Find research datasets worth reusing

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE11_Hydro (Altamaha River near Lewis Creek, Georgia) from 07-Oct-2014 through 31-Dec-2014

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE11_Hydro (Altamaha River near Lewis Creek, Georgia) from 07-Oct-2014 through 31-Dec-2014. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE1_Hydro (Sapelo River near Eulonia, Georgia) from 13-Sep-2001 through 31-Dec-2001

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE1_Hydro (Sapelo River near Eulonia, Georgia) from 12-Sep-2001 through 31-Dec-2001. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately bimonthly. Salinity, depth and sigma-t were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE1_Hydro (Sapelo River near Eulonia, Georgia) from 01-Jan-2002 through 31-Dec-2002

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE1_Hydro (Sapelo River near Eulonia, Georgia) from 01-Jan-2002 through 31-Dec-2002. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately bimonthly. Salinity, depth and sigma-t were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE2_Hydro (Four Mile Island, Georgia) from 26-Oct-2001 through 31-Dec-2001

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE2_Hydro (Four Mile Island, Georgia) from 26-Oct-2001 through 31-Dec-2001. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately bimonthly. Salinity, depth and sigma-t were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2002 through 31-Dec-2002

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2002 through 31-Dec-2002. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately bimonthly. Salinity, depth and sigma-t were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE3_Hydro (Sapelo Sound north of Sapelo Island, Georgia) from 08-Aug-2001 through 31-Dec-2001

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE3_Hydro (Sapelo Sound north of Sapelo Island, Georgia) from 08-Aug-2001 through 31-Dec-2001. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately bimonthly. Salinity, depth and sigma-t were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE6_Hydro (Doboy Sound south of Sapelo Island, Georgia) from 25-Feb-2002 through 31-Dec-2002

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE6_Hydro (Doboy Sound south of Sapelo Island, Georgia) from 25-Feb-2002 through 31-Dec-2002. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately bimonthly. Salinity, depth and sigma-t were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE7_Hydro (Altamaha River near Carrs Island, Georgia) from 10-Aug-2001 through 31-Dec-2001

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE7_Hydro (Altamaha River near Carrs Island, Georgia) from 10-Aug-2001 through 31-Dec-2001. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately bimonthly. Salinity, depth and sigma-t were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE7_Hydro (Altamaha River near Carrs Island, Georgia) from 01-Jan-2002 through 31-Dec-2002

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE7_Hydro (Altamaha River near Carrs Island, Georgia) from 01-Jan-2002 through 31-Dec-2002. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately bimonthly. Salinity, depth and sigma-t were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE8_Hydro (Altamaha River near Aligator Creek) from 26-Oct-2001 through 31-Dec-2001

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE8_Hydro (Altamaha River near Alligator Creek) from 26-Oct-2001 through 31-Dec-2001. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately bimonthly. Salinity, depth and sigma-t were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE8_Hydro (Altamaha River near Aligator Creek) from 01-Jan-2002 through 31-Dec-2002

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE8_Hydro (Altamaha River near Aligator Creek) from 01-Jan-2002 through 31-Dec-2002. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately bimonthly. Salinity, depth and sigma-t were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

opencc-by-4.0Oct 2024View details →

Research data: Continuity Amid Transformation: An Analysis of Pottery Production from the Late La Tène to Early Roman Periods in Eastern Bohemia

