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IPBES Data Management Tutorials - Session 3.7: Data management report details: Long-term storage details
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session, <em>Data management report details: Long-term storage details</em>, explores the reasons why IPBES recommends Zenodo as a long-term repository. </p>
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
How aphids fly: take off, free flight and implications for short and long distance migration.
<p>We used a Phantom T4040 camera at 9350-13,000 FPS and at 4.2-Mpx resolution (2560 x 1664) . The aspect ratios varied, but were typically 2048 x 1280 pixels - 2560 x 1664. Videos were captured by the Phantom Camera Control software (PCC) as Cine RAW files and converted to MP4 for analysis and viewing in slow motion. A timer recording behaviour in milliseconds is embedded in MP4 files. Filming at high FPS and in HD requires specialist flicker-free high-speed illumination lighting: we used two GSVitec™ MultiLED MX that each produced 12,000 Lux of white light (24,000 total).</p> <p>Videos include <em>Drepanosiphum platanoidis</em> (Schrank), the sycamore aphid, that feeds on <em>Acer </em>sp, a monophyletic group of trees ancestral to Asia, but present in Europe for the last 30 million years (Gao et al. 2020). <em>Myzus persicae</em> (Sulzer), the peach-potato aphid, is a medium sized aphid that is extremely polyphagous and is truly a global pest species. </p>
Data from: Structure and dynamics of secondary and mature rainforests: insights from South Asian long-term monitoring plots
<p><strong>1) DESCRIPTION </strong></p> <p>The dataset contains annual woody stems (shrubs and trees) census data collected from two long-term ecological monitoring plots spanning one hectare each in the Anamalai Hills of the Southern Western Ghats, India. These two plots represent one situated in a mature forest located within relatively undisturbed rainforest of the Anamalai Tiger Reserve (ATR) and one in secondary forest on the Valparai Plateau, respectively. Both plots have been censused and measured from 2017 to 2022 following the standardized protocol (RAINFOR-GEM, Marthews et al. 2014).</p> <p><br><strong>2) CONTACTS</strong></p> <p>CONTACT #1<br>1. Name: Akhil Murali<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 82812 97441<br>4. Email address: akhil@ncf-india.org<br>5. ORCID: 0000-0001-6149-6458</p> <p>CONTACT #2<br>1. Name: Srinivasan Kasinathan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: srini@ncf-india.org<br>5. ORCID: 0000-0001-7323-6653 </p> <p>CONTACT #3<br>1. Name: Kshama Bhat<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: kshama@ncf-india.org<br>5. ORCID: 000-0002-6190-2687</p> <p>CONTACT #4 <br>1. Name: Jayashree Ratnam <br>2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India<br>3. Work Phone: +91 80 23666001 <br>4. Email address: jratnam@ncbs.res.in <br>5. ORCID: 0000-0002-6568-8374</p> <p>CONTACT #5<br>1. Name: Mahesh Sankaran <br>2. Work Address: National Centre for Biological Sciences, TIFR, Bellary Road, Bengaluru 560065, Karnataka, India<br>3. Work Phone: +91 80 23666001<br>4. Email address: mahesh@ncbs.res.in <br>5. ORCID: 0000-0002-1661-6542</p> <p>CONTACT #6<br>1. Name: Divya Mudappa<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org<br>5. ORCID: 0000-0001-9708-4826</p> <p>CONTACT #7<br>1. Name: T. R. Shankar Raman<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org<br>5. ORCID: 0000-0002-1347-3953</p> <p>CONTACT #8<br>1. Name: Anand M Osuri <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: aosuri@ncf-india.org <br>5. ORCID: 0000-0001-9909-5633</p> <p><br><strong>3) GEOGRAPHIC COVERAGE and SITE DESCRIPTION</strong></p> <p>a) Site type: : Tropical Forest<br>b) Geography: : Anamalai Tiger Reserve, Southern Western Ghats.<br>c) Habit: : Mid elevation Wet evergreen Forest<br>d) Site History: : </p> <p>i) MANAMBOLI- The Mature Forest plot (10.357748° N, 76.889747° E; 825 m asl) is situated within a relatively undisturbed 200-hectare mid-elevation tropical wet evergreen rainforest tract at the core of the Anamalai Tiger Reserve (ATR). This area has been protected from logging and other significant disturbances since its establishment as a protected area in 1979.</p> <p>ii) CANDURA- The Secondary Forest plot (10.30855411° N, 76.83391853° E; 875 m asl) is situated within a 124-hectare rainforest remnant on the Valparai Plateau: the Candura rainforest remnant. The Candura site experienced episodic selective logging in the 1990s and early 2000s, with the last logging episode occurring in 2004. In the early 2000s, the understorey of the remnant was cleared for Vanilla (Vanilla planifolia) cultivation in the central and southern parts (abandoned in 2007), robusta coffee (Coffea canephora) in the northwestern corner (abandoned in the early 2000s), and pepper in 21 hectares in the northeastern part (established in 2015, abandoned in 2021).</p> <p>Climate: Humid tropical with about 2400 mm rainfall annually, falling mainly during the southwest monsoon.</p> <p><br><strong>4) TEMPORAL COVERAGE</strong></p> <p>a) Begins: 2017-11-30 (Year, Month, Day)<br>b) Ends: 2022-11-12 (Year, Month, Day)</p> <p><br>5) SAMPLING DESIGN AND METHODS </p> <p>a) Plot Design: Each 1 ha plot of 100 m × 100 m, sub-divided into 100 continuous sub-plots of 10 m × 10 m, was surveyed and mapped to maximum accuracy using a theodolite in the field, with grid corners permanently staked. <br>b) Data collection period and frequency: After the plot establishment in NOvember -- December 2017, the plots were recensused each year (around November). <br>c) Research Methods: All woody plant individuals with girth at breast height (GBH, at 1.3 m) ≥10 cm were tagged with numbered aluminum tags and spatially mapped. Plant species were identified using standard floral keys. Stem GBH was measured for all single stemmed individuals. For trees with buttresses, the GBH point of measurement (POM) was taken at 50 cm above the buttresses or at the height where the stem is regular. New saplings that recruited into the ≥10 cm GBH class were identified, mapped, tagged, and added to the monitoring. Stems that appeared to be dead were recorded at each monitoring and those that showed no signs of recovery in subsequent visits were recorded as mortality.</p> <p><br><strong>6) FILES INCLUDED</strong></p> <p>The dataset includes the following 9 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)</p> <p>01_README.txt<br>Metadata (this file) including information on the dataset explaining associated files and their contents.