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651 results for “The Netherlands”
The Netherlands - China Low Frequency Explorer signal chain pre launch test-data. (version 1.0)
<p>The Netherland Chinese Low Frequency Explorer Pre launch system ground test results with analog and digital signal chain. The tests include the instrument in various experiment settings that are avalable as pre-set functions during the observation phase.</p> <p>The data set is processed to L1B data, a script is provided together with the data to process this and plot the power spectra for all the monopole antennas.</p> <p> </p>
Netherlands F3 Interpretation Dataset
<p><strong>Netherlands F3 Interpretation Dataset</strong></p> <p>Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. Such a fact is arguably due to three main factors: powerful computers, new techniques to train deeper networks and more massive datasets. Although the first two are readily available in modern computers and ML libraries, the last one remains a challenge for many domains. It is a fact that big data is a reality in almost all fields today, and geosciences are not an exception. However, to achieve the success of general-purpose applications such as ImageNet - for which there are +14 million labeled images for 1000 target classes - we not only need more data, we need more high-quality labeled data. Such demand is even more difficult when it comes to the Oil & Gas industry, in which confidentiality and commercial interests often hinder the sharing of datasets to others. In this letter, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation. The Netherlands F3 dataset was acquired in the North Sea, offshore Netherlands. The data is publicly available and comprises pos-stack data, eight horizons and well logs of 4 wells. However, for the dataset to be of practical use for our tasks, we had to reinterpret the seismic, generating nine horizons separating different seismic facies intervals. The interpreted horizons were used to create 651 labeled masks for inlines and 951 for crosslines. We present the results of two experiments to demonstrate the utility of our dataset. </p> <p><strong>Dataset contents</strong></p> <ul> <li>Crosslines: <ul> <li>Classes: 10</li> <li>Number of slices: 651</li> <li>Records per class: 9,440</li> <li>Total of records: 94,400</li> </ul> </li> <li>Inlines: <ul> <li>Classes: 10</li> <li>Number of slices: 951</li> <li>Records per class: 9,720 <ul> <li>Total of records: 94,720</li> </ul> </li> </ul> </li> <li>Configuration: <ul> <li>Crop: [0, 0, 0, 0]</li> <li>Gray levels: 256</li> <li>Noise: 0.3</li> <li>Percentile: 5.0</li> <li>Strides: [20, 48]</li> <li>Tile shape: [25, 64, 1]</li> </ul> </li> </ul>
A Multi-Year Data Set of Beach-Foredune Topography and Environmental Forcing Conditions at Egmond aan Zee, the Netherlands
<p>The data set contains 39 digital elevation models and 11 orthophotos of a beach-foredune system near Egmond aan Zee, the Netherlands, a high-wave storm-dominated site with an approximately 25 m high foredune. The elevation data set combines a long duration (six years; January 2013 - January 2019) with a high temporal resolution (typically 2-4 months) and is spatially extensive (1.4 km alongshore) with a high spatial (1 m) resolution. To facilitate the testing and further development of coastal dune evolution models, the data set is supplemented with high-frequency time series of offshore wave, water level and wind characteristics as well as several subtidal bathymetries.</p><p>The data set is described in detail in the following open-access, peer-reviewed paper:</p><p>Ruessink, G.; Schwarz, C.S.; Price, T.D.; Donker, J.J.A. A Multi-Year Data Set of Beach-Foredune Topography and Environmental Forcing Conditions at Egmond aan Zee, The Netherlands. <i>Data</i> <strong>2019</strong>, <i>4</i>, 73. <a href="https://doi.org/10.3390/data4020073">https://doi.org/10.3390/data4020073</a></p><p>Update December 7, 2023: The data descriptor paper contains a typo related to the rotation of the RD and local coordinate schemes. On page 4/15 it is said that this rotation angle is 177 degrees, it should be 172.8 degrees. A big thank-you to Haoyang Peng (UNSW, Australia) for pointing out that the 177 degrees is incorrect. </p><p> </p><p> </p>
ENERGISE Living Lab country report _ Netherlands
<p>ENERGISE Living Labs (ELLs) employ practice-based approaches to reduce energy use in households while co-creating knowledge on why energy-intensive practices are performed and how they depend on the context in which they are performed. Altogether 16 living labs were implemented in eight European countries in 2018.</p> <p><br> The Dutch ELLs were led by the ENERGISE team from Maastricht University, in Maastricht in the Netherlands. The ENERGISE Living Labs were implemented in the Southern most province in the Netherlands, Limburg – in two municipalities. Maastricht for ELL1 and Roermond for ELL2, the community-based ELL. Participants were recruited with the help of a local implementation partner Op het Zuiden.</p>