Data used in the research presented in the article titled "Continuity Amid Transformation: An Analysis of Pottery Production from the Late La Tène to Early Roman Periods in Eastern Bohemia". Abstract of the article: At the end of the La Tène period and the beginning of the Roman period in the first century BC, society in Central Europe underwent a significant transformation, which included notable changes in pottery production. This transformation is often attributed to the collapse of the social structures of the La Tène period and the arrival of a new population. Pottery production, in particular, is generally considered to have undergone a complete transformation. However, previous studies on this transition have primarily focused on the stylistic analysis of shapes and decorations, as illustrated by the pottery assemblage from Slepotice (Eastern Bohemia). In order to obtain additional data on the transitional period, this study of pottery from Slepotice incorporates analyses of the materials used and the manufacturing process through macroscopic observation, X-ray fluorescence analysis, and thin-section analysis. These analyses provide new insights into the differences in pottery production and distribution during the first century BC. Our research indicates that while the transformation included the collapse of the La Tène socioeconomic network, it did not result in a complete break in the pottery production process. Link to the article: <a href="https://doi.org/10.1016/j.jasrep.2025.105073">https://doi.org/10.1016/j.jasrep.2025.105073</a>   List of the files: Supplementary Material 1 Settlement structure in the vicinity of Slepotice during the La Tène and Roman periods: 1 – Slepotice, 2 – České Lhotice, 3 – Brčekoly, 4 – Chrudim Supplementary Material 2 Values of pottery attributes (Mat, InMn, InVar, In, traces left from the shaping process, Po, Vy, and morphological groups) classified based on macroscopic observation Supplementary material 3 Schematic classification of rim attributes, illustrating different variants of rim direction (Op), thickening of the upper part of the rim (Oz), and trimming of the lip (Os) Supplementary material 4 Attributes of the 30 samples selected for XRF analysis based on macroscopic observation. These attributes include fabric properties, surface treatment, morphological features, and technological traces Supplementary material 5 Figures of ceramic samples (with corresponding IDs) from feature 144/1998 showing preserved rims and bases Supplementary material 6 Figures of ceramic samples (with corresponding IDs) from feature 355/2001 showing preserved rims Supplementary Material 7 Chemical composition of 30 selected samples according to XRF analysis (main oxides in wt%, and elements in ppm) Supplementary Material 8 Principal Component Analysis (PCA) results: The scree plot (top left) visualises the proportion of variance explained by each principal component. The biplots (top right and bottom right) illustrate the distribution of samples, with arrows indicating the contribution of specific elements to the observed variance. The dendrogram (bottom left) shows hierarchical clustering of the samples, aiding in the selection of representative samples for thin-section petrographic analysis Supplementary Material 9 Relationships between the dating and other attributes of pottery classified based on macroscopic observation. These attributes include fabric properties, surface treatment, morphological features, and technological traces Supplementary Material 10 Relationships between the chemical groups (determined by XRF analysis) and pottery attributes classified based on macroscopic observation. These attributes include fabric properties, surface treatment, morphological features, and technological traces Supplementary Material 11 Petrography of fabric groups and subgroups, focusing on their properties. The evaluation begins with a general assessment of each fabric group as a whole, followed by a detailed examination of its subgroups Supplementary Material 12 Petrographic characterization of ceramics using a semiquantitative scale, simplified for statistical analysis (0.1 – trace, 0.5 – rare, 1 – occasional, 2 – common, 3 – frequent, 4 – abundant, 5 – dominant) Supplementary Material 13 Thin-section samples: Description of the ceramic matrix, natural inclusions, and added tempers Supplementary material 14 Variations in chemical composition among different fabric groups

opencc-by-4.0Feb 2022View details →

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19" 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". 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". 40-lifetables.csv 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. 30-lt_input.csv Life table input data. <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> `death_total_prop_q1`: observed proportion of deaths in first quarter of year </li> <li> `death_total_prop_q2`: observed proportion of deaths in second quarter of year </li> <li> `death_total_prop_q3`: observed proportion of deaths in third quarter of year </li> <li> `death_total_prop_q4`: observed proportion of deaths in fourth quarter of year </li> <li> `death_expected_prop_q1`: expected proportion of deaths in first quarter of year </li> <li> `death_expected_prop_q2`: expected proportion of deaths in second quarter of year </li> <li> `death_expected_prop_q3`: expected proportion of deaths in third quarter of year </li> <li> `death_expected_prop_q4`: expected proportion of deaths in fourth quarter of year </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> Deaths <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> Population <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> COVID deaths <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> External life expectancy estimates <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>

opencc-by-4.0Feb 2022View details →

Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

Data for Figures and Tables in "Bounce backs amid continued losses: Life expectancy changes since COVID-19" 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". 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". 50-e0diffT.csv 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. 51-arriagaT.csv 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. 52-sexdiff.csv 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. 53-e0diffcodT.csv 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. 55-vaxe0.csv 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. 54-tab_arriaga.csv 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.

