021. The position of the arrowhead indicates the total contribution of mortality changes in a given age group to the change in life expectancy at birth since 2019. The discontinuity in the arrow indicates those contributions separately for the years 2020 and 2021. Annual contributions can compound or reverse. The total life expectancy change from 2019 to 2021 in a given country is the sum of the arrowhead positions across age.</p> <p><strong>52-sexdiff.csv</strong></p> <p>Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Blue colors indicate an increase and red colors a decrease in the female life expectancy advantage. Muted colors indicate non-significant changes.</p> <p><strong>53-e0diffcodT.csv</strong></p> <p>Figure 4: Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.</p> <p><strong>55-vaxe0.csv</strong></p> <p>Figure 5: Years of life expectancy deficit during October through December 2021 contributed by ages <60 and 60+ against % of population twice vaccinated by October 1st in the respective age groups. LE deficit is defined as the counterfactual LE from a Lee-Carter mortality forecast based on death rates for the fourth quarter of the years 2015 to 2019 minus observed LE.</p> <p><strong>54-tab_arriaga.csv</strong></p> <p>Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of the pandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.</p>
Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"
<p>This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld, M. Finkbeiner, J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”<strong><em>,</em></strong> <em>Sustainable Energy Fuels</em>, <strong>2022</strong>, <strong>6</strong>, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>.</p> <p>It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.</p>
Research Data Life Cycle
<p>Visual description of an ideal research data life cycle, including traditional elements (dark blue), data reuse elements (light blue) and dissemination/sharing/publication elements (light red).</p> <p>Inspired by: Ruegg et al, Completing the data life cycle: using information management in macrosystems ecology research, Front Ecol Environ 2014; 12(1): 24–30, doi:10.1890/120375</p>
Context-based Entity Recommendation on Real-Life Knowledge Work in Context (RLKWiC dataset)
<h2><a href="../records/11059573">RLKWiC</a> Add-on: Benchmarking Dataset for Entity Recommendation</h2> <p>This benchmark, built on top of the Real-Life Knowledge Work in Context (<a href="../records/11059573">RLKWiC</a>) dataset, is designed to evaluate context-based entity recommendation by simulating a scenario where participants receive entities extracted from their activities across their defined contexts. </p> <p>In total, 1850 entity recommendations were generated across 56 contexts. After deduplication, these entities were presented to participants for explicit relevance assessment on a 3-point scale:</p> <ul> <li>0 [Irrelevant]: Signifying a lack of relevance between the recommended entity and the context.</li> <li>1 [Relevant] Denoting a connection between the entity and the context, although it may not fully represent it.</li> <li>2 [Representative]: The entity closely aligns with the context, indicating a high level of relevance where the context can be inferred to be about this entity.</li> </ul> <p>Participants could also suggest additional relevant entities. The resulting dataset comprises 1067 entities with explicit relevance scores, offering a resource for benchmarking entity recommendation in real-life knowledge work.</p> <h3><strong>Paper: </strong><a href="https://dl.acm.org/doi/10.1145/3640457.3688068" target="_blank" rel="noopener">Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data</a></h3>
Raw images from: Detecting life by behavior, the overlooked sensitivity of behavioral assays
<p>Raw images of the manuscript entitle: "Detecting life by behavior, the overlooked sensitivity of behavioral assays"</p> <p>Description: Using a magnetotactic bacterial species,<em> Magnetospirillum magneticum</em>, we conduct a lab sensitivity experiment comparing PCR with the hanging drop behavioral assay, using a dilution series.</p> <p>Data:</p> <p>1.-Gel image resulted from the <em>Magnetospirillum magneticum </em>PCR assays. </p> <p>2.-Microphotographs of <em>Magnetospirillum magneticum </em>obtained using the hanging drop technique and serial dilution. </p> <p>3.-Videos 1 to 4.Environmental samples were taken from Agmon Hula lake, (33° 10′ N 35° 60′ E). We used the HDT (see main MS) to morphologically identify magnetotactic bacterial species.</p>
Quality of Life of Breast Cancer Patients in Albania
<p>Quality of Life of Breast Cancer Patients in Albania. <span>For this study, the European Organization for Research and Treatment of Cancer Quality of Life (EORTC QLQ – C30) questionnaire was used</span></p>
The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life. Data file underpinning Figure 2A and Figure 2B
<div>These datasheets accompany the article "The Earth BioGenome Project Phase II: Illuminating the Eukaryotic Tree of Life" in Frontiers in Science</div> <div>This file contains data processed from Catalog of Life on 31 December 2023. The catalog was downloaded and post-processed to</div> <div>remove prokaryotic taxa</div> <div>remove extinct and fossil taxa</div> <div>remove taxon names that were listed as junior synonyms</div> <div>remove taxon names listed as "invalid"</div> <div>Total living, valid eukaryotic genera 167,085</div> <div>The taxa were sorted by the nomenclatorial Code under which they were declared (to avoid namespace clashes)</div> <div>International Code for Algae, Fungi and Plants https://www.iapt-taxon.org/nomen/main.php</div> <div>Algal, Fungal, Plant code genera 31,076</div> <div>International Code of Zoological Nomenclature https://www.iczn.org/the-code/the-code-online/</div> <div>Zoological code genera 136,009</div> <div>The Code-sorted taxa were aggregated by the generic portion of their names, and two plots were generated:</div> <div>a plot aggregating the cumulative number of species in genera sorted by species number (Figure 2A)</div> <div>a plot illustrating the distribution of the size of genera (Figure 2B)</div> <div>This data file gives access to these processed data for</div> <div>Figure 2 A Data</div> <div>Figure 2 B Data</div> <div>The original data including the intermediate calculations of values, and the plotted graphs, are available as a GoogleDoc at https://docs.google.com/spreadsheets/d/1V-bTtWjIRasC3AgID0jGlyToKqI-H9h1aPeSxUpNrjk/edit?usp=sharing</div>
Duhumbi life events
<p>Here are a few photo files displaying Duhumbi the ancient Duhumbi burial practice. Other life-events, including a traditional marriage, are slated for recording at a later moment, but described here and in the grammar. The community has hitherto resisted recording of the practice of cutting up the corpse and disposing it in the river. The Duhumbi people traditionally consider three main life events: birth, marriage and death. There are no special coming-of-age, adulthood ceremonies, engagements and the like. </p> <p>This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes <em><strong>of any kind</strong>, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) </em>is <strong>not</strong> permitted without <strong>specific written consent</strong> from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.</p> <p>This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>. Legal Code on <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode</a>.</p> <p>Tim Bodt: bodttim (at) gmail (dot) com</p>
Edward FitzGerald Life and Letters – Overview of all letters
<p>These files form part of an archive of research material relating to the Life and Letters of Edward FitzGerald. The data have been compiled by independent researchers W H (Bill) Martin and Sandra Mason; their contact details are below. The files comprise a number of searchable listings of information contained in FitzGerald’s letters. The information formed input to a book on Edward FitzGerald which is referenced below.</p> <p>This section of the archive contains a large database, <em>efgdb</em>, giving an overview of the letters, their dating, the people to whom FitzGerald wrote, his location at the time of writing, and a broad classification of the content of each letter. The letters are those contained in the collection published by A M Terhune and A B Terhune in 1980 – see reference below. An explanatory README text file contains further information on the database, including a table showing the fields included in the database and giving definitions of them and of the codings used where relevant. </p>
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