Monitoring long-term peat subsidence with subsidence platens in Zegveld, The Netherlands
<p><span>Peat oxidation in peat meadow areas is causing greenhouse gas emissions as well as land subsidence. Due to yearly fluctuations in soil surface level, long-term monitoring is needed to determine long-term net subsidence rates. In the experimental peat-meadow farm at Zegveld (NL) subsidence platens were installed in 1970 in a field with low ditchwater level, and in 1973 in a field with high ditchwater level. Platens were installed at 7 different depths, allowing to investigate where in the peat profile subsidence occurs. Elevation of platens as well as soil surface has been measured with surveyor’s levelling each year at the end of winter, so that a long timeseries up to 2023 is available. Analysis showed that surface level in the field with high ditchwater level subsided by 23 cm in 50 years (4.6 mm/yr), while in the field with low ditchwater level this was 31 cm in 53 years (5.8 mm/yr). Results also showed that in the field with low ditch water level, most subsidence due to permanent shrinkage and peat oxidation occurred between 40 and 100 cm depth, while for the other field this was between 20 and 40 cm depth. Finally, in 2023 subsidence was still observed under continuously saturated conditions at 140 cm depth. Presumably, in the aerated part of the profile peat oxidation and the associated earthification process is the main cause of subsidence, while the observed subsidence in the saturated soil at 140 cm depth must be due to other processes, such as consolidation and creep.</span></p>
National Checklists 2017: The Netherlands Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from The Netherlands collected using effechecka and geonames polygons
National Checklists 2019: The Netherlands Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from The Netherlands collected using effechecka and geonames polygons
Country-wide data products for the ecosystem structure metrics derived from ALS data across the Netherlands (AHN3)
<p>This data repository contains country-wide data products for the ecosystem structure metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands (AHN3). Twenty-five ecosystem structure metrics (at 10-meter resolution, GeoTIFF format) were derived from AHN3 dataset (<a href="https://downloads.pdok.nl/ahn3-downloadpage/">https://downloads.pdok.nl/ahn3-downloadpage/</a>) using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow (<a href="../record/5636773">https://zenodo.org/record/5636773</a>). Laserfarm is a free and open-source workflow that enables efficient, scalable, and distributed processing of multi-terabyte LiDAR point clouds from national and regional ALS surveys into LiDAR metrics of ecosystem structure. All code of Laserfarm is hosted and freely available on GitHub (<a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>). The Jupyter Notebooks for the processing of the AHN3 dataset are available on GitHub (<a href="https://github.com/eEcoLiDAR/AHN/tree/main/AHN3">https://github.com/eEcoLiDAR/AHN/tree/main/AHN3</a>).</p> <p>The twenty-five LiDAR metrics are related to three key dimensions of ecosystem structure (ecosystem height, ecosystem cover, and ecosystem structural complexity), and a layer of point density and a layer of building/road/water mask are also provided. Each GeoTIFF layer represents one LiDAR metric at 10 m resolution covering the whole Netherlands (file name as "ahn3_10m_feature_name.tiff").</p> <p>An overview of all the listed metrics (maps) is also provided in the PDF version (AHN3.pdf).</p> <p>A detailed description of the dataset is available from the following data publication:<br>Kissling, W. D., Y. Shi, Z. Koma, C. Meijer, O. Ku, F. Nattino, A. C. Seijmonsbergen, and M. W. Grootes. 2022. Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey. Data in Brief: 108798.<br><a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1016%2Fj.dib.2022.108798&data=05%7C01%7Cy.shi%40uva.nl%7C177a19a4359a422b0ef808dad9d30ef8%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C638061797757145956%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=2R7NSGli4Mw6Pp5FAIyOzBu4USPZXigng46EFVT4X68%3D&reserved=0">https://doi.org/10.1016/j.dib.2022.108798</a></p> <p>A detailed description of all the metrics can be found in the README file (README.docx). </p> <p>A .zip file is also provided containing all the data for the validation of the AHN3 data products (AHN3_validation.zip). </p>