opencc-by-nc-4.0May 2024View details →

Solar Asset Mapper: A continuously-updated global inventory of solar energy facilities built with satellite data and machine learning

TransitionZero’s Solar Asset Mapper is a global, satellite-derived dataset of utility-scale solar farms generated with a combination of machine learning and human annotation. Our Q1 2024 dataset contains the location and shape of 63,616 assets, along with estimated capacities. We estimate the construction date for over 80% of these assets. The dataset contains over 19,100 square kilometres of solar farms across 183 countries, with a total estimated capacity of 711 GW. Download the dataset, read the explainer and explore our polygon browser UI at <a href="https://www.transitionzero.org/products/solar-asset-mapper" target="_blank" rel="noopener">TransitionZero.org.</a> <a href="https://blog.transitionzero.org/hubfs/Data%20Products/TZ-SAM/tz-sam-scientific-methodology-Q12024.pdf" target="_blank" rel="noopener">Download our methodology paper here </a> <h1>1. Dataset Description</h1> We publish six files. <ul> <li>analysis_polygons.gpkg: our “analysis-ready” dataset containing geometries, capacity estimates and construction date estimates.</li> <li>analysis_polygons.csv: a version of analysis_polygons.gpkg containing a central latitude and longitude in place of a geometry, to allow parsing without geospatial software.</li> <li>sources.csv: a table mapping the IDs of our analysis-ready dataset to the raw geometries that make them up.</li> <li>raw_polygons.gpkg: the raw geometries used to compose analysis_polygons.gpkg.</li> <li>TZ Solar Asset Mapper Q1 2024.xlsx: an Excel formatted version of the analysis_polygons.csv file.</li> <li>tz-sam_scientific_data.pdf: A pre-print aricle that explains the methodology in detail.</li> </ul> <h2>1.1 Analysis-level datasets</h2> Our analysis-level dataset comprises our most complete view of global asset-level solar installations, incorporating our own detections as well as known solar farm geometries from other datasets. The geospatial dataset contains the following fields: <ul> <li>id: unique ID for the asset</li> <li>geometry: Polygon or MultiPolygon defining the asset</li> <li>capacity_mw: estimated capacity of the asset in megawatts</li> <li>constructed_before: upper bound for construction date (estimated date of the image in which the solar plant was first seen in a constructed state)</li> <li>constructed_after: lower bound for construction date (estimated date of the image in which construction began for the solar plant)</li> </ul> The CSV version replaces the Geometry column with: <ul> <li>latitude: the latitude of the centroid of the asset</li> <li>longitude: the longitude of the centroid of the asset</li> <li>country: administrative country name</li> </ul> <h2>1.2 Raw datasets and sources</h2> The analysis-level datasets hide some complexity in the underlying data that we expose in the raw_polygons and sources file. <ul> <li>We produce new sets of polygons for each run. Often these overlap, sometimes in complicated ways.</li> <li>We cluster together overlapping and nearby geometries from both our detections and external sources. Currently these sources are:</li> <li>Large solar farms scraped from OpenStreetMap (OSM)</li> <li>Validated geometries from <a href="../records/5005868">Kruitwagen et. al., A global inventory of solar photovoltaic generating units</a>.