</p> <p>02_Candura_annual_census.csv <br>This contains the Annual census data with the following column headings: <br>site: Site name (Can = Candura)<br>cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022)<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ht1: Point of measurement at 1.3 m above the ground or 50 cm above the top of the highest buttress or stilt root (POM1)<br>c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97)<br>g1: Stem girth at POM1 (in centimetre)<br>ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached<br>c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97)<br>g2: Girth at POM2 (in centimetre)<br>dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No)<br>da: alive status of stem (d-dead,a-alive)<br>remarks: remarks or notes</p> <p>03_Manamboly_annual_census.csv<br>This contains Annual census data with the following column headings: <br>site: Site name (Man = Manamboli)<br>cno: Census Number (1 = 2017, 2 = 2018..., 6 = 2022)<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ht1: Point of measurement at 1.3 m above the ground or 50 cm above the top of the highest buttress or stilt root (POM1)<br>c1: Alive status of the stem at the POM1 (coded according Marthews et al. 2014, page: 97)<br>g1: Stem girth at POM1 (in centimetre)<br>ht2: 20 cm above the ht1 or point of measurement 2 (POM2) recording girth at which the dendroband is attached<br>c2: Alive status of the stem at the POM2 (coded acording Marthews et al. 2014, page: 97)<br>g2: Girth at POM2 (in centimetre)<br>dyn: whether the dendroband is attached to the tree or not (y-Yes, n-No)<br>da: alive status of stem (d-dead,a-alive)<br>remarks: remarks or notes</p> <p>04_Candura_vernier.csv<br>This file has the girth measurement of trees with lianas where digital vernier calipers were used to measure stem diameter since it was not possible to measure stem girth using measuring tape.<br>site: Site name (Can = Candura)<br>cno: Census Number<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>vern1_d1 First measure of diameter at POM1 (in millimetre) <br>vern2_d1 Second measure of diameter at POM1 (in millimetre) <br>vern3_d1 Third measure of diameter at POM1 (in millimetre) <br>calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>vern1_d2 First measure of diameter at POM2 (in millimetre) <br>vern2_d2 Second measure of diameter at POM2 (in millimetre) <br>vern3_d2 Third measure of diameter at POM2 (in millimetre) <br>calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>Remarks Remarks and notes</p> <p>05_Manamboli_vernier.csv<br>This file has the girth measurement of trees with lianas where digital vernier calipers were used to measure stem diameter since it was not possible to measure stem girth using measuring tape.<br>site: Site name (Man = Manamboli)<br>cno: Census Number<br>ymd: Date in DD-MM-YYYY format (Day Month Year)<br>gno: Grid Number<br>tno: Unique tag number for the plant<br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes<br>vern1_d1 First measure of diameter at POM1 (in millimetre) <br>vern2_d1 Second measure of diameter at POM1 (in millimetre) <br>vern3_d1 Third measure of diameter at POM1 (in millimetre) <br>calc_g1: Girth at POM1 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>vern1_d2 First measure of diameter at POM2 (in millimetre) <br>vern2_d2 Second measure of diameter at POM2 (in millimetre) <br>vern3_d2 Third measure of diameter at POM2 (in millimetre) <br>calc_g2 Girth at POM2 (in centimetre; calculated using the averaged value as diameter from the three measurements)<br>Remarks Remarks and notes</p> <p>06_Candura_Height_data.csv<br>This contains data on the heights of individual trees in plot as measured in 2018.<br>site: Site name (Can = Candura)<br>ymd: Date in YYYY/MM/DD format (Year Month Day)<br>gno: Grid Number: <br>tno: unique tag number: <br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes: <br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>height: Height of tree in metres<br>remarks: Remarks: and notes</p> <p>07_Manamboli_Height_data.csv<br>This contains data on the heights of individual trees in plot as measured in 2018.<br>site: Site name (Man = Manamboli)<br>ymd: Date in YYYY-MM-DD format (Year Month Day)<br>gno: Grid Number: <br>tno: unique tag number: <br>pno: Pole Number (unique alphabetic code for each stem of multi-stemmed individuals)<br>sps: Species name as codes: <br>lx: X coordinate of tree in the 10 m × 10 m subplot (in metres)<br>ly: Y coordinate of tree in the 10 m × 10 m subplot (in metres)<br>height: Height of tree in metres<br>remarks: Remarks: and notes</p> <p>08_Species_name_match.csv<br>This file provides the combined list of species codes updated taxonomy and successional guild. Scientific names were updated to current taxonomy using the species name matching tool of the Global Biodiversity Information Facility, GBIF (www.gbif.org).<br>sps: Species codes<br>query : Scientific name of the plant at the time of data collection: <br>scientificName: : with auther citation: <br>key: GBIF key<br>rank: Taxonomic rank or level of identification (GENUS, SPECIES)<br>kingdom: Taxonomic Kingdom (plants) provided by GBIF name matching tool: <br>phylum: Taxonomic Phylum provided by GBIF name matching tool<br>class: Taxonomic Class provided by GBIF name matching tool<br>order: Taxonomic Order provided by GBIF name matching tool<br>family: Taxonomic Family provided by GBIF name matching tool<br>genus: Taxonomic Genus provided by GBIF name matching tool<br>botanical_name: Updated scientific name of the species provided by GBIF name matching tool<br>habt_new: Successional guild of the species (Mature = mature forest species; Secondary = secondary successional species; Int - Introduced species)</p> <p>09_R_scrpt_for_manuscript.R<br>Text file with code in the R statistical and programming environment (www.r-project.org).</p> <p><br><strong>Reference</strong><br>Marthews TR, Riutta T, Oliveras Menor I, Urrutia R, Moore S, Metcalfe D, Malhi Y, Phillips O, Huaraca Huasco W, Ruiz Jaén M, Girardin C, Butt N, Cain R and colleagues from the RAINFOR and GEM networks (2014). Measuring Tropical Forest Carbon Allocation and Cycling: A RAINFOR-GEM Field Manual for Intensive Census Plots (v3.0). Manual, Global Ecosystems Monitoring network, http: //gem.tropicalforests.ox.ac.uk/.</p> <p> </p>
Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach
<p>Contact details:</p> <p>wasim.shoman at chalmers.se </p> <p>waahh7 at gmail com</p> <p><strong>Abstract of the research:</strong></p> <p>Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO<sub>2</sub> emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions. However, they require sufficient charging infrastructure to support long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operation (travel times over 4.5 hours or over 360 km distance traveled) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25´25 km<sup>2</sup> square with each square that could include multiple charging stations and parking lots of multiple charging points) needs to have four to five times more overnight than megawatt charging points. We estimate that about 40,000 overnight charging points (50-100 kW, combined charging system, CCS) and about 9,000 megawatt charging system (MCS, 0.7 – 1.2 MW) points are required for 15% of trucks as BETs in long-haul operation. On average, 8 and 2 CCS and MCS chargers are required per charging area, and each MCS and CCS serve, on average, 11 and 2 BETs daily, respectively. Public charging entails about 110 GWh daily electricity demand in each charging area. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization.</p> <p><strong>The methodology:</strong></p> <p>We develop a method to place charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by HDV is identified using the ETISplus dataset. We develop a travel pattern for the HDV to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving HDV. Break and rest locations for all moving HDVs are aggregated to suggest energy requirements if assuming these HDVs are BETs. The aggregated energy to charge stopped BETs is used to identify the number and type of chargers within each suggested charging station.</p> <p><strong>Datasets details</strong></p> <p>The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers, and Planned transport infrastructure. </p> <p>The first dataset titled 'ChargerLocations' contains information about the locations of suggested charging stations, the number and type of chargers, and the number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields:</p> <table> <tbody> <tr> <td>Name</td> <td>Description</td> <td>Data Type</td> <td>Unit</td> </tr> <tr> <td>DTN30/MainDTN</td> <td> number of electrified trucks in 2030</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChE30</td> <td> charged energy in Mega watt-hour from all charging (fast and slow)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>ChERM</td> <td> charged energy in Megawatt hour with slow charging only (rest)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_R</td> <td> number of electrified trucks using slow chargers (rest)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChEBM</td> <td> charged energy in Megawatt hour with fast charging only (break)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_B</td> <td> number of electrified trucks using fast chargers (break)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NSCh2pD</td> <td> number of slow chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NFCh30m</td> <td> number of fast chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>TotCha</td> <td> Total number of chargers</td> <td>integer </td> <td>number</td> </tr> </tbody> </table> <p> </p> <p>The second dataset titled (RestandBreaksPoints.shp) with information about the rest and break point locations. The dataset includes detailes about stop type, number of stopped trucks, and required charged energy. The dataset is a shapefile with "shp" format. </p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data Type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>ID_origin_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_origin_region</p> </td> <td> <p>National name of NUTS-3 region of origin</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ID_destination_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_destination_<br> region</p> </td> <td> <p>National name of NUTS-3 destination region</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Rest</p> </td> <td> <p>A value of ”1” indicates a rest stop</p> </td> <td> <p>Boolean</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Break</p> </td> <td> <p>A value of ”1” indicates a break stop</p> </td> <td> <p>Boolean</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ChaDisKM</p> </td> <td> <p>Charged range within a trip for stopped the truck</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>ChaEnekWh</p> </td> <td> <p>Charged energy within a trip for stopped the truck</p> </td> <td> <p>Float</p> </td> <td> <p>KWh</p> </td> </tr> <tr> <td> <p>MainDTN</p> </td> <td> <p>Number of stopped trucks for the main electrification scenario (15%)</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>ChE30M</p> </td> <td> <p>Charged energy for all stopped trucks</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>ChERM</p> </td> <td> <p>Charged energy for the trucks stopping for rest</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>MDTN_R</p> </td> <td> <p>Number of trucks stopping for rest</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>ChEBM</p> </td> <td> <p>Charged energy for the trucks stopping for break</p> </td> <td> <p>Float</p> </td> <td> <p>MWh</p> </td> </tr> <tr> <td> <p>MDTN_B</p> </td> <td> <p>Number of trucks stopping for break</p> </td> <td> <p>Float</p> </td> <td> <p>number</p> </td> </tr> <tr> <td> <p>geometry</p> </td> <td> <p>X, Y coordinates</p> </td> <td> <p>geometry</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p>The following dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X):</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>ID_origin_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_origin_region</p> </td> <td> <p>National name of NUTS-3 region of origin</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>ID_destination_region</p> </td> <td> <p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p> </td> <td> <p>Integer (9digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Name_destination_<br> region</p> </td> <td> <p>National name of NUTS-3 destination region</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Edge_path_E_road</p> </td> <td> <p>List of the <em>network edge IDs</em> of the shortest path between the O-D pair, determined with Dijkstra's algorithm</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Distance_from_origin_<br> region_to_E_road</p> </td> <td> <p>Distance from the geometric centre of the origin region to the closest network node</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Distance_within_E_<br> road</p> </td> <td> <p>Distance of the shortest edge path between the O-D pair</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Distance_from_E_<br> road_to_destination_<br> region</p> </td> <td> <p>Distance from the geometric centre of the destination region to the closest network node</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Total_distance</p> </td> <td> <p>Sum of <em>Distance_from_origin_region_to_E_road, Distance_within_E_road</em> and <em>Distance_from_E_road_to_destination_region</em></p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2010</p> </td> <td> <p>Number of trucks that drive between the O-D pair in 2010</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2019</p> </td> <td> <p>Number of trucks that drive between the O-D pair after they had been scaled to 2019</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_<br> 2030</p> </td> <td> <p>Number of trucks that drive between the O-D pair according to the forecast for 2030</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2010</p> </td> <td> <p>Number of tons that are transported between the O-D pair in 2010 according to ETISplus</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2019</p> </td> <td> <p>Number of tons that are transported between the O-D pair after they had been scaled to 2019</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> <tr> <td> <p>Traffic_flow_tons_<br> 2030</p> </td> <td> <p>Number of tons that are transported between the O-D pair according to the forecast for 2030</p> </td> <td> <p>Integer</p> </td> <td> <p>Tons [t]</p> </td> </tr> </tbody> </table> <p>Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Network_Node_ID</p> </td> <td> <p>Unique network node ID</p> </td> <td> <p>Integer (6 digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Network_Node_X</p> </td> <td> <p>Longitude of the location of network node</p> </td> <td> <p>Float</p> </td> <td> <p>Degrees</p> </td> </tr> <tr> <td> <p>Network_Node_Y</p> </td> <td> <p>Latitude