Navigating the complex policy landscape for carbon farming in The Netherlands and the EU -- Open Research Europe Extended Data-- Tables 1-6, Figures 1-2
<p>This is extended data for the article entitle 'Navigating the complex policy landscape for carbon farming in The Netherlands and the EU' submitted to Open Research Europe by Eise Spijker. </p>
Extinction Rebellion Netherlands: Dutch Climate Activism Tweets (2020-2023)
<p>This dataset contains text data from Extinction Rebellion Netherlands (XR NL) tweets between January 1, 2020, and December 31, 2023. It captures key moments in the Dutch climate activism movement, focusing on themes such as environmental protests, fossil fuel resistance, and civil disobedience. The tweets reflect XR NL’s efforts in organizing non-violent direct actions and blockades, including significant events like the A12 highway protests and Schiphol airport demonstrations. Central themes include the climate crisis, sustainability, and the ecological emergency, highlighting the movement’s focus on climate justice and the call for urgent government action in the Netherlands.</p>
Energy and cost calculations for retrofitting packages based on Tabula building archetypes in the Netherlands
<div> <div> <div> <div> <p>The dataset contains detailed energy and cost calculations for various retrofitting packages applied to different Tabula building archetypes in the Netherlands. The energy balance calculations are structured by building type and age categories, such as DH (detached house) and SD (semi-detached house) from different time periods (e.g., 1965-1974). The sheets include calculations of existing building performance, proposed retrofit scenarios, and associated energy savings. Cost breakdowns are provided for each retrofit option, detailing specific construction costs, taxes, and subsidies available for each scenario. This comprehensive dataset integrates both the technical (energy savings and U-values) and financial (costs and subsidies) aspects of retrofitting to provide a holistic view of retrofitting strategies in the Netherlands. </p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
HOLSEA-NL: Holocene water level and sea-level indicator dataset for the Netherlands
<p>This dataset contains an assembly of geological water-level indicators, relevant for studying relative sea level rise (RSLR), regional subsidence quantification and causal breakdown, coastal prism accommodation and Holocene aggradation chronology of the Holocene Netherlands. It gives a sources-referenced, uniform overview of 658 basal geological water-level indicators collected from original research of various type and application (140 primary references). From the indicators, 59% was collected in 1950-2000, mainly in academic studies and survey mapping campaigns; 37% was collected in 2000-2020 in academic studies and archaeological surveying projects, 4% was newly collected (this study), the latter mainly in previously under sampled central and northern Netherlands regions. 117 are true sea-level indicators (so-called SLIPs), the majority of datapoints (536) are inland water level indicators that are upper limiting to sea-level. The total number of entries is 712, because we included some literature mentioned rejected samples and deep positioned intercalated water level indicators.</p> <p>The dataset is compiled in the so-called HOLSEA workbook format. It covers measured, calculated and classification fields defining the geological observational data and its uncertainties, allowing to document and assess indicative meaning adapted to specific use variants. Hereto, the workbook contains expansions to the original format. See Related Works (ESSD paper: De Wit et al. 2024).</p>
Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers [Data set].
<p>Data set covering the meta data of the 39 well fields, the macro chemistry data and the data of the noble gases and carbon, hydrogen and oxygen isotope tracers used for assessing the paleoclimate signals and age distributions in the publication in Water Resources Research (2021)</p> <p><strong>Paleoclimate signals and groundwater age distributions from 39 public water works in the Netherlands; insights from noble gases and carbon, hydrogen and oxygen isotope tracers</strong></p> <p>Hans Peter Broers, Jürgen Sültenfuß<sup> </sup>, Werner Aeschbach, Arne Kersting,, Armin Menkovich, Jasperien de Weert and Jeroen Castelijns</p>
CoMix social contact data (Netherlands)
<p>Social contact data for Netherlands from the CoMix survey.<br> Changelog:<br> <br> Version 2: Updated time frame.<br> </p>
Soil biological, chemical and physical parameters and herbage yield in a field experiment with organic and inorganic fertilizers on peat grassland in the Netherlands