</li> </ul> Each cluster comprises one row in the analysis-level dataset. In order to enable tracking raw detections from run to run, as well as to provide detailed sourcing information, we provide all of these raw polygons, along with a source file that lists all of the raw polygons contained in each analysis-level polygon. raw_polygons.gpkg contains the following fields: <ul> <li>id: ID of the raw source polygon</li> <li>geometry: Polygon or MultiPolygon defining the asset</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> Sources.csv contains the following fields: <ul> <li>cluster_id: ID of the corresponding item in the analysis-level dataset</li> <li>source_id: ID of the raw source polygon</li> <li>source: either “solar asset mapper”, “osm” or “2019_global_pv”.</li> <li>acquisition_date: for solar asset mapper polygons, this is the date of the inference run that produced the polygon; for OSM polygons it is the date that the polygon was scraped from OSM; for 2019_global_pv it is 2019-01-01, the approximate detection date of that dataset.</li> </ul> <h2>1.3 Caveats and limitations</h2> <h3>1.3.1 Capacity Estimates</h3> While we have made every effort to remove false positives from the published dataset, some will remain due to the difficulty of manually validating detections in 10-metre satellite imagery. To estimate false positive prevalence throughout the data a subset of approximately 2000 detections were selected at random from our positively labelled solar assets. Each of these were validated through a higher degree of scrutiny utilising high-resolution imagery. This analysis yielded an expected rate of false positives of around 1%. <h3>1.3.2 Plant Shapes</h3> Our plant outlines are not perfect. They will occasionally be much smaller or larger than the underlying plant. Our tests show that on average, these effects average out. <h3>1.3.3 Capacity Updates</h3> Our capacity estimation model should produce relatively unbiased country-level aggregates, since it is trained to learn the typical ground coverage ratio of plants by country. The model has no way to distinguish between a very dense and a very sparse (e.g. dual-axis-tracking) plant in the same country. Plants with unusually high or low ground coverage ratios will not have accurate capacity estimates. <h3>1.3.4 Construction Date Estimates</h3> We are not able to directly estimate the construction date of a plant. We estimate an upper bound (the date of the image in which the plant was first seen in constructed state) and a lower bound (the date of the image in which the plant was last seen in an unconstructed state). For plants that were constructed before the launch date of Sentinel-2 in 2017, we produce only an upper bound. We leave it to consumers of the data to interpret these bounds and/or estimate likely grid connection dates. 2. Attribution TZ-SAM is made available under a Creative Commons Attribution Non-Commercial 4.0 International License (CC-BY-NC-4.0). Attribution to TransitionZero is required. You must also clearly indicate if you have made any changes to the TZ-SAM dataset and what these are. Please refer to the suggested citation formats: <ul> <li>“TransitionZero Solar Asset Mapper, TransitionZero, May 2024 release.”</li> <li>“TZ-SAM, TransitionZero, May 2024 release.”</li> <li>“TransitionZero (2024) Solar Asset Mapper.”</li> </ul>