of the location of network node</p> </td> <td> <p>Float</p> </td> <td> <p>Degrees</p> </td> </tr> <tr> <td> <p>ETISplus_Zone_ID</p> </td> <td> <p>ID of the NUTS-3 region in which the network node is located</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Country</p> </td> <td> <p>Unique country code of the country in which the network node is located (country codes are defined by ETISplus)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>Description of variables used in the network edges list (Updated_04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p> <table> <tbody><tr> <th> <p><strong>Name</strong></p> </th> <th> <p><strong>Description</strong></p> </th> <th> <p><strong>Data Type</strong></p> </th> <th> <p><strong>Unit</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Network_Edge_ID</p> </td> <td> <p>Unique edge ID</p> </td> <td> <p>Integer<br> (7 digits)</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Manually_Added</p> </td> <td> <p>Determines whether an edge had been manually added to the network (1) or not (0)</p> </td> <td> <p>Binary-integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Distance</p> </td> <td> <p>Length of the network edge</p> </td> <td> <p>Float</p> </td> <td> <p>Kilometres [km]</p> </td> </tr> <tr> <td> <p>Network_Node_A_ID</p> </td> <td> <p>Unique ID of the network node that defines one end point of the network edge</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Network_Node_B_ID</p> </td> <td> <p>Unique ID of the network node that defines one end point of the network edge</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_2019</p> </td> <td> <p>Number of trucks that drive on the edge in 2019 (both highway directions combined)</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> <tr> <td> <p>Traffic_flow_trucks_2030</p> </td> <td> <p>Number of trucks that drive on the edge in 2030 (both highway directions combined)</p> </td> <td> <p>Float</p> </td> <td> <p>Number of trucks</p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
<h2>General</h2> <p>For more details and the most up-to-date information please consult our project page: <a href="https://kainmueller-lab.github.io/fisbe" target="_blank" rel="noopener">https://kainmueller-lab.github.io/fisbe</a>.</p> <h2>Summary</h2> <ul> <li>A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains <ul> <li>30 completely labeled (segmented) images</li> <li>71 partly labeled images</li> <li>altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)</li> </ul> </li> <li>To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects</li> <li>A set of metrics and a novel ranking score for respective meaningful method benchmarking</li> <li>An evaluation of three baseline methods in terms of the above metrics and score</li> </ul> <h2>Abstract</h2> <p>Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.</p> <h2>Dataset documentation:</h2> <p>We provide a detailed documentation of our dataset, following the <a href="https://arxiv.org/abs/1803.09010" target="_blank" rel="noopener">Datasheet for Datasets</a> questionnaire:</p> <p><em>>> <a href="https://kainmueller-lab.github.io/fisbe/datasheet" target="_blank" rel="noopener">FISBe Datasheet</a></em></p> <p>Our dataset originates from the <a href="https://www.janelia.org/project-team/flylight" target="_blank" rel="noopener">FlyLight project</a>, where the authors released a large image collection of nervous systems of ~74,000 flies, <a href="https://gen1mcfo.janelia.org/cgi-bin/gen1mcfo.cgi" target="_blank" rel="noopener">available for download</a> under CC BY 4.0 license.</p> <h2>Files</h2> <ul> <li>fisbe_v1.0_{completely,partly}.zip <ul> <li>contains the image and ground truth segmentation data; there is one <em>zarr</em> file per sample, see below for more information on how to access <em>zarr</em> files.</li> </ul> </li> <li>fisbe_v1.0_mips.zip <ul> <li>maximum intensity projections of all samples, for convenience.</li> </ul> </li> <li>sample_list_per_split.txt <ul> <li>a simple list of all samples and the subset they are in, for convenience.</li> </ul> </li> <li>view_data.py <ul> <li>a simple python script to visualize samples, see below for more information on how to use it.</li> </ul> </li> <li>dim_neurons_val_and_test_sets.json <ul> <li>a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.</li> </ul> </li> <li>Readme.md <ul> <li>general information</li> </ul> </li> </ul> <h2>How to work with the image files</h2> <p>Each sample consists of a single 3d MCFO image of neurons of the fruit fly.<br>For each image, we provide a pixel-wise instance segmentation for all separable neurons.<br>Each sample is stored as a separate <em>zarr</em> file (<a href="https://zarr.readthedocs.io" target="_blank" rel="noopener">zarr</a> is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").<br>The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.<br>The segmentation mask for each neuron is stored in a separate channel.<br>The order of dimensions is CZYX.</p> <p>We recommend to work in a virtual environment, e.g., by using conda:</p> <p><code>conda create -y -n flylight-env -c conda-forge python=3.9</code><br><code>conda activate flylight-env</code></p> <h3>How to open <em>zarr</em> files</h3> <ol> <li>Install the python zarr package: <pre><code>pip install zarr</code></pre> </li> <li>Opened a zarr file with:<br> <p><code>import zarr</code><br><code>raw = zarr.open(<path_to_zarr>, mode='r', path="volumes/raw")</code><br><code>seg = zarr.open(<path_to_zarr>, mode='r', path="volumes/gt_instances")</code></p> <p><code># optional:</code><br><code>import numpy as np</code><br><code>raw_np = np.array(raw)</code></p> </li> </ol> <p>Zarr arrays are read lazily on-demand.<br>Many functions that expect numpy arrays also work with zarr arrays.<br>Optionally, the arrays can also explicitly be converted to numpy arrays.</p> <h3>How to view <em>zarr</em> image files</h3> <p>We recommend to use <a href="https://napari.org" target="_blank" rel="noopener">napari</a> to view the image data.</p> <ol> <li>Install napari: <pre><code>pip install "napari[all]"</code></pre> </li> <li>Save the following Python script: <br> <p><code>import zarr, sys, napari</code></p> <p><code>raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")</code><br><code>gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")</code></p> <p><code>viewer = napari.Viewer(ndisplay=3)</code><br><code>for idx, gt in enumerate(gts):</code><br><code> viewer.add_labels(</code><br><code> gt, rendering='translucent', blending='additive', name=f'gt_{idx}')</code><br><code>viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')</code><br><code>viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')</code><br><code>viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')</code><br><code>napari.run()</code></p> </li> <li>Execute: <pre><code>python view_data.py <path-to-file>/R9F03-20181030_62_B5.zarr</code></pre> </li> </ol> <h2>Metrics</h2> <ul> <li>S: Average of avF1 and C</li> <li>avF1: Average F1 Score</li> <li>C: Average ground truth coverage</li> <li>clDice_TP: Average true positives clDice</li> <li>FS: Number of false splits</li> <li>FM: Number of false merges</li> <li>tp: Relative number of true positives</li> </ul> <p>For more information on our selected metrics and formal definitions please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>Baseline</h2> <p>To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely <a href="https://github.com/Kainmueller-Lab/PatchPerPix" target="_blank" rel="noopener">PatchPerPix (ppp)</a>, <a href="https://github.com/google/ffn" target="_blank" rel="noopener">Flood Filling Networks (FFN)</a> and a non-learnt application-specific <a href="https://www.biorxiv.org/content/10.1101/2020.06.07.138941v1" target="_blank" rel="noopener">color clustering from Duan et al.