<p>To evaluate the performance of organic and inorganic fertilizers for regeneration of ecosystem services in peat grasslands with biodiversity goals, we carried out a field experiment in the western peat district in the Netherlands. The fertilizers tested represent the current practice and potential alternatives for regenerative grassland management on drained peat.</p> <p> </p> <p><strong>Experimental setup</strong></p> <p>The field experiment (2013 – 2015) was conducted on a permanent grassland on peat soil (Terric Histosol; SOM 56 g 100 g<sup>−1</sup> and pH<sub>KCl</sub> of 4.5 in 0-10 cm) at the experimental dairy farm at Zegveld (the Netherlands). In March 2013, a randomized block experiment (six blocks) was laid out with six fertilizer treatments and a control treatment (no fertilizer: “Contr”). The fertilizer used were: conventional dairy cattle slurry manure (“Slurry”), mature compost of kitchen and garden waste (“Comp”), dairy cattle farmyard manure (“FYM”), solid fraction of the cattle slurry manure (“SFrac”, obtained by pressurized filtration), inorganic N fertilizer (“IF”; calcium ammonium nitrate, 27% N) and a combination of inorganic N fertilizer and sawdust (“IF+SD”). Plot size was 4 × 10 m; for the Slurry treatment plots were 5.2 × 10 m. Slurry was applied by slit injection, the other fertilizers were applied by hand. Target application rate was 120 kg total N ha<sup>−1</sup> yr<sup>−1</sup>, divided in two applications per year (February/March and May). This is relatively low for conventional grasslands but usual for grasslands with biodiversity goals (Kleijn et al., 2004). The amount of C<sub>total</sub> applied in Comp was taken for the rate of sawdust to be applied. All plots were fertilized with 200 kg K<sub>2</sub>O ha<sup>−1</sup> yr<sup>−1</sup> (applications in March and May) (Commissie Bemesting Grasland en Voedergewassen, 2019). Fertilizer application quantities and organic matter and nutrient inputs are provided in Fertilizer_intput.csv (dataset).</p> <p>The grassland had an history of conventional management with mainly cutting, winter grazing with sheep and a normal fertilization regime with both slurry manure and inorganic fertilizer. The normal cutting and grazing regime was continued in the first two years of the experiment; during 2015, the monitoring year, the plots were not grazed and only cut for herbage measurements.</p> <p> </p> <p><strong>Measurements</strong></p> <p>From April to October 2015, soil and aboveground measurements were carried out. Most soil parameters were measured in October. Earthworms and insect larvae are an important food source for meadow birds during the pre-breeding period in spring (Galbraith, 1989) and were therefore sampled in April. Soil moisture and penetration resistance were measured both in April and October.</p> <p> </p> <p><em>Soil biological parameters</em></p> <p>Earthworms and insect larvae were sampled in the top soil layer in two soil cubes (20 × 20 × 20 cm) per plot. Earthworms were hand-sorted, counted, weighed and fixed in alcohol prior to identification. Both adults and juveniles were identified to species (Sims and Gerard, 1985; Stöp-Bowitz, 1969) and classified into functional groups (Bouché, 1977). Crane flies (Tipulidae; leatherjackets) or click beetles (Elateridae; wireworms) larvae were counted.</p> <p>Phospholipid fatty acids (PLFA) were measured in October. PLFA were extracted from 4 g of fresh soil (Palojärvi, 2006), and analyzed by gas chromatography (Hewlett-Packard, USA). PLFA i15:0, a15:0, 15:0, i16:0, 16:1ω9, i17:0, a17:0, cy17:0, 18:1ω7 and cy19:0 were chosen to represent bacteria and PLFA 18:2ω6 was used as a marker of saprotrophic fungi (Hedlund, 2002). The neutral lipid fatty acid (NLFA) 16:1ω5 occurs in storage lipids of arbuscular mycorrhizal fungi (AMF) and was used as marker of AMF (Vestberg et al., 2012). PLFA i15:0, a15:0, i16:0, i17:0 and a17:0 were used as a measure of Gram-positive bacteria, and cy17:0 and cy19:0 for Gram-negative bacteria. PLFA 10Me16:0, 10Me17:0 and 10Me18:0 represented actinomycetes.</p> <p> </p> <p><em>Soil chemical parameters</em></p> <p>A soil sample from the 0−10 cm layer (c. 50 randomly taken soil cores) per experimental plot was collected in October (auger diameter 2.3 cm; Eijkelkamp grass plot sampler, Giesbeek, the Netherlands), was sieved (1 cm mesh size) and homogenized. One sub-sample was taken for analysis of hot water extractable carbon (HWC) according to Ghani et al. (2003) and one for chemical analysis. Prior to analysis of soil acidity (pH<sub>KCl</sub>), soil organic matter (SOM), total carbon (C<sub>total</sub>), total nitrogen (N<sub>total</sub>), total phosphorus (P<sub>total</sub>) and ammonium-lactate extractable P (P<sub>AL</sub>) by Eurofins Agro (Wageningen, the Netherlands), the sub sample was dried at 40°C. Soil pH<sub>KCl</sub> was measured according to NEN-ISO 10390 2005. SOM was determined by loss-on-ignition (NEN 5754 2005). C<sub>total</sub> was measured by incineration at 1150°C, and determination of the CO<sub>2</sub> produced by an infrared detector (LECO Corporation, St. Joseph, Mich., USA). For N<sub>total</sub>, evolved gasses after incineration were reduced to N<sub>2</sub> and measured with a thermal-conductivity detector (LECO Corporation, St. Joseph, Mich., USA). P<sub>total</sub> was analysed with Fleishmann acid (Houba et al., 1997). P<sub>AL</sub> is used to assess the P supply capacity of grassland soils (Reijneveld et al., 2014) and was determined according to Egnér et al. (1960) (NEN 5793).