opencc-by-4.0Jul 2018View details →

Continuous rotation electron diffraction data for Zeolite Mordenite

Raw continuous rotation electron diffraction data for mordenite:     - mordenite_cRED_1.zip     - mordenite_cRED_2.zip The zip file contains 3 directories          - SMV: Diffraction data (stretch correction applied) in SMV format     - Tiff: Raw diffraction data in 16-bit unsigned integer TIFF format     - Defocused images in 16-bit unsigned integer TIFF format Experimental parameters are stored in the header files of the SMV images, and in the file cRED_log.txt The SMV data can be processed using XDS. Raw serial electron diffraction data sets for mordenite:     - mordenite_SerialED.zip The zip file contains at least 3 directories:     - calib: contains the calibration files for the experiment     - data: contains the raw diffraction data for all the identified crystals in hdf5 format     - images: contains the image data used to locate crystals in hdf5 format Experimental parameters (such as the crystal coordinates) are stored in the attributes on the data files. The Python code to process the data can be found in the problematic-0.1.0.zip folder or on http://github.com/stefsmeets/problematic Prediction scores for all diffraction patterns are given in `learning.csv`

opencc-by-4.0Sep 2024View details →

Data for "SeaMoon: from protein language models to continuous structural heterogeneity"

Datasets used for development of SeaMoon:  <a href="https://github.com/PhyloSofS-Team/seamoon">https://github.com/PhyloSofS-Team/seamoon</a>. This upload contains the following data: <ul> <li>precomputed_emb.tar.gz is a compressed archive containing the precomputed data used for training and testing the models of the SeaMoon method, in Torch .pt format.  The file prefixes consist of two IDs, "ID1_ID2_", identifying the <a href="https://github.com/PhyloSofS-Team/DANCE">DANCE</a> [1] protein conformational collection used for its generation. "ID1" represents the first member of the collection in alphabetical order, while "ID2" is the reference conformation for the structural alignment. The "ESM_data" or "ProstT5_data" suffixes designate the type of embeddings, generated by either ESM2 [2] or ProstT5 [3]. The dictionnary contains the following keys: <ul> <li>emb: The per-residue embedding.</li> <li>data: A tuple containing "ID2" (the reference), the amino acid sequence, and the coverage of the positions in the original DANCE collection.</li> <li>eigvect: The eigenvectors of the covariance matrix of the "ID1_ID2" collection, centered on reference conformaton "D2".</li> <li>eigval: The associated eigenvalues.</li> <li>ref: The coordinates of the C-alpha atoms of the reference conformaton "ID2".</li> </ul> </li> <li>train_list.txt, train_list_5ref.txt, val_list.txt and test_list.txt contain the identifiers of the samples used for training and evaluating the SeaMoon models. In the "5ref" setting, we used up to 5 reference conformations per collection. </li> </ul> For details on SeaMoon see: <div> <div>SeaMoon: Prediction of molecular motions based on language models</div> </div> <div>Valentin Lombard, Dan Timsit, Sergei Grudinin, Elodie Laine</div> <div>bioRxiv 2024.09.23.614585; doi: https://doi.org/10.1101/2024.09.23.614585</div> <div> </div> <div>For more information on data usage and generation please see <a href="https://github.com/PhyloSofS-Team/seamoon">https://github.com/PhyloSofS-Team/seamoon</a>.</div> <div> </div> <div>Abstract:</div> How protein move and deform determines their interactions with the environment and is thus of utmost importance for cellular functioning. Following the revolution in single protein 3D structure prediction, researchers have focused on repurposing or developing deep learning models for sampling alternative protein conformations. In this work, we explored whether continuous compact representations of protein motions could be predicted directly from protein sequences, without exploiting nor sampling protein structures. Our approach, called SeaMoon, leverages protein Language Model (pLM) embeddings as input to a lightweight (~1M trainable parameters) convolutional neural network. SeaMoon achieves a success rate of up to 40% when assessed against ~1,000 collections of experimental conformations exhibiting a wide range of motions. SeaMoon capture motions not accessible to the normal mode analysis, an unsupervised physics-based method relying solely on a protein structure's 3D geometry, and generalises to proteins that do not have any detectable sequence similarity to the training set. SeaMoon is easily retrainable with novel or updated pLMs.    [1] Lombard, V.; Grudinin, S.; Laine, E. Explaining Conformational Diversity in Protein Families through Molecular Motions. Scientific Data 2024, 11, 752. [2] Lin, Z.; Akin, H.; Rao, R.; Hie, B.; Zhu, Z.; Lu, W.; Smetanin, N.; Verkuil, R.; Kabeli, O.; Shmueli, Y.; Dos Santos Costa, A.; Fazel-Zarandi, M.; Sercu, T.; Candido, S.; Rives, A. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 2023, 379, 1123–1130. [3] Heinzinger, M.; Weissenow, K.; Sanchez, J. G.; Henkel, A.; Steinegger, M.; Rost, B. ProstT5: Bilingual language model for protein sequence and structure. bioRxiv 2023, 2023–07.