</a>.<br>For detailed information on the methods and the quantitative results please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>License</h2> <p>The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener">Creative Commons Attribution 4.0 International (CC BY 4.0) license</a>.</p> <h2>Citation</h2> <p>If you use <em>FISBe</em> in your research, please use the following BibTeX entry: </p> <pre><code>@misc{mais2024fisbe, title = {FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures}, author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller}, year = 2024, eprint = {2404.00130}, archivePrefix ={arXiv}, primaryClass = {cs.CV} }</code></pre> <h2>Acknowledgments</h2> <p>We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable<br>discussions.<br>P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.<br>This work was co-funded by Helmholtz Imaging.</p> <h2>Changelog</h2> <p>There have been no changes to the dataset so far.<br>All future change will be listed <a href="https://kainmueller-lab.github.io/fisbe/changelog" target="_blank" rel="noopener">on the changelog page</a>.</p> <h2>Contributing</h2> <p>If you would like to contribute, have encountered any issues or have any suggestions, please <a href="https://github.com/Kainmueller-Lab/fisbe/issues" target="_blank" rel="noopener">open an issue</a> for the FISBe dataset in the accompanying github repository.</p> <p>All contributions are welcome!</p>
Supplementary material to: Long-term (bio)deterioration of Fe-containing and Fe-depleted sandstones: An experimental insight into biotic and abiotic interactions.
<p>This dataset includes: micorphotographs, scanning electron microscope images and related EDS spectra, thermal analysis (DSC-TG), grain size distribution. Abbreviations used in the supplementary file names refer to: GMB (growth medium inoculated with the bacteria, Pseudomonas fluorescens), GM (sterile growth medium), ARE (artificial root exudates), H2O (water), NR (Sample Nowa Ruda), Z (Sample Żerkowice ŻR).</p>
Datasets from study: "Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability"
<p>This repository contains the datasets needed to reproduce the figures from manuscript: Land surface observations boost temperature forecast skill: experiments using Long Short-Term Memory surrogate for physics-based models to assess potential predictability.</p> <p>In this study, we examine the potential of land surface temperature and vegetation data, which are not routinely assimilated in NWP models, for enhancing temperature forecast skill. We build surrogate models for NWP using Long Short-Term Memory.</p>
Global GFED-based monthly burned area time series (1996-2016) at 1 km and ESA CCI MODIS-based long-term monthly P90 burned area occurrence at 500 m
<p>Contains two separate datasets:</p> <ol> <li>Global <a href="https://www.globalfiredata.org/data.html">GFED-based monthly burned area</a> (in ha) <a href="https://youtu.be/kBJcP8mL2Qs">time series (1996-2016)</a> at 1 km (downscaled using cubic-splines from 25 km);</li> <li>Global burned area long term (2000-2012) P90 (quantile probability = 0.9) based on the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI burned area accumulated weekly product</a>;</li> </ol> <p>Original GFED monthly data is provided as HDF4 files (ftp.fuoco.geog.umd.edu/data/GFED/GFED4). Dataset is described in detail in <a href="https://doi.org/10.1002/jgrg.20042">Giglio et al. (2013)</a>. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/GFED"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a> or watch <a href="https://youtu.be/kBJcP8mL2Qs"><strong>this video</strong></a>.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>nhz = theme: natural hazards,</li> <li>monthly.burned.ha = variable: estimated monthly burned area in ha,</li> <li>gfed = data source GFED data,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000.02 = time reference aggregated: month Feb of year 2000,</li> <li>v4 = version number: GFEDv4,</li> </ul>
Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362
<p>Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362 from the the long term experiments belonging in some of the SoilCare project partners. </p>
HomogWS-se: A century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden
<p>Creating a century-long homogenized near-surface wind speed (WS) observation dataset is essential to improve our knowledge about the uncertainty and causes of WS stilling and recovery. We rescued paper-based WS records dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized WS dataset (HomogWS-se) for community uses among climatology, ecology, hydrology and energy industry. HomogWS-se can be used to study the WS variability and change, assess climate reanalysis, and constrain climate simulations for better future projection of changes in the WS and wind energy potential. HomogWS-se contains 13 individual text files with 10-member century-long homogenized monthly WS series, as well as the member-mean series.</p>
Global MODIS-based snow cover monthly long-term (2000-2012) at 500 m, and aggregated monthly values (2000-2020) at 1 km
<p>The Global monthly snow cover repository contains multiple products (based on the MODIS/Terra MOD10A2):</p> <ol> <li>Global snow cover monthly long-term (2000–2012) P90 and standard deviation derived from the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI snow cover weekly product</a>;</li> <li>Global snow cover monthly values P05, P50 and P95 for the period 2000–2020 derived using <a href="https://climate.esa.int/en/odp/#/project/snow">ESA snow cover fraction daily 1-km values</a>;</li> <li>Min and max geometric temperatures for the mid-month (dtm_temp.max_geom.*_m_1km_s0..0cm_xxxx_epsg4326_v1.tif);</li> </ol> <p>Quantiles (probability either 0.05, 0.5, 0.9 and/or 0.95) have been derived by matching dates in the filenames (daily or weekly values). After deriving quantiles, gaps were filled using temporal neighbors (e.g. missing values for year 2002 were filled using average of values between year 2001 and 2003). The gaps were especially large for months of November, December, January and February, northern Hemisphere. Important note: maps still contain some artifacts due to high reflections of white-sands e.g. Salar de Uyuni desert in Bolivia and similar. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/snow.cover"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>snow.cover = variable: snow cover fractions,</li> <li>esa.modis = data source ESA snow product,</li> <li>p.90 = upper 90% quantile,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2012 = time reference aggregated: from 2000 to 2012,</li> <li>v1 = version number: 1,</li> </ul>
Data associated with "A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations"
<p><strong>Datasets used in <em>A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations.</em></strong></p> <p><strong>Datatypes:</strong></p> <ol> <li>Multi-index pandas dataframe (.pkl)</li> <li>Numpy array (.npy)</li> <li>Collection of numpy arrays (.npz)</li> <li>Python dictionary objects (.pkl)</li> </ol> <p><strong>Datasets:</strong></p> <p><strong>alignments.pkl: A dataframe containing numpy arrays of image displacements for each mouse in each memory context.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are ['T', 'F1', 'N1', 'F2', 'N2'] for the training, recall 1-hour, neutral, recall 1-day, neutral day 2 memory contexts respectively. Each element of this dataframe is a numpy array of shape images x 2 that hold x and y image displacements respectively. These alignments are computed after the inscopix software motion correction and are used in Supplemental Figure 2 of the paper.</p> <p><strong>behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id.The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context (*) in ('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2').</p> <p><strong>correlated_pairs_df.pkl: A dataframe containing arrays of neuron indices that have a correlation in activity pattern > 0.3.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a numpy array with three columns. The first two columns are the neuron indices that are correlated and the last column is the strength of the correlation.</p> <p><strong>dredd_freezes_df.pkl: A dataframe containing freezing percentages for SOM-Cre and RTT-SOM-Cre mice treated with DREADDS.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment (mcherry, hm3d, hm4d). The columns contain one of ['Neutral', 'Fear', 'Fear_2']. Each element of the dataframe is a freezing percentage for a single mouse. This dataframe is built from reading the dredd_behavior.xlsx excel file. This is used to generate figure 5E of the paper.</p> <p><strong>high_degree_df.pkl: A dataframe containing list of high degree neuron indices.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'=not applicable since no DREADD used). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a list of neuron indices that are high-degree cells.</p> <p><strong>N006_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N006 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Supplemental Figure 2 of the paper.</p> <p><strong>N006_wt_cxtbasis.pkl: A dictionary containing arrays for basis images and singular values for each context.</strong></p> <p>This dictionary has keys, ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing the memory contexts. Each value is a 2 element list containing the U-basis images as column vectors and singular values, one per basis image in U. The shape of the basis images is the same shape stored in N006_wt_basis.pkl. This dataset is used in Supplementary Figure 2 to track cells across contexts of the CFC task (see also N006_wt_cxtsources.pkl)</p> <p><strong>N006_wt_cxtsources.pkl: A dictionary containing the independent component source images computed from the basis images for automatically identifying regions of interest (ROIs). </strong></p> <p>The dictionary is keyed on ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] contexts. Each value in the dictionary at a given key is a 3-D numpy array of shape sources x height x width. These data were used to construct the source images and max intensity projection image of the sources in Supplemental Figure 2F-J of the paper.</p> <p><strong>N006_wt_rois.pkl: A dictionary containing the boundaries and annuli coordinates of all rois for mouse N006 of genotype wild-type.</strong></p> <p>This dictionary is keyed on ['boundaries', 'annuli'] contexts and each value is a 179 element list of arrays of boundary line coordinates or annulus point coordinates one per ROI detected for this mouse.</p> <p><strong>N006_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N006 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=205 source images, height=517 pixels and width=704 pixels. This data was used to construct Supplemental Figure 3F.</p> <p><strong>N019_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N019 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 1C of the paper.</p> <p><strong>N019_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=204 source images, height=516 pixels and width=698 pixels. This data was used to construct Figure 1C of the paper.</p> <p><strong>P80_animals.pkl: A pandas multi-index object containing the genotype, mouse_id and treatment of the top 80% behavioral performance animals.</strong></p> <p>In this study, we drop the lowest 20% performing WT and RTT animals based on freezing percentage during the recall contexts. This multi-index is used to filter the data before each computation or plot in this study. So for example Figure 1B contains only the top 80% performing WT and RTT mice.</p> <p><strong>pc_sipscs_amps.pkl: A dictionary containing the amplitudes of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC amplitudes, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>pc_sipscs_freqs.pkl: A dictionary containing the frequencies of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC frequencies, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>rois_df.pkl: A multi-index dataframe containing all ROI information for each non-DREADD treated cell in this study (Figures 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0. The columns are ['centroid', 'cell_boundary', 'annulus_boundary']. The centroid for each cell is a 2-tuple of row, column pixel centroid coordinates. The cell_boundary is a two-column array of row, col boundary points for each ROI. The annulus_boundary is a two-column array of row, column interior points in the annulus. The annulus region excludes points of overlap with nearby cell bodies (See STAR methods of the paper).</p> <p><strong>signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD experiments used in this study (Figs 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 5771 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This dataframe is used in the construction of Figures 1-3 in the paper.</p> <p><strong>som_behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context in *=('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'). This dataframe was not used in the paper but may still be useful for further analysis.</p> <p><strong>som_sepsc_amplitudes:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC amplitudes. This data was used in Figure 4E-G.</p> <p><strong>som_sepsc_freqs:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC frequencies. This data was used in Figure 4E-G.</p> <p><strong>som_signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD SOM cell recordings used in this study (Figs 5).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 710 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This data was used to construct Figure 5B-C.</p> <p><strong>ssn33_sstcre_basis.npz: a dict containing three numpy arrays representing the basis images for mouse ssn33 of genotype sst-cre.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 100 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 5A of the paper.</p> <p><strong>ssn33_sstcre_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=86 source images, height=516 pixels and width=654 pixels. This data was used to construct Figure 5A of the paper.</p>