</p> <p> </p> <p><em>Soil physical parameters</em></p> <p>Soil moisture was determined in April and October in a homogenized 0−10 cm soil sample after drying at 105°C for 24 hrs. Moisture content was expressed as percentage of fresh soil weight.</p> <p>Penetration resistance was measured (April and October) with a penetrologger (Eijkelkamp, Giesbeek, the Netherlands; cone of 2.0 cm<sup>2</sup> penetration surface and 60° apex angle. Penetration resistance was expressed as an average of 7 penetrations per plot and per soil layer of 0−10, 10−20, and 20−30 cm.</p> <p>Soil structure and rooting density were assessed in October in the 0−10 cm and 10−25 cm layers. The percentage of crumbs, sub-angular blocky elements and angular blocky elements was estimated by one experienced person as described by Peerlkamp (1959) and Shepherd (2000), Root density was estimated by scoring visible roots (score 1–10; 1 for no roots and 10 for above average).</p> <p>Water infiltration rate was measured in October at three spots per experimental plot in 5 of the 6 blocks (35 plots). A PVC pipe (15 cm high, 15 cm diameter) was pushed into the soil to a depth of 10 cm. 500 ml water was poured into each pipe and the infiltration time was recorded. If the infiltration time exceeded 15 min, the remaining water volume was estimated to calculate the infiltration rate (mm min<sup>−1</sup>).</p> <p> </p> <p><em>Grass yield and botanical composition</em></p> <p>Grass dry matter (DM) and N yield were determined during 2015 with a Haldrup plot harvester (J. Haldrup a/s, Løgstør, Denmark). The four harvest dates were May 15, June 29, August 19 and September 30. Fresh biomass, DM content (70°C for 24 hrs) and total N content (Kjeldahl) were determined for each harvest. Herbage DM yield (Mg DM ha<sup>−1</sup>) and herbage N yield (kg N ha<sup>−1</sup>) were calculated. Apparent N recovery (ANR; kg N.kg N<sup>−1</sup>) was calculated as (N yield<sub>(fertilized)</sub> – N yield<sub>(non-fertilized)</sub>)/(N fertilization rate) (Vellinga and André, 1999).</p> <p>In June 2015, botanical composition was measured by visually estimating the relative soil cover of the sward and the proportion of each species therein (Sikkema, 1997).</p> <p> </p> <p><strong>Data files</strong></p> <ul> </ul> <p> </p> <p><em><strong>Data_soil_grass.csv</strong></em></p> <p><em>Content:</em></p> <p>Dataset with soil biological (earthworms, microbial PLFA), soil chemical, soil physical parameters, herbage dry matter and N yields, and botanical parameters.</p> <p><em>Column names and units:</em></p> <ul> <li>plot: Experimental plot number (1-42)</li> <li>treatment: Treatment code (see text)</li> <li>block: Block number (1-6)</li> <li>EW_species_number: Earthworm - number of species</li> <li>EW_totalnumber: Earthworm - total number per m2</li> <li>EW_epigeic: Earthworm - number of epigeic adults and juveniles per m2</li> <li>EW_endogeic: Earthworm - number of endogeic adults and juveniles per m2</li> <li>EW_adults: Earthworm - number of adults per m2</li> <li>EW_juveniles: Earthworm - number of juveniles per m2</li> <li>EW_adult_epigeic: Earthworm - number of epigeic adults per m2</li> <li>EW_adult_endogeic: Earthworm - number of endogeic adults per m2</li> <li>EW_juven_epigeic: Earthworm - number of epigeic juveniles per m2</li> <li>EW_juven_endogeic: Earthworm - number of endogeic juveniles per m2</li> <li>EW_L_rubellus: Earthworm - number of L. rubellus adults and juveniles per m2</li> <li>EW_A_chlorotica: Earthworm - number of A. chlorotica adults and juveniles per m2</li> <li>EW_A_caliginosa: Earthworm - number of A. caliginosa adults and juveniles per m2</li> <li>EW_O_lacteum: Earthworm - number of O. lacteum adults and juveniles per m2</li> <li>EW_A_rosea: Earthworm - number of A. rosea adults and juveniles per m2</li> <li>EW_O_cyaenum: Earthworm - number of O. cyaneum adults and juveniles per m2</li> <li>EW_L_castaneus: Earthworm - number of L. castaneus adults and juveniles per m2</li> <li>EW_D_rubida: Earthworm - number of D. rubida adults and juveniles per m2</li> <li>EW_adult_L_rubellus: Earthworm - number of L. rubellus adults per