Short-term bioelectric stimulation of collective cell migration in tissues reprograms long-term supracellular dynamics
<p>Full-resolution representative data sufficient to repeat analyses for the work of: AE Wolf, MA Heinrich, IB Breinyn, TJ Zajdel, and DJ Cohen in "Short-term bioelectric stimulation of collective cell migration in tissues reprograms long-term supracellular dynamics".</p> <p>Please see the _README2.0.0.txt file for explanations on contents in this Zenodo repository.</p> <p>Relevant codes used in our analyses are available on Github (github.com/CohenLabPrinceton/ElectrotaxisSupracellularMemory).</p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km distribution trends for 1930–2019 (long-term) and 1987–2019 (short-term), including country-level breakdowns
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data for the long- (1930–2019) and short- term (1987–2019) 10 x 10 km (“hectad”) distribution trends, presented in both the <em>Plant Atlas 2020</em> book (Stroh et al., 2023) and website (www.plantatlas2020.org).</p>
Long-term biomass estimates for the central stock of northern anchovy
<p>Long-term biomass estimates for the central stock of northern anchovy (CSNA; <em>Engraulis mordax</em>) in the California Current Ecosystem are estimated from geospatial weighting of egg and larval data from winter/spring CalCOFI surveys (1951–2021). Estimates include the entire range of the CSNA, from northwestern Baja California, Mexico, to north of Point Reyes, California, and nearshore waters.</p>
Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes
<p>This dataset contains some of the raw and preprocessed data presented in the manuscript "Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes" submitted to Nature Communications. Detailed information on each individual file is as follows: </p> <ul> <li><strong>256ch_device2_impedance_spectroscopy.csv:</strong> Impedance magnitudes presented in Fig. 2b.</li> <li><strong>rat1_impedances.csv:</strong> Impedance magnitudes belonging to Rat #1 (presented in Fig. 4c).</li> <li><strong>rat2_impedances.csv:</strong> Impedance magnitudes belonging to Rat #2 (presented in Fig. 4c).</li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_neuron.tif:</strong> Max. intensity projection image of Nissl staining shown in Fig. 4g. </li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_IBA.tif:</strong> Max. intensity projection image of IBA staining shown in Fig. 4g. </li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_GFAP.tif:</strong> Max. intensity projection image of GFAP staining shown in Fig. 4g. </li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_neuron_4x4bins.tif: </strong>z-stack-averaged and binned image of Nissl staining used in histology analysis shown in Fig. 4g.</li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_IBA_4x4bins.tif:</strong> z-stack-averaged and binned image of IBA staining used in histology analysis shown in Fig. 4g.</li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_GFAP_4x4bins.tif:</strong> z-stack-averaged and binned image of GFAP staining used in histology analysis shown in Fig. 4g.</li> <li><strong>neuron_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for Nissl staining (Fig. 4g). </li> <li><strong>gfap_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for GFAP staining (Fig. 4g). </li> <li><strong>iba_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for IBA staining (Fig. 4g). </li> <li><strong>256ch_device2_phase_spectroscopy.csv: </strong>Impedance phases presented in Supplementary Fig. 7a.</li> <li><strong>rat1_impedance_phases.csv: </strong>Impedance phases belonging to Rat #1 (presented in Supplementary Fig. 7b).</li> <li><strong>rat2_impedance_phases.csv:</strong> Impedance phases belonging to Rat #2 (presented in Supplementary Fig. 7b).</li> <li><strong>mouseLL2_impedances_magnitudes.csv:</strong> Impedance magnitudes presented in Supplementary Fig. 9a.</li> <li><strong>mouseLL2_impedances_phases.csv: </strong>Impedance phases presented in Supplementary Fig. 9b. </li> <li><strong>mouseLL2_single_unit_SNRs.csv: </strong>Single unit SNRs presented in Supplementary Fig. 9c.</li> <li><strong>mouseLL2_single_unit_lifetimes.csv: </strong>Single unit lifetimes presented in Supplementary Fig. 9d. </li> <li>Figure_5_data.mat: Data used in Figure 5 (can be imported into the corresponding Matlab script in the GitHub repository).</li> </ul> <p>The rest of the data supporting the figures is provided in the Source File and Supplementary Data files, which are available through the online version of the article. Any additional requests for information can be directed to, and will be fulfilled by, the corresponding author. </p>
Supplemental Figures for "On the comparative utility of entropic learning versus deep learning for long-range ENSO prediction"
<p>Supplemental figures for the paper "On the comparative utility of entropic learning versus deep learning for long-range ENSO prediction".</p>
AusENDVI: A long-term NDVI dataset for Australia
<p>AusENDVI (<strong>Aus</strong>tralian <strong>E</strong>mprical <strong>NDVI</strong>) is a monthly, 5-km gridded estimate of NDVI across Australia from 1982-2022. It is built by calibrating and harmonising NOAA's Climate Data Record AVHRR NDVI data to MODIS MCD43A4 NDVI using a gradient boosting ensemble decision tree method. Additionally, the datasets are gapfilled using a synthetic NDVI dataset. The methods are extensively described in an <a href="https://doi.org/10.5194/essd-16-4389-2024">Earth System Science Data publication.</a></p> <p>AusENDVI consists of several datasets, each dataset has a description in the attributes of the NetCDF file that describes its provenance. The naming convention is "AusENDVI_<model_type>_<year_range>_<version>.nc". </p> <ol> <li> <p><em>AusENDVI-clim_gapfilled_1982_2013</em>. Calibrated and harmonised Climate Data Record AVHRR NDVI data from Jan. 1982 to Dec. 2013. This version of the dataset used climate data in the calibration and harmonisation process and has the best agreement statistics with MODIS MCD43A4 NDVI. The dataset has been gap filled using the methods described in the accompanying publication.</p> </li> <li><em>AusENDVI-clim_MCD43A4_gapfilled_1982_2022</em>. This dataset consists of calibrated and harmonised NOAA Climate Data Record AVHRR NDVI data from Jan. 1982 to Feb. 2000, joined with MODIS-MCD43A4 NDVI data from Mar. 2000 to Dec. 2022. This version of the dataset _used climate data_ in the calibration and harmonisation process. The dataset has been gapfilled using the methods described in the accompanying publication</li> <li> <p><em>AusENDVI-noclim_1982_2013</em>. Calibrated and harmonised Climate Data Record AVHRR NDVI data from Jan. 1982 to Dec. 2013. This version of the dataset did not use climate data in the calibration and harmonisation process and the dataset has not been gap filled.</p> </li> <li> <p><em>AusENDVI-synthetic_1982_2022</em>. This dataset consists of synthetic NDVI data that was built by training a model on the joined _AusENDVI-clim_ and _MODIS-MCD43A4 NDVI_ timeseries using climate, woody-cover-fraction, and atmospheric CO2 as predictors. The synthetic NDVI is used for gap filling.</p> </li> </ol> <p>All datasets are in 'EPSG:4326' projection, and have a spatial resolution of 0.05 degrees. Geographic coordinate information is contained in the `spatial_ref` variable. </p> <p>A <strong>Jupyter Notebook </strong>is also provided that shows how to load, plot, QC mask, reproject, and gap-fill AusENDVI datasets. The notebook is effectively a 'readme' file.</p> <ul> <li>The notebook is also available to view/download <a href="https://nbviewer.org/github/cbur24/AusENDVI/blob/main/notebooks/analysis/AusENDVI_loading_example.ipynb">here</a></li> </ul> <p>An open-source <strong>github repository </strong>details the methods used to create these datasets</p> <ul> <li>https://github.com/cbur24/AusENDVI</li> </ul> <p> </p> <p>A few small changes to the datasets were implemented in <strong>version 0.2.0:</strong></p> <ol> <li>All datasets now have their values clipped to the range 0-1</li> <li>The AusENDVI-clim dataset is now gapfilled, and includes a QC layer</li> <li>The merged <em>AusENDVI-noclim_MCD43A4_1982_2022</em> dataset was removed to simplify the number of datasets included in the repository. Users who want to join the 'noclim' and MODIS datasets can do so by clipping out MCD43A4 from the <em>AusENDVI-clim_MCD43A4_gapfilled_1982_2022 </em>dataset.</li> <li>The accompanying Jupyter Notebook 'readme' has been updated.</li> </ol>
Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots
<h1>Dataset and code description</h1> <p>This repository contains the codes and data for theScience Robotics paper <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>.</p> <p>The codes are in <strong>rr_scirob_analyses</strong> and the datasets are in <strong>rr_scirob_data</strong>.<strong> </strong>If you want to rerun the data processing as presented in the paper, you need both <strong>rr_scirob_analyses</strong> and <strong>rr_scirob_data. </strong>You can copy the contents of <strong>rr_scirob_data </strong>into <strong>rr_scirob_analyses, </strong>as they have the same folder structure. Alternatively, you can run the <strong>download </strong>scripts to obtain the partial datasets relevant for certain subfigures. The file <strong>rr_scirob_data_readmes</strong> contains more detailed README files (rosbag info). You can copy its contents to <strong>rr_scirob_analyses </strong>after copying the contents of the <strong>rr_scirob_data</strong>.</p> <p>The individual datasets are organised into seven folders.</p> <h2>Three Figures with Key Behavioural Metrics </h2> <p>Three of the folders correspond to the Key Behavioural Measures, which are presented in three figures in the paper. These are:</p> <ul> <li>Figure-2-KBM-1-Queen Queen - related Key Behavioural Metrics</li> <li>Figure-3-KBM-2-Workers Worker Bee - related Key Behavioural Metrics</li> <li>Figure-4-KBM-3-Comb Comb and Brood -related Key Behavioural Metrics </li> </ul> <p>Each of these <em>Figure-X</em> folders contains the relevant figure from the paper and four subfolders corresponding to the panels of that figure. These are <strong>macro</strong>, <strong>micro</strong>, <strong>mezo</strong>, <strong>social</strong>, related to the four panels of that figure.<br>Each of these subfolders contains a README file, describing how to process the data and providing further details. <br>Furthermore, there are three additional folders located in each of the 'panel' folder:</p> <ul> <li><strong>data</strong>: this is used to store the data necessary to generate the graphs. You can either populate it with the data from Zenodo, i.e., https://zenodo.org/records/13801588 Alternatively, you can use the `download.sh` script wich will download and extract the necessary data from the RoboRoyale project cloud.</li> <li><strong>tmp</strong>: This folder is used to store intermediate results of the processing scripts</li> <li><strong> output</strong>: This folder is used to store all the generated outputs of the individual scripts. These should be identical with the panels of the figure in the paper. These figures are also provided in the relevant folders.</li> </ul> <p>Running the scripts contained in the micro, mezo, macro and social folders generates images and graphs in the output subfolders. These should be identical to the ones in the panels of Figures 2-4 in the paper.</p> <h2>One Resting Analysis Figure</h2> <p>One folder corresponds to the queen resting analysis figure</p> <ul> <li>Figure-5-Resting : Queen resting time analysis</li> </ul> <p>This folder has three subfolders named <strong>data</strong>, <strong>tmp</strong> and <strong>output</strong> similar to the previous folders. Again, running the scripts will generate the figures and/or run the statistical tests as in the previous case.</p> <h2>Three Performance Assessments: Queen Tracking, Workerbee Localisation and Oviposition Detection</h2> <p>Three other folders are related to performance analysis of the core methods required to calculate the KBMs.</p> <ul> <li>KBM-1-performance evaluation: Provides datasets and scripts to assess the performance of the queen marker detector</li> <li>KBM-2-performance evaluation: Provides datasets and scripts to assess the performance of the worker bee detector</li> <li>KBM-3-performance evaluation: Provides datasets and scripts to assess the performance of the oviposition detector </li> </ul> <p>Each of these folders contains a README file explaining what to run in order to evaluate the performance of the method and to replicate the paper's results.</p> <h2>Additional materials and data</h2> <p>The core data used here is the month-long queen tracking information, consisting of 28 million entries in a file <strong>2023-month-queenpos-short.txt.</strong> <br>A description of the file structure is provided in the README of the relevant KBM folder.</p> <p>Additional data are available in the dataset section of https://roboroyale.eu.</p> <h2>Rosbags</h2> <p>The work is based on the Robot Operating System (ROS) and thus, the raw data come in the form of rosbags. We provide a few of the rosbags to allow checking examples of video and other raw data as reported by the system:</p> <ul> <li>2023-10-25-08-42-20-Queen-Feeding.bag - queen feeding (KBM-1 Social)</li> <li>KPI1_2_mezo-queen_walk_sample.bag - queen walk as drawn in (KBM-1 Mezo)</li> <li>2023-10-10-00-04-10-trophylaxis.bag - worker bee trophylaxis (KBM-2 Social)</li> <li>2023-09-19-09-00-20-egg-removal.bag - worker bee removing egg (KBM-2 Social)</li> </ul> <h2>Licence </h2> <p>This data and code are under the Creative Commons Attribution-ShareAlike 4.0 International license. If you use these data in your work, please <strong>cite</strong> the relevant paper, i.e., Ulrich, Stefanec, Rekabi-bana et al.: <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>. Science Robotics, 2024.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.