m2</li> <li>EW_adult_A_chlorotica: Earthworm - number of A. chlorotica adults per m2</li> <li>EW_adult_A_caliginosa: Earthworm - number of A. caliginosa adults per m2</li> <li>EW_adult_O_lacteum: Earthworm - number of O. lacteum adults per m2</li> <li>EW_adult_A_rosea: Earthworm - number of A. rosea adults per m2</li> <li>EW_adult_O_cyaenum: Earthworm - number of O. cyaneum adults per m2</li> <li>EW_adult_L_castaneus: Earthworm - number of L. castaneus adults per m2</li> <li>EW_adult_D_rubida: Earthworm - number of D. rubida adults per m2</li> <li>EW_juven_L_rubellus: Earthworm - number of L. rubellus juveniles per m2</li> <li>EW_juven_A_chlorotica: Earthworm - number of A. chlorotica juveniles per m2</li> <li>EW_juven_A_caliginosa: Earthworm - number of A. caliginosa juveniles per m2</li> <li>EW_non_determined: Earthworm - number of non determined individuals per m2</li> <li>EW_total_biomass: Earthworm - total fresh biomass per m2</li> <li>Leatherjackets: number of leatherjackets per m2</li> <li>Wireworms: number of wireworms per m2</li> <li>TOTmicrPLFA: total microbial PLFA in nmol.g-1 dry soil</li> <li>bactPLFA: bacterial PLFA in nmol.g-1 dry soil</li> <li>saprofungPLFA: saprotrophic fungal PLFA in nmol.g-1 dry soil</li> <li>Fung_bactPLAF_ratio: ratio of fungal to bacterial PLFA</li> <li>GramPLUSplfa: gram positive PLFA in nmol.g-1 dry soil</li> <li>GramMINplfa: gram negative PLFA in nmol.g-1 dry soil</li> <li>ratioGram_PLUS_MIN: ratio of gram positive to gram negative PLFA</li> <li>AMFsporNLFA: AMF spores NLFA in nmol.g-1 dry soil</li> <li>ActinomPLFA: Actinomycetes PLFA in nmol.g-1 dry soil</li> <li>ShannonPLFA: PLFA shannon diversity index</li> <li>SOM: soil organic matter in g.100 g-1 dry soil</li> <li>Ctotal: total C in g.100 g-1 dry soil</li> <li>HWC: hot water extractable C in μg.100 g-1 dry soil</li> <li>Ntotal: total N in g.100 g-1 dry soil</li> <li>Ptotal: total P2O5 in mg.100 g-1 dry soil</li> <li>P_AL: total P-AL in mg.100 g-1 dry soil</li> <li>pH_KCl: pH-KCl</li> <li>CN_ratio: C:N ratio</li> <li>C_SOM: C:SOM ratio</li> <li>Soilmoisture_April: soil moisture content in April in g.100g-1 fresh soil</li> <li>Penetrationresistance_April_cm010: penetration resistance in April in 10-20 cm in Newton</li> <li>Penetrationresistance_April_cm1020: penetration resistance in April in 20-30 cm in Newton</li> <li>Penetrationresistance_April_cm2030: penetration resistance in April in 0-10 cm in Newton</li> <li>Soilmoisture_October: soil moisture content in October in g.100g-1 fresh soil</li> <li>Penetrationresistance_October_cm010: penetration resistance in October in 10-20 cm in Newton</li> <li>Penetrationresistance_October_cm1020: penetration resistance in October in 20-30 cm in Newton</li> <li>Penetrationresistance_October_cm2030: penetration resistance in October in 0-10 cm in Newton</li> <li>crumb_struct_cm010: percentage of crumb elements in 0-10 cm</li> <li>round_struct_cm011: percentage of sub-angular elements in 0-10 cm</li> <li>rootdensity_cm010: score (1-10) of root density in 0-10 cm</li> <li>crumb_struct_cm1025: percentage of crumb elements in 10-25 cm</li> <li>round_struct_cm1025: percentage of sub-angular elements in 10-25 cm</li> <li>sharp_struct_cm1025: percentage of angular elements in 10-25 cm</li> <li>rootdensity_cm1025: score (1-10) of root density in 10-25 cm</li> <li>water_infiltration: water infiltration rate in mm per minute</li> <li>DM_yield_year: total herbage dry matter yield in kg.ha-1 per year</li> <li>DM_yield_H1: herbage dry matter yield of harvest 1 in kg.ha-1</li> <li>DM_yield_H2: herbage dry matter yield of harvest 2 in kg.ha-1</li> <li>DM_yield_H3: herbage dry matter yield of harvest 3 in kg.ha-1</li> <li>DM_yield_H4: herbage dry matter yield of harvest 4 in kg.ha-1</li> <li>N_yield_year: total herbage N yield in kg.ha-1 per year</li> <li>N_yield_H1: herbage N yield of harvest 1 in kg.ha-1</li> <li>N_yield_H2: herbage N yield of harvest 2 in kg.ha-1</li> <li>N_yield_H3: herbage N yield of harvest 3 in kg.ha-1</li> <li>N_yield_H4: herbage N yield of harvest 4 in kg.ha-1</li> <li>DMperc_yield_year: herbage dry matter content (per year; weighed average over the 4 harvests) in g.100g-1 fresh weight</li> <li>DMperc_yield_H1: herbage dry matter content of harvest 1 in g.100g-1 fresh weight</li> <li>DMperc_yield_H2: herbage dry matter content of harvest 2 in g.100g-1 fresh weight</li> <li>DMperc_yield_H3: herbage dry matter content of harvest 3 in g.100g-1 fresh weight</li> <li>DMperc_yield_H4: herbage dry matter content of harvest 4 in g.100g-1 fresh weight</li> <li>Ncontent_yield_year: herbage N content (per year; weighed average over the 4 harvests) in g.kg-1 dry matter</li> <li>Ncontent_yield_H1: herbage N content of harvest 1 in g.kg-1 dry matter</li> <li>Ncontent_yield_H2: herbage N content of harvest 2 in g.kg-1 dry matter</li> <li>Ncontent_yield_H3: herbage N content of harvest 3 in g.kg-1 dry matter</li> <li>Ncontent_yield_H4: herbage N content of harvest 4 in g.kg-1 dry matter</li> <li>fresh_yield_H1: herbvage fresh yield of harvest 1 in Mg.ha-1</li> <li>ANR: apparent N recovery in kg N.kg N-1</li> <li>productive_grasses: cover percentage of L. perenne and P trivialis</li> <li>monocotyledons: cover percentage of monocotyledons</li> <li>dicotyledons: cover percentage of dicotyledons</li> <li>plant_species: number of plant species</li> <li>monocot_species: number of monocotyledon species</li> <li>dicot_species: number of dicotyledon species</li> <li>Lolium_perenne: plant cover %</li> <li>Poa_trivialis: plant cover %</li> <li>Phleum_pratense: plant cover %</li> <li>Elytrigia_repens: plant cover %</li> <li>Poa_annua: plant cover %</li> <li>Agrostis_stolonifera: plant cover %</li> <li>Holcus_lanatus: plant cover %</li> <li>Alopecurus_pratensis: plant cover %</li> <li>Alopecurus_geniculatus: plant cover %</li> <li>Trifolium_repens: plant cover %</li> <li>Taraxacum_officinale: plant cover %</li> <li>Ranunculus_arvensis: plant cover %</li> <li>Rumex_obtusifolius: plant cover %</li> <li>Rumex_crispus: plant cover %</li> <li>Ranunculus_acris: plant cover %</li> <li>Stellaria_media: plant cover %</li> <li>Cardamine_pratensis: plant cover %</li> <li>Bellis_perennis: plant cover %</li> <li>Rumex_acetosa: plant cover %</li> <li>Ranunculus_sceleratus: plant cover %</li> <li>Polygonum_aviculare: plant cover %</li> <li>Capsella_bursa-pastoris: plant cover %</li> <li>Glechoma_hederacea: plant cover %</li> <li>Geranium_molle: plant cover %</li> </ul> <p> </p> <p><em><strong>Fertilizer_input.csv</strong></em></p> <p><em>Content:</em></p> <p>Application quantities of fertilizers and ash, organic matter, C and mineral inputs, and fertilizer C:N ratio. Total N input is the sum of mineral N (Nmin) and organic N (Norg). Average values per hectare and per year over the years 2013−2015.</p> <p><em>Column names and units:</em></p> <ul> <li>Treatment: Treatment code (see text)</li> <li>Fertilizer_fresh: Applied fertilizer in Mg.ha<sup>-1</sup> per year (fresh weight)</li> <li>Fertilizer_DM: Applied fertilizer in Mg.ha<sup>-1</sup> per year (dry matter weight); for IF+SD this is the sum of 2.72 Mg sawdust + 0.45 Mg N fertilizer</li> <li>Ash: Mineral fraction in kg.ha<sup>-1</sup> per year</li> <li>OM: Organic matter in kg.ha<sup>-1</sup> per year</li> <li>C: Total C in kg.ha<sup>-1</sup> per year</li> <li>Nmin: Mineral N in kg.ha<sup>-1</sup> per year</li> <li>Norg: Organic N in kg.ha<sup>-1</sup> per year</li> <li>P2O5: kg.ha<sup>-1</sup> per year</li> <li>C_N_ratio: C:N ratio</li> </ul>
Drivers of spatial and temporal micro- and mesozooplankton dynamics in an estuary under strong anthropogenic influences (The Eastern Scheldt, Netherlands)
<p>Supplement to: Horn, H.G., van Rijswijk, P., Soetaert, K., van Oevelen, D. (2023): Drivers of spatial and temporal micro- and mesozooplankton dynamics in an estuary under strong anthropogenic influences (The Eastern Scheldt, Netherlands). J Sea Res. <a href="https://doi.org/10.1016/j.seares.2023.102357">https://doi.org/10.1016/j.seares.2023.102357</a></p> <p>This data set contains mesozooplankton and microzooplankton abundances, temperature, salinity, O2, DOC, Chl.a, SPM, and nutrient concentrations from eight stations in the Eastern Scheldt sampled in 2018. Phytoplankton growth and microzooplankton grazing rates from dilution experiments are also provided.</p>
Recording of a learner of the Dutch language reading a Dutch position paper about Open Science in the Netherlands
<p>This repo contains a recording I made of me reading the position paper "<a href="https://commons.wikimedia.org/wiki/File:283.002-Erkennen-en-Waarderen-Position-Paper_NL_web.pdf#%7B%7Bint%3Alicense-header%7D%7D">Ruimte voor ieders talent</a>" (<a href="https://commons.wikimedia.org/wiki/File:283.002-Erkennen-en-Waarderen-Position-Paper_EN_web.pdf#%7B%7Bint%3Afiledesc%7D%7D">Room for everyone's talent</a>) upon first contact, albeit after having read the accompanying post "<a href="https://www.scienceguide.nl/2019/11/erkennen-en-waarderen-moet/">Erkennen en waarderen in de wetenschap gaan drastisch veranderen</a>" by Sicco de Knecht, which provides a summary of the position paper and got me interested in knowing more. That post was not openly licensed, so my recording of reading it cannot be shared, but the position paper is available under the <a href="https://en.wikipedia.org/wiki/en:Creative_Commons">Creative Commons</a> <a href="https://creativecommons.org/licenses/by/3.0/nl/deed.en">Attribution 3.0 Netherlands</a> license, which thus also pre-determines the licensing of this recording of me reading it.</p> <p>The reason I am putting this out here is that I am interested in sharing knowledge in general, including the process of how it was acquired, which is usually not very visible, even if all the relevant teaching materials are available, which is getting more popular but still remains rare overall. For the same reasons, by the way, I am running a <a href="https://github.com/Daniel-Mietchen/learning2code">GitHub repo</a> about my progress in learning programming and querying languages.</p> <p>I have no training in Dutch but am a native speaker of German and fluent in English, through which I can understand Dutch texts fairly easily, and I have read a good number of them (including books but more frequently Wikipedia articles) silently over multiple years, which gave me some idea about how the language works.</p> <p>What is usually neglected when reading silently is pronunciation, e.g. while I passively know how numbers and letters are pronounced in Dutch, I normally read them in English or German when reading Dutch texts silently, since that is quicker and less effort for me, and most of the reading I do is to learn about content, not the respective languages.</p> <p>With the bit of extra time afforded by the present holiday period, I can give some more attention to the language part, and since the topic of the position paper (open science and how we need to adapt incentive structures in the research landscape around that) is one that I deeply care about, I could also imagine coming back to the text occasionally to track my progress over time.</p> <p>So if you are interested in the process of language acquisition or in second language acquisition (for me, though, Dutch is far from being second) and especially in open education or open science around these topics, I'd appreciate you getting in touch to learn from each other around how such materials can help me and others learn this language or anything else, and how the workflows around that could be streamlined.</p>
Novel Coronavirus (COVID-19) Cases in The Netherlands
<p>On 27 February 2020, the first case of COVID-19 disease was confirmed in The Netherlands by RIVM (National Institute for Public Health and the Environment). In the weeks after, thousands of people were diagnosed with the infectious disease. Data on COVID-19 case counts are important for research and applications on various topics like epidemiology and statistics.</p> <p>This dataset contains reported case counts derived from official sources like RIVM (National Institute for Public Health and the Environment), LCPS (National Coordination Center for Patient Distribution), and NICE (National Intensive Care Evaluation). Data from these sources are collected, standardized, and published in various formats on a daily basis.</p> <p>The README document in this repository provides an overview of the available datasets, their file location(s), and codebooks. Copies of the original data are stored in the folder named 'raw_data'. Scripts to process the raw data into standardized files can be found in the folder workflows.</p>
AI results complementing the Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union- The Netherlands
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2019, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
Results from national testing programs on the occurrence of chemical contaminants in food and feed - The Netherlands
<p>In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed. </p> <p>The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. </p> <p>Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below: </p> <ul> <li>Commission Regulation (EU) 625/2017, on the application of food and feed law</li> <li>Commission Delegated Regulation (EU) 2022/931</li> <li>Commission Implementing Regulation (EU) 2022/932</li> <li>Commission Regulation (EU) 2023/915, on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006</li> </ul> <p>These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2024, 2023, 2022, 2021, and 2020, split by sampling year (data element ‘sampY’). </p> <p>More details are available in last year's finalised call for data ‘<span><a href="https://www.efsa.europa.eu/en/call/annual-call-continuous-collection-chemical-contaminants-occurrence-data-food-and-feed">Annual call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA</a></span>’.</p> <p>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: </p> <p>OCC-CHEMMON2020 – Institute for Public Health and the Environment</p> <p>OCC-CHEMMON2021 – Institute for Public Health and the Environment</p> <p>OCC-CHEMMON2022 – Institute for Public Health and the Environment</p> <p>OCC-CHEMMON2023 – Institute for Public Health and the Environment</p> <p>OCC-CHEMMON2024 – Institute for Public Health